Showing posts with label Knowledge Management. Show all posts
Showing posts with label Knowledge Management. Show all posts

13 September 2020

🎓Knowledge Management: Definitions II (What's in a Name)

Knowledge Management

Browsing through the various books on databases and programming appeared over the past 20-30 years, it’s probably hard not to notice the differences between the definitions given even for straightforward and basic concepts like the ones of view, stored procedure or function. Quite often the definitions lack precision and rigor, are circular and barely differentiate the defined term (aka concept) from other terms. In addition, probably in the attempt of making the definitions concise, important definitory characteristics are omitted.

Unfortunately, the same can be said about other non-scientific books, where the lack of appropriate definitions make the understanding of the content and presented concepts more difficult. Even if the reader can arrive in time to an approximate understanding of what is meant, one might have the feeling that builds castles in the air as long there is no solid basis to build upon – and that should be the purpose of a definition – to offer the foundation on which the reader can build upon. Especially for the readers coming from the scientific areas this lack of appropriateness and moreover, the lack of definitions, feels maybe more important than for the professional who already mastered the respective areas.

In general, a definition of a term is a well-defined descriptive statement which serves to differentiate it from related concepts. A well-defined definition should be meaningful, explicit, concise, precise, non-circular, distinct, context-dependent, relevant, rigorous, and rooted in common sense. In addition, each definition needs to be consistent through all the content and when possible, consistent with the other definitions provided. Ideally the definitions should cover as much of possible from the needed foundation and provide a unitary consistent multilayered non-circular and hierarchical structure that facilitates the reading and understanding of the given material.

Thus, one can consider the following requirements for a definition:

Meaningful: the description should be worthwhile and convey the required meaning for understanding the concept.

Explicit: the description must state clearly and provide enough information/detail so it can leave no room for confusion or doubt.

Context-dependent: the description should provide upon case the context in which the term is defined.

Concise: the description should be as succinct as possible – obtaining the maximum of understanding from a minimum of words.

Precise: the description should be made using unambiguous words that provide the appropriate meaning individually and as a whole.

Intrinsic non-circularity: requires that the term defined should not be used as basis for definitions, leading thus to trivial definitions like “A is A”.

Distinct: the description should provide enough detail to differentiate the term from other similar others.

Relevant: the description should be closely connected or appropriate to what is being discussed or presented.

Rigorous: the descriptions should be the result of a thorough and careful thought process in which the multiple usages and forms are considered.  

Extrinsic non-circularity: requires that the definitions of two distinct terms should not be circular (e.g. term A’s definition is based on B, while B’s definition is based on A), situation usually met occasionally in dictionaries.

Rooted in common sense: the description should not deviate from the common-sense acceptance of the terms used, typically resulted from socially constructed or dictionary-based definitions.

Unitary consistent multilayered hierarchical structure: the definitions should be given in an evolutive structure that facilitates learning, typically in the order in which the concepts need to be introduced without requiring big jumps in understanding. Even if concepts have in general a networked structure, hierarchies can be determined, especially based on the way concepts use other concepts in their definitions. In addition, the definitions must be consistent – hold together – respectively be unitary – form a whole.

29 May 2015

🎓Knowledge Management: Keeping Current or the Quest to Lifelong Learning for IT Professionals

Introduction

    The pace with which technologies and the business changes becomes faster and faster. If 5-10 years back a vendor needed 3-5 years before coming with a new edition of a product, nowadays each 1-2 years a new edition is released. The release cycles become shorter and shorter, vendors having to keep up with the changing technological trends. Changing trends allow other vendors to enter the market with new products, increasing thus the competition and the need for responsiveness from other vendors. On one side the new tools/editions bring new functionality which mainly address technical and business requirements. On the other side existing tools functionality gets deprecated and superset by other. Knowledge doesn’t resume only to the use of tools, but also in the methodologies, procedures, best practices or processes used to make most of the respective products. Evermore, the value of some tools increases when mixed, flexible infrastructures relying on the right mix of tools working together.

    For an IT person keeping current with the advances in technologies is a major requirement. First of all because knowing modern technologies is a ticket for a good and/or better paid job. Secondly because many organizations try to incorporate in their IT infrastructure modern tools that would allow them increase the ROI and achieve further benefits. Thirdly because, as I’d like to believe, most of the IT professionals are eager to learn new things, keep up with the novelty. Being an adept of the continuous learning philosophy is also a way to keep the brain challenged, other type of challenge than the one we meet in daily tasks.

Knowledge Sources

    Face-to-face or computer-based trainings (CBTs) are the old-fashioned ways of keeping up-to-date with the advances in technologies though paradoxically not all organizations afford to train their IT employees. Despite of affordable CBTs, face-to-face trainings are quite expensive for the average IT person, therefore the IT professional has to reorient himself to other sources of knowledge. Fortunately many important Vendors like Microsoft or IBM provide in one form or another through Knowledge Bases (KB), tutorials, forums, presentations and Blogs a wide range of resources that could be used for learning. Similar resources exist also from similar parties, directly or indirectly interested in growing the knowledge pool.

    Nowadays reading a book or following a course it isn’t anymore a requirement for learning a subject. Blogs, tutorials, articles and other types of similar material can help more. Through their subject-oriented focus, they can bring some clarity in a small unit of time. Often they come with references to further materials, bring fresh perspectives, and are months or even years ahead books or courses. Important professionals in the field can be followed on blogs, Twitter, LinkedIn, You Tube and other social media platforms. Seeing in what topics they are interested in, how they code, what they think, maybe how they think, some even share their expertize ad-hoc when asked, all of this can help an IT professional considerably if he knows how to take advantage of these modern facilities.

    MOOCs start to approach IT topics, and further topics that can become handy for an IT professional. Most of them are free or a small fee is required for some of them, especially if participants’ identity needs to be verified. Such courses are a valuable resource of information. The participant can see how such a course is structured, what topics are approached, and what’s the minimal knowledge base required; the material is almost the same as in a normal university course, and in the end it’s not the piece of paper with the testimonial that’s important, but the change in perspective we obtained by taking the course. In addition the MOOC participant can interact with people with similar hobbies, collaborate with them on projects, and why not, something useful can come out of it. Through MOOCs or direct Vendor initiatives, free or freeware versions of software is available. Sometimes the whole functionality is available for personal use. The professional is therefore no more dependent on the software he can use only at work. New possibilities open for the person who wants to learn.

Maximizing the Knowledge Value

    Despite the considerable numbers of knowledge resources, for an IT professional the most important part of his experience comes from hand-on experience acquired on the job. If the knowledge is not rooted in hand-on experience, his knowledge remains purely theoretical, with minimal value. Therefore in order to maximize the value of his learning, an IT professional has to attempt using his knowledge as much and soon as possible in praxis. One way to increase the value of experience is to be involved in projects dealing with new technologies or challenges that would allow a professional to further extend his knowledge base. Sometimes we can choose such projects or gain exposure to the technologies, though other times no such opportunities can be sized or identified.

    Probably an IT professional can use in his daily duties 10-30% of what he learned. This percentage can be however increased by involving himself in other types of personal or collective (open source or work) projects. This would allow exploring the subjects from other perspective. Considering that many projects involve overtime, many professionals have also a rich personal life, it looks difficult to do that, though not impossible.

    Even if not on a regular basis achievable, a professional can allocate 1-3 hours on a weekly basis from his working time for learning something new. It can be something that would help directly or indirectly his organization, though sometimes it pays off to learn technologies that have nothing to do with the actual job. Somebody may argue that the respective hours are not “billable”, are a waste of time and other resources, that the technologies are not available, that there’s lot of due tasks, etc. With a little benevolence and with the right argumentation also such criticism can be silenced. The arguments can be for example based on the fact that a skilled professional can be with time more productive, a small investment in knowledge can have later a bigger benefit for both parties – employee and employer. An older study was showing that when IT professionals was given some freedom to approach personal projects at work, and use some time for their own benefit, the value they bring for an organization increased. There are companies like Google who made from this type of work a philosophy.

    A professional can also allocate 1-3 hours from his free time while commuting or other similar activities. Reading something before going to bed or as relaxation after work can prove to be a good shut-down for the brain from the daily problems. Where there’s interest in learning something new a person will find the time, no matter how busy his schedule is. It’s important however to do that on a regular basis, and with time the hours and knowledge accumulate.

    It’s also important to have a focused effort that will bring some kind of benefit. Learning just for the sake of learning brings little value on investment for a person if it’s not adequately focused. For sure it’s interesting and fun to browse through different topics, it’s even recommended to do so occasionally, though on the long run if a person wants to increase the value of his knowledge, he needs somehow to focus the knowledge within a given direction and apply that knowledge.

    Direction we obtain by choosing a career or learning path, and focusing on the direct or indirect related topics that belong to that path. Focusing on the subjects related to a career path allows us to build our knowledge further on existing knowledge, understanding a topic fully. On the other side focusing on other areas of applicability not directly linked with our professional work can broaden our perspective by looking at one topic from another’s topic perspective. This can be achieved for example by joining the knowledge base of a hobby we have with the one of our professional work. In certain configurations new opportunities for joint growth can be identified.

    The value of knowledge increases primarily when it’s used in day-to-day scenarios (a form of learning by doing). It would be useful for example for a professional to start a project that can bring some kind of benefit. It can be something simple like building a web page or a full website, an application that processes data, a solution based on a mix of technologies, etc. Such a project would allow simulating to some degree day-to-day situations, when the professional is forced to used and question some aspects, to deal with some situations that can’t be found in textbook or other learning material. If such a project can bring a material benefit, the value of knowledge increases even more.

    Another way to integrate the accumulated knowledge is through blogging and problem-solving. Topic or problem-oriented blogging can allow externalizing a person’s knowledge (aka tacit knowledge), putting knowledge in new contexts into a small focused unit of work, doing some research and see how other think about the same topic/problem, getting feedback, correcting or improving some aspects. It’s also a way of documenting the various problems identified while learning or performing a task. Blogging helps a person to improve his writing communication skills, his vocabulary and with a little more effort can be also a visit card for his professional experience.

    Trying to apply new knowledge in hand-on trainings, tutorials or by writing a few lines of code to test functionality and its applicability, same as structuring new learned material into notes in the form of text or knowledge maps (e.g. concept maps, mind maps, causal maps, diagrams, etc.) allow learners to actively learn the new concepts, increasing overall material’s retention. Even if notes and knowledge maps don’t apply the learned material directly, they offer a new way of structuring the content and resources for further enrichment and review. Applied individually, but especially when combined, the different types of active learning help as well maximize the value of knowledge with a minimum of effort.

Conclusion

    The bottom line – given the fast pace with which new technologies enter the market and the business environment evolves, an IT professional has to keep himself up-to-date with nowadays technologies. He has now more means than ever to do that – affordable computer-based training, tutorials, blogs, articles, videos, forums, studies, MOOC and other type of learning material allow IT professionals to approach a wide range of topics. Through active, focused, sustainable and hand-on learning we can maximize the value of knowledge, and in the end depends of each of us how we use the available resources to make most of our learning experience.

24 December 2013

🎓Knowledge Management: Knowledge (Just the Quotes)

"There are two modes of acquiring knowledge, namely, by reasoning and experience. Reasoning draws a conclusion and makes us grant the conclusion, but does not make the conclusion certain, nor does it remove doubt so that the mind may rest on the intuition of truth unless the mind discovers it by the path of experience." (Roger Bacon, "Opus Majus", 1267)

"Knowledge being to be had only of visible and certain truth, error is not a fault of our knowledge, but a mistake of our judgment, giving assent to that which is not true." (John Locke, "An Essay Concerning Human Understanding", 1689)

"[…] the highest probability amounts not to certainty, without which there can be no true knowledge." (John Locke, "An Essay Concerning Human Understanding", 1689)

"It is your opinion, the ideas we perceive by our senses are not real things, but images, or copies of them. Our knowledge therefore is no farther real, than as our ideas are the true representations of those originals. But as these supposed originals are in themselves unknown, it is impossible to know how far our ideas resemble them; or whether they resemble them at all. We cannot therefore be sure we have any real knowledge." (George Berkeley, "Three Dialogues", 1713)

"Our knowledge springs from two fundamental sources of the mind; the first is the capacity of receiving representations (receptivity for impressions), the second is the power of knowing an object through these representations (spontaneity [in the production] of concepts)." (Immanuel Kant, "Critique of Pure Reason", 1781)

"Knowledge is only real and can only be set forth fully in the form of science, in the form of system." (G W Friedrich Hegel, "The Phenomenology of Mind", 1807)

"One may even say, strictly speaking, that almost all our knowledge is only probable; and in the small number of things that we are able to know with certainty, in the mathematical sciences themselves, the principal means of arriving at the truth - induction and analogy - are based on probabilities, so that the whole system of human knowledge is tied up with the theory set out in this essay." (Pierre-Simon Laplace, "Philosophical Essay on Probabilities", 1814) 

"We [...] are profiting not only by the knowledge, but also by the ignorance, not only by the discoveries, but also by the errors of our forefathers; for the march of science, like that of time, has been progressing in the darkness, no less than in the light." (Charles C Colton, "Lacon", 1820)

"Our knowledge of circumstances has increased, but our uncertainty, instead of having diminished, has only increased. The reason of this is, that we do not gain all our experience at once, but by degrees; so our determinations continue to be assailed incessantly by fresh experience; and the mind, if we may use the expression, must always be under arms." (Carl von Clausewitz, "On War", 1832)

"All knowledge is profitable; profitable in its ennobling effect on the character, in the pleasure it imparts in its acquisition, as well as in the power it gives over the operations of mind and of matter. All knowledge is useful; every part of this complex system of nature is connected with every other. Nothing is isolated. The discovery of to-day, which appears unconnected with any useful process, may, in the course of a few years, become the fruitful source of a thousand inventions." (Joseph Henry, "Report of the Secretary" [Sixth Annual Report of the Board of Regents of the Smithsonian Institution for 1851], 1852)

"Isolated facts and experiments have in themselves no value, however great their number may be. They only become valuable in a theoretical or practical point of view when they make us acquainted with the law of a series of uniformly recurring phenomena, or, it may be, only give a negative result showing an incompleteness in our knowledge of such a law, till then held to be perfect." (Hermann von Helmholtz, "The Aim and Progress of Physical Science", 1869)

"Simplification of modes of proof is not merely an indication of advance in our knowledge of a subject, but is also the surest guarantee of readiness for farther progress." (William T Kelvin, "Elements of Natural Philosophy", 1873)

"The whole value of science consists in the power which it confers upon us of applying to one object the knowledge acquired from like objects; and it is only so far, therefore, as we can discover and register resemblances that we can turn our observations to account." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"[…] when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science." (William T Kelvin, "Electrical Units of Measurement", 1883)

"The smallest group of facts, if properly classified and logically dealt with, will form a stone which has its proper place in the great building of knowledge, wholly independent of the individual workman who has shaped it." (Karl Pearson, "The Grammar of Science", 1892)

"Without a theory all our knowledge of nature would be reduced to a mere inventory of the results of observation. Every scientific theory must be regarded as an effort of the human mind to grasp the truth, and as long as it is consistent with the facts, it forms a chain by which they are linked together and woven into harmony." (Thomas Preston, "The Theory of Heat", 1894)

"Knowledge is the distilled essence of our intuitions, corroborated by experience." (Elbert Hubbard, "A Thousand & One Epigrams, 1911)

"It is experience which has given us our first real knowledge of Nature and her laws. It is experience, in the shape of observation and experiment, which has given us the raw material out of which hypothesis and inference have slowly elaborated that richer conception of the material world which constitutes perhaps the chief, and certainly the most characteristic, glory of the modern mind." (Arthur J Balfour, "The Foundations of Belief", 1912)

"We have discovered that it is actually an aid in the search for knowledge to understand the nature of the knowledge we seek." (Arthur S Eddington, "The Philosophy of Physical Science", 1938)

"Science usually advances by a succession of small steps, through a fog in which even the most keen-sighted explorer can seldom see more than a few paces ahead. Occasionally the fog lifts, an eminence is gained, and a wider stretch of territory can be surveyed - sometimes with startling results. A whole science may then seem to undergo a kaleidoscopic ‘rearrangement’, fragments of knowledge being found to fit together in a hitherto unsuspected manner. Sometimes the shock of readjustment may spread to other sciences; sometimes it may divert the whole current of human thought." (James H Jeans, "Physics and Philosophy" 3rd Ed., 1943)

"Every bit of knowledge we gain and every conclusion we draw about the universe or about any part or feature of it depends finally upon some observation or measurement. Mankind has had again and again the humiliating experience of trusting to intuitive, apparently logical conclusions without observations, and has seen Nature sail by in her radiant chariot of gold in an entirely different direction." (Oliver J Lee, "Measuring Our Universe: From the Inner Atom to Outer Space", 1950)

"The essence of knowledge is generalization. That fire can be produced by rubbing wood in a certain way is a knowledge derived by generalization from individual experiences; the statement means that rubbing wood in this way will always produce fire. The art of discovery is therefore the art of correct generalization." (Hans Reichenbach, "The Rise of Scientific Philosophy", 1951)

"Knowledge rests on knowledge; what is new is meaningful because it departs slightly from what was known before; this is a world of frontiers, where even the liveliest of actors or observers will be absent most of the time from most of them." (J Robert Oppenheimer, "Science and the Common Understanding", 1954)

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory - The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"Incomplete knowledge must be considered as perfectly normal in probability theory; we might even say that, if we knew all the circumstances of a phenomenon, there would be no place for probability, and we would know the outcome with certainty." (Félix E Borel, Probability and Certainty", 1963)

"Knowing reality means constructing systems of transformations that correspond, more or less adequately, to reality. They are more or less isomorphic to transformations of reality. The transformational structures of which knowledge consists are not copies of the transformations in reality; they are simply possible isomorphic models among which experience can enable us to choose. Knowledge, then, is a system of transformations that become progressively adequate." (Jean Piaget, "Genetic Epistemology", 1968)

"Scientific knowledge is not created solely by the piecemeal mining of discrete facts by uniformly accurate and reliable individual scientific investigations. The process of criticism and evaluation, of analysis and synthesis, are essential to the whole system. It is impossible for each one of us to be continually aware of all that is going on around us, so that we can immediately decide the significance of every new paper that is published. The job of making such judgments must therefore be delegated to the best and wisest among us, who speak, not with their own personal voices, but on behalf of the whole community of Science. […] It is impossible for the consensus - public knowledge - to be voiced at all, unless it is channeled through the minds of selected persons, and restated in their words for all to hear." (John M Ziman, "Public Knowledge: An Essay Concerning the Social Dimension of Science", 1968)

"Models constitute a framework or a skeleton and the flesh and blood will have to be added by a lot of common sense and knowledge of details."(Jan Tinbergen, "The Use of Models: Experience," 1969)

"Human knowledge is personal and responsible, an unending adventure at the edge of uncertainty." (Jacob Bronowski, "The Ascent of Man", 1973)

"Knowledge is not a series of self-consistent theories that converges toward an ideal view; it is rather an ever increasing ocean of mutually incompatible (and perhaps even incommensurable) alternatives, each single theory, each fairy tale, each myth that is part of the collection forcing the others into greater articulation and all of them contributing, via this process of competition, to the development of our consciousness." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"Knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"We admit knowledge whenever we observe an effective (or adequate) behavior in a given context, i.e., in a realm or domain which we define by a question (explicit or implicit)." (Humberto Maturana & Francisco J Varela, "The Tree of Knowledge", 1987)

"We live on an island surrounded by a sea of ignorance. As our island of knowledge grows, so does the shore of our ignorance." (John A Wheeler, Scientific American Vol. 267, 1992)

"Knowledge is theory. We should be thankful if action of management is based on theory. Knowledge has temporal spread. Information is not knowledge. The world is drowning in information but is slow in acquisition of knowledge. There is no substitute for knowledge." (William E Deming, "The New Economics for Industry, Government, Education", 1993) 

"Discourses are ways of referring to or constructing knowledge about a particular topic of practice: a cluster (or formation) of ideas, images and practices, which provide ways of talking about, forms of knowledge and conduct associated with, a particular topic, social activity or institutional site in society. These discursive formations, as they are known, define what is and is not appropriate in our formulation of, and our practices in relation to, a particular subject or site of social activity." (Stuart Hall, "Representation: Cultural Representations and Signifying Practices", 1997)

"An individual understands a concept, skill, theory, or domain of knowledge to the extent that he or she can apply it appropriately in a new situation." (Howard Gardner, "The Disciplined Mind", 1999)

"Knowledge is factual when evidence supports it and we have great confidence in its accuracy. What we call 'hard fact' is information supported by  strong, convincing evidence; this means evidence that, so far as we know, we cannot deny, however we examine or test it. Facts always can be questioned, but they hold up under questioning. How did people come by this information? How did they interpret it? Are other interpretations possible? The more satisfactory the answers to such questions, the 'harder' the facts." (Joel Best, Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists, 2001)

"Knowledge is in some ways the most important (though intangible) capital of a software engineering organization, and sharing of that knowledge is crucial for making an organization resilient and redundant in the face of change. A culture that promotes open and honest knowledge sharing distributes that knowledge efficiently across the organization and allows that organization to scale over time. In most cases, investments into easier knowledge sharing reap manyfold dividends over the life of a company." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

More quotes on "Knowledge" at the-web-of-knowledge.blogspot.com.

16 December 2013

🎓Knowledge Management: Data, Information, Knowledge, Wisdom (Just the Quotes)

 "Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information upon it." (Samuel Johnson, 1775)

"It is almost as difficult to make a man unlearn his errors as his knowledge. Mal-information is more hopeless than non-information; for error is always more busy than ignorance. Ignorance is a blank sheet, on which we may write; but error is a scribbled one, on which we must first erase. Ignorance is contented to stand still with her back to the truth; but error is more presumptuous, and proceeds in the same direction. Ignorance has no light, but error follows a false one. The consequence is, that error, when she retraces her footsteps, has further to go, before she can arrive at the truth, than ignorance." (Charles C Colton, “Lacon”, 1820)

"In every branch of knowledge the progress is proportional to the amount of facts on which to build, and therefore to the facility of obtaining data." (James C Maxwell, [Letter to Lewis Campbell] 1851) 

"[The information of a message can] be defined as the 'minimum number of binary decisions which enable the receiver to construct the message, on the basis of the data already available to him.' These data comprise both the convention regarding the symbols and the language used, and the knowledge available at the moment when the message started." (Dennis Gabor, "Optical transmission" in Information Theory : Papers Read at a Symposium on Information Theory, 1952)

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory - The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"The idea of knowledge as an improbable structure is still a good place to start. Knowledge, however, has a dimension which goes beyond that of mere information or improbability. This is a dimension of significance which is very hard to reduce to quantitative form. Two knowledge structures might be equally improbable but one might be much more significant than the other." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"In perception itself, two distinct processes can be discerned. One is the gathering of the primary, sensory data or simple sensing of such things as light, moisture or pressure, and the other is the structuring of such data into information." (Edward Ihnatowicz, "The Relevance of Manipulation to the Process of Perception", 1977) 

"Data, seeming facts, apparent asso­ciations-these are not certain knowledge of something. They may be puzzles that can one day be explained; they may be trivia that need not be explained at all. (Kenneth Waltz, "Theory of International Politics", 1979)

"Knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"Information is data that has been given meaning by way of relational connection. This 'meaning' can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it." (Russell L Ackoff, "Towards a Systems Theory of Organization", 1985)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987) 

"Probabilities are summaries of knowledge that is left behind when information is transferred to a higher level of abstraction." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Network of Plausible, Inference", 1988)

"Information engineering has been defined with the reference to automated techniques as follows: An interlocking set of automated techniques in which enterprise models, data models and process models are built up in a comprehensive knowledge-base and are used to create and maintain data-processing systems." (James Martin, "Information Engineering, 1989)

"Knowledge is theory. We should be thankful if action of management is based on theory. Knowledge has temporal spread. Information is not knowledge. The world is drowning in information but is slow in acquisition of knowledge. There is no substitute for knowledge." (William E Deming, "The New Economics for Industry, Government, Education", 1993)

"Knowledge, truth, and information flow in networks and swarm systems. I have always been interested in the texture of scientific knowledge because it appears to be lumpy and uneven. Much of what we collectively know derives from a few small areas, yet between them lie vast deserts of ignorance. I can interpret that observation now as the effect of positive feedback and attractors. A little bit of knowledge illuminates much around it, and that new illumination feeds on itself, so one corner explodes. The reverse also holds true: ignorance breeds ignorance. Areas where nothing is known, everyone avoids, so nothing is discovered. The result is an uneven landscape of empty know-nothing interrupted by hills of self-organized knowledge." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995) 

"Now that knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information." (Peter Drucker, "Managing in a Time of Great Change", 1995)

"Data is discrimination between physical states of things (black, white, etc.) that may convey or not convey information to an agent. Whether it does so or not depends on the agent's prior stock of knowledge." (Max Boisot, "Knowledge Assets", 1998)

"The unit of coding is the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon." (Richard Boyatzis, "Transforming qualitative information", 1998)

"While hard data may inform the intellect, it is largely soft data that generates wisdom." (Henry Mintzberg, "Strategy Safari: A Guided Tour Through The Wilds of Strategic Management", 1998)

"Information is just bits of data. Knowledge is putting them together. Wisdom is transcending them." (Ram Dass, "One-Liners: A Mini-Manual for a Spiritual Life (ed. Harmony", 2007)

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon."(Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)

"We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out picture is clear and accurate. But is it?" (Leonard Mlodinow, "The Drunkard's Walk: How Randomness Rules Our Lives", 2009) 

"We reach wisdom when we achieve a deep understanding of acquired knowledge, when we not only 'get it', but when new information blends with prior experience so completely that it makes us better at knowing what to do in other situations, even if they are only loosely related to the information from which our original knowledge came. Just as not all the information we absorb leads to knowledge, not all of the knowledge we acquire leads to wisdom." (Alberto Cairo, "The Functional Art", 2011)

"Any knowledge incapable of being revised with advances in data and human thinking does not deserve the name of knowledge." (Jerry Coyne, "Faith Versus Fact", 2015)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context."  (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"Real wisdom is not the knowledge of everything, but the knowledge of which things in life are necessary, which are less necessary, and which are completely unnecessary to know." (Lev N Tolstoy)

"The Information Age offers much to mankind, and I would like to think that we will rise to the challenges it presents. But it is vital to remember that information - in the sense of raw data - is not knowledge, that knowledge is not wisdom, and that wisdom is not foresight. But information is the first essential step to all of these." (Arthur C Clark)

🎓Knowledge Management: Domains (Just the Quotes)

"Great discoveries which give a new direction to currents of thoughts and research are not, as a rule, gained by the accumulation of vast quantities of figures and statistics. These are apt to stifle and asphyxiate and they usually follow rather than precede discovery. The great discoveries are due to the eruption of genius into a closely related field, and the transfer of the precious knowledge there found to his own domain." (Theobald Smith, Boston Medical and Surgical Journal Volume 172, 1915)

"Learning is any change in a system that produces a more or less permanent change in its capacity for adapting to its environment. Understanding systems, especially systems capable of understanding problems in new task domains, are learning systems." (Herbert A Simon, "The Sciences of the Artificial", 1968)

"A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system." (Humberto R Maturana, "Biology of Cognition", 1970)

"No theory ever agrees with all the facts in its domain, yet it is not always the theory that is to blame. Facts are constituted by older ideologies, and a clash between facts and theories may be proof of progress. It is also a first step in our attempt to find the principles implicit in familiar observational notions." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure. […]  a person might use his cognitive map to derive explanations of the past, make predictions for the future, and choose policies in the present." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"The thinking person goes over the same ground many times. He looks at it from varying points of view - his own, his arch-enemy’s, others’. He diagrams it, verbalizes it, formulates equations, constructs visual images of the whole problem, or of troublesome parts, or of what is clearly known. But he does not keep a detailed record of all this mental work, indeed could not. […] Deep understanding of a domain of knowledge requires knowing it in various ways. This multiplicity of perspectives grows slowly through hard work and sets the state for the re-cognition we experience as a new insight." (Howard E Gruber, "Darwin on Man", 1981)

"Metaphor [is] a pervasive mode of understanding by which we project patterns from one domain of experience in order to structure another domain of a different kind. So conceived metaphor is not merely a linguistic mode of expression; rather, it is one of the chief cognitive structures by which we are able to have coherent, ordered experiences that we can reason about and make sense of. Through metaphor, we make use of patterns that obtain in our physical experience to organise our more abstract understanding." (Mark Johnson, "The Body in the Mind", 1987)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"[…] a mental model is a mapping from a domain into a mental representation which contains the main characteristics of the domain; a model can be ‘run’ to generate explanations and expectations with respect to potential states. Mental models have been proposed in particular as the kind of knowledge structures that people use to understand a specific domain […]" (Helmut Jungermann, Holger Schütz & Manfred Thuering, "Mental models in risk assessment: Informing people about drugs", Risk Analysis 8 (1), 1988)

"Algorithmic complexity theory and nonlinear dynamics together establish the fact that determinism reigns only over a quite finite domain; outside this small haven of order lies a largely uncharted, vast wasteland of chaos." (Joseph Ford, "Progress in Chaotic Dynamics: Essays in Honor of Joseph Ford's 60th Birthday", 1988)

"When partitioning a domain, we divide the information model so that the clusters remain intact. [...] Each section of the information model then becomes a separate subsystem. Note that when the information model is partitioned into subsystems, each object is assigned to exactly one subsystem."  (Stephen J Mellor, "Object-Oriented Systems Analysis: Modeling the World In Data", 1988) 

"While a small domain (consisting of fifty or fewer objects) can generally be analyzed as a unit, large domains must be partitioned to make the analysis a manageable task. To make such a partitioning, we take advantage of the fact that objects on an information model tend to fall into clusters: groups of objects that are interconnected with one another by many relationships. By contrast, relatively few relationships connect objects in different clusters." (Stephen J Mellor, "Object-Oriented Systems Analysis: Modeling the World In Data", 1988) 

"A law explains a set of observations; a theory explains a set of laws. […] a law applies to observed phenomena in one domain (e.g., planetary bodies and their movements), while a theory is intended to unify phenomena in many domains. […] Unlike laws, theories often postulate unobservable objects as part of their explanatory mechanism." (John L Casti, "Searching for Certainty: How Scientists Predict the Future", 1990)

"Generally speaking, problem knowledge for solving a given problem may consist of heuristic rules or formulas that comprise the explicit knowledge, and past-experience data that comprise the implicit, hidden knowledge. Knowledge represents links between the domain space and the solution space, the space of the independent variables and the space of the dependent variables." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Inference is the process of matching current facts from the domain space to the existing knowledge and inferring new facts. An inference process is a chain of matchings. The intermediate results obtained during the inference process are matched against the existing knowledge. The length of the chain is different. It depends on the knowledge base and on the inference method applied." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"An individual understands a concept, skill, theory, or domain of knowledge to the extent that he or she can apply it appropriately in a new situation." (Howard Gardner, "The Disciplined Mind", 1999)

"Knowledge maps are node-link representations in which ideas are located in nodes and connected to other related ideas through a series of labeled links. They differ from other similar representations such as mind maps, concept maps, and graphic organizers in the deliberate use of a common set of labeled links that connect ideas. Some links are domain specific (e.g., function is very useful for some topic domains...) whereas other links (e.g., part) are more broadly used. Links have arrowheads to indicate the direction of the relationship between ideas." (Angela M. O’Donnell et al, "Knowledge Maps as Scaffolds for Cognitive Processing", Educational Psychology Review Vol. 14 (1), 2002) 

"We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior." (Stephen J Mellor, "Executable UML: A Foundation for Model-Driven Architecture", 2002)

"A domain model is not a particular diagram; it is the idea that the diagram is intended to convey. It is not just the knowledge in a domain expert’s head; it is a rigorously organized and selective abstraction of that knowledge." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Domain experts are usually not aware of how complex their mental processes are as, in the course of their work, they navigate all these rules, reconcile contradictions, and fill in gaps with common sense. Software can’t do this. It is through knowledge crunching in close collaboration with software experts that the rules are clarified, fleshed out, reconciled, or placed out of scope." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Effective domain modelers are knowledge crunchers. They take a torrent of information and probe for the relevant trickle. They try one organizing idea after another, searching for the simple view that makes sense of the mass. Many models are tried and rejected or transformed. Success comes in an emerging set of abstract concepts that makes sense of all the detail. This distillation is a rigorous expression of the particular knowledge that has been found most relevant." (Eric Evans, "Domain-Driven Design: Tackling complexity in the heart of software", 2003)

"Perception and memory are imprecise filters of information, and the way in which information is presented, that is, the frame, influences how it is received. Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions. Unfortunately, sometimes these heuristics lead to bias, especially when used outside their natural domains." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)

"This is always the case in analogical reasoning: Relations between two dissimilar domains never map completely to one another. In fact, it is often the salient similarities between the base and target domains that provoke thought and increase the usefulness of an analogy as a problem-solving tool." (Robbie T Nakatsu, "Diagrammatic Reasoning in AI", 2010)

"Conceptual models are best thought of as design-tools - a way for designers to straighten out and simplify the design and match it to the users’ task-domain, thereby making it clearer to users how they should think about the application. The designers’ responsibility is to devise a conceptual model that seems natural to users based on the users’ familiarity with the task domain. If designers do their job well, the conceptual model will be the basis for users’ mental models of the application." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"A model or conceptual model is a schematic or representation that describes how something works. We create and adapt models all the time without realizing it. Over time, as you gain more information about a problem domain, your model will improve to better match reality." (James Padolsey, "Clean Code in JavaScript", 2020)

"Knowledge graphs use an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle gives us an additional layer of organizing data (metadata) that adds connected context to support reasoning and knowledge discovery. […] Importantly, some processing can be done without knowledge of the domain, just by leveraging the features of the property graph model (the organizing principle)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

09 July 2013

🎓Knowledge Management: Mental Model (Definitions)

"A mental model is a cognitive construct that describes a person's understanding of a particular content domain in the world." (John Sown, "Conceptual Structures: Information Processing in Mind and Machine", 1984)

"A mental model is a data structure, in a computational system, that represents a part of the real world or of a fictitious world." (Alan Granham, "Mental Models as Representations of Discourse and Text", 1987)

"[…] a mental model is a mapping from a domain into a mental representation which contains the main characteristics of the domain; a model can be ‘run’ to generate explanations and expectations with respect to potential states. Mental models have been proposed in particular as the kind of knowledge structures that people use to understand a specific domain […]" (Helmut Jungermann, Holger Schütz & Manfred Thuering, "Mental models in risk assessment: Informing people about drugs", Risk Analysis 8 (1), 1988)

 "A mental model is a knowledge structure that incorporates both declarative knowledge (e.g., device models) and procedural knowledge (e.g., procedures for determining distributions of voltages within a circuit), and a control structure that determines how the procedural and declarative knowledge are used in solving problems (e.g., mentally simulating the behavior of a circuit)." (Barbara Y White & John R Frederiksen, "Causal Model Progressions as a Foundation for Intelligent Learning Environments", Artificial Intelligence 42, 1990)

"’Mental models’ are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action. [...] Mental models are deeply held internal images of how the world works, images that limit us to familiar ways of thinking and acting." (Peter Senge, "The Fifth Discipline”, 1990)

"[A mental model] is a relatively enduring and accessible, but limited, internal conceptual representation of an external system (historical, existing, or projected) [italics in original] whose structure is analogous to the perceived structure of that system." (James K Doyle & David N Ford, "Mental models concepts revisited: Some clarifications and a reply to Lane", System Dynamics Review 15 (4), 1999)

"In broad terms, a mental model is to be understood as a dynamic symbolic representation of external objects or events on the part of some natural or artificial cognitive system. Mental models are thought to have certain properties which make them stand out against other forms of symbolic representations." (Gert Rickheit & Lorenz Sichelschmidt, "Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)

"A mental model is conceived […] as a knowledge structure possessing slots that can be filled not only with empirically gained information but also with ‘default assumptions’ resulting from prior experience. These default assumptions can be substituted by updated information so that inferences based on the model can be corrected without abandoning the model as a whole. Information is assimilated to the slots of a mental model in the form of ‘frames’ which are understood here as ‘chunks’ of knowledge with a well-defined meaning anchored in a given body of shared knowledge." (Jürgen Renn, “Before the Riemann Tensor: The Emergence of Einstein’s Double Strategy", 2005)

"A mental model is a mental representation that captures what is common to all the different ways in which the premises can be interpreted. It represents in 'small scale' how 'reality' could be - according to what is stated in the premises of a reasoning problem. Mental models, though, must not be confused with images." (Carsten Held et al, "Mental Models and the Mind", 2006)

"’Mental models’ are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action." (Jossey-Bass Publishers, "The Jossey-Bass Reader on Educational Leadership”, 2nd Ed. 2007)

"A mental model is an internal representation with analogical relations to its referential object, so that local and temporal aspects of the object are preserved." (Gert Rickheit et al, "The concept of communicative competence" [in "Handbook of Communication Competence"], 2008)

"Internal representations constructed on the spot when required by demands of an external task or by a self-generated stimulus. It enables activation of relevant schemata, and allows new knowledge to be integrated. It specifies causal actions among concepts that take place within it, and it can be interacted with in the mind." (Daniel Churchill, "Mental Models" [in "Encyclopedia of Information Technology Curriculum Integration"] , 2008)

"Mental models are representations of reality built in people’s minds. These models are based on arrangements of assumptions, judgments, and values. A main weakness of mental models is that people’s assumptions and judgments change over time and are applied in inconsistent ways when building explanations of the world." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"A mental model is the collection of concepts and relationships about the image of real world things we carry in our heads" (Hassan Qudrat-Ullah, "System Dynamics Based Technology for Decision Support", 2009)

"A mental recreation of the states of the world reproduced cognitively in order to offer itself as a basis for reasoning." (Eshaa M Alkhalifa, "Open Student Models", 2009)

[Shared Mental Model:] "A mental model that is shared among team members, and may include: 1) task-specific knowledge, 2) task-related knowledge, 3) knowledge of teammates and 4) attitudes/beliefs." (Rosemarie Reynolds et al, "Measuring Shared Mental Models in Unmanned Aircraft Systems", 2015) 

"A network of knowledge content, as well as the relationships among the content."(Rosemarie Reynolds et al, "Measuring Shared Mental Models in Unmanned Aircraft Systems", 2015)

"A mental model (aka mental representation/image/picture) is a mental structure that attempts to model (depict, imagine) how real or imaginary things look like, work or fit together." (The Web of Knowledge) [source]

Resources:
Quotes on "Mental Models" at the-web-of-knowledge.blogspot.com.

07 July 2013

🎓Knowledge Management: Conceptual Model (Definitions)

"A conceptual model is a qualitative description of the system and includes the processes taking place in the system, the parameters chosen to describe the processes, and the spatial and temporal scales of the processes." (A Avogadro & R C Ragaini, "Technologies for Environmental Cleanup", 1993)

"A conceptual model is a model of the projected system that is independent of implementation details." (Michael Worboys, "GIS: A Computing Perspective", 1995)

"A conceptual model is what in the model theory is called a set of formulas making statements about the world." (Dickson Lukose [Eds.], "Conceptual Structures: Fulfilling Peirce's Dream" Vol 5, 1997)

"A conceptual model is a representation of the system expertise using this formalism. An internal model is derived from the conceptual model and from a specification of the system transactions and the performance constraints." (Zbigniew W. Ras & Andrzej Skowron [Eds.], Foundations of Intelligent Systems: 10th International Symposium Vol 10, 1997)

"A conceptual model is one which reflects reality by placing words which are concepts into the model in the same way that the model aeroplane builder puts wings, a fuselage, and a cockpit together." (Lynn Basford & ‎Oliver Slevin, "Theory and Practice of Nursing: An Integrated Approach to Caring Practice", 2003) 

"A conceptual model is simply a framework or schematic to understand the interaction of workforce education and development systems with other variables in a society." (Jay W Rojewski, "International Perspectives on Workforce Education and Development", 2004) 

"A conceptual model is a mental image of a system, its components, its interactions. It lays the foundation for more elaborate models, such as physical or numerical models. A conceptual model provides a framework in which to think about the workings of a system or about problem solving in general. An ensuing operational model can be no better than its underlying conceptualization." (Henry N Pollack, "Uncertain Science … Uncertain World", 2005)

"A particular kind of learning object design to be supplied to learners to support their mental modeling." (Daniel Churchill, "Mental Models" [in "Encyclopedia of Information Technology Curriculum Integration"], 2008)

"The concepts and constructs about real work things we have in our heads are called mental model." (Hassan Qudrat-Ullah, "System Dynamics Based Learning Environments" [in "Encyclopedia of Information Technology Curriculum Integration"], 2008)

"Representations of real or imaginary structure in the human mind enabling orientation as well as goal orientated actions and movements" (Ralf Wagner, "Customizing Multimedia with Multi-Trees" [in "Encyclopedia of Multimedia Technology and Networking" 2nd Ed.], 2009)

"A conceptual model is a qualitative description of 'some aspect of the behaviour of a natural system'. This description is usually verbal, but may also be accompanied by figures and graphs." (Howard S. Wheater et al., "Groundwater Modelling in Arid and Semi-Arid Areas, 2010) 

"[…] a conceptual model is a diagram connecting variables and constructs based on theory and logic that displays the hypotheses to be tested." (Mary Wolfinbarger Celsi et al, "Essentials of Business Research Methods", 2011) 

"A conceptual model of an interactive application is, in summary: the structure of the application - the objects and their operations, attributes, and relation-ships; an idealized view of the how the application works – the model designers hope users will internalize; the mechanism by which users accomplish the tasks the application is intended to support." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"Simply put, a conceptual model is a simplified representation of reality, devised for a certain purpose and seen from a certain point of view."(David W Emble & Bernhard Thalheim, "Handbook of Conceptual Modeling", 2012) 

"Briefly, a conceptual model is the configuration of conceptual elements and the navigation between them. As such, a conceptual model is the foundation of the user interface of any interactive system." (Avi Parush, "Conceptual Design for Interactive Systems", 2015)

"A conceptual model is a framework that is initially used in research to outline the possible courses of action or to present an idea or thought. When a conceptual model is developed in a logical manner, it will provide a rigor to the research process." (N Elangovan & R Rajendran, "Conceptual Model: A Framework for Institutionalizing the Vigor in Business Research", 2015) 

"A model or conceptual model is a schematic or representation that describes how something works. We create and adapt models all the time without realizing it. Over time, as you gain more information about a problem domain, your model will improve to better match reality." (James Padolsey, "Clean Code in JavaScript", 2020)

Resources:
Quotes on "Conceptual Models" at the-web-of-knowledge.blogspot.com.

28 June 2013

🎓Knowledge Management: Cognitive Map (Definitions)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"A cognitive map is the representation of thinking about a problem that follows from the process of mapping." (Colin Eden, "Analyzing cognitive maps to help structure issues or problems", 2002)

"A mental representation of a portion of the physical environment and the relative locations of points within it." (Andrew M Colman, "A Dictionary of Psychology" 3rd Ed, 2008)

"A mental model (or map) of the external environment which may be constructed following exploratory behaviour." (Michael Allaby, "A Dictionary of Zoology" 3rd Ed., 2009)

"An FCM [Fuzzy Cognitive Map] is a directed graph with concepts like policies, events etc. as nodes and causalities as edges. It represents causal relationship between concepts." (Florentin Smarandache &  W B Vasantha Kandasamy, "Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps", 2014)

"A conceptual tool that provides a representation of particular natural or social environments in the form of a model." (Evangelos C Papakitsos et al, "The Challenges of Work-Based Learning via Systemic Modelling in the European Union", 2020)

"A representation of the conceptualization that the subject constructs of the system in which he evolves. The set of cognitive representations that emerge make it possible to understand his actions, the links between the factors structuring the cognitive patterns dictating his behaviors." (Henda E Karray & Souhaila Kammoun, "Strategic Orientation of the Managers of a Tunisian Family Group Before and After the Revolution", 2020)

"A cognitive map is a type of mental representation which serves an individual to acquire, code, store, recall, and decode information about the relative locations and attributes of phenomena in their everyday or metaphorical spatial environment." (Wikipedia) [source]

13 June 2013

🎓Knowledge Management: Tacit Knowledge (Definitions)

"Know-how that is difficult to articulate and share; intuition or skills that cannot easily be put into words." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"The domain of subjective, cognitive, and experimental knowledge that is highly personal and difficult to formalize." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The knowledge that a person retains in their mind. It is relatively hard to transfer to others and to disseminate widely. Also known as implicit knowledge." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Knowledge that is based on experience and not easy to share, document, or explain." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Knowledge that resides in people's heads. Also referred to as know-how, rules of thumb, or heuristics." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"Tacit knowledge is sometimes referred to as knowledge inside people’s heads. It includes the skills and intuition that experienced people apply as a matter of course in their work. Tacit knowledge is contrasted with explicit knowledge, which is knowledge that is documented in a sharable form. One of the goals of knowledge management is to enable tacit knowledge to be shared by making it explicit knowledge." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Undocumented information." (Project Management Institute, "Software Extension to the PMBOK® Guide 5th Ed", 2013)

"Personal knowledge that can be difficult to articulate and share such as beliefs, experience, and insights." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

07 June 2013

🎓Knowledge Management: Taxonomy (Definitions)

"A classification system." (Ruth C Clark & Chopeta Lyons, "Graphics for Learning", 2004)

"A hierarchical structure within which related items are organized, classified, or categorized, thus illustrating the relationships between them." (Richard Caladine, "Taxonomies for Technology", 2008)

"A taxonomy is a hierarchical structure displaying parent-child relationships (a classification). A taxonomy extends a vocabulary and is a special case of a the more general ontology." (Troels Andreasen & Henrik Bulskov, "Query Expansion by Taxonomy", 2008)

"An orderly classification that explicitly expresses the relationships, usually hierarchical (e.g., genus/species, whole/part, class/instance), between and among the things being classified." (J P Getty Trust, "Introduction to Metadata" 2nd Ed., 2008)

"This term traditionally refers to the study of the general principles of classification. It is widely used to describe computer-based systems that use hierarchies of topics to help users sift through information." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"A kind of classification method which organizes all kinds of things into predefined hierarchical structure." (Yong Yu et al, "Social Tagging: Properties and Applications", 2010)

"Any system of categories used to organize something, including documents, often less comprehensive than a thesaurus." (Steven Woods et al, "Knowledge Dissemination in Portals", 2011)

"Generally, a collection of controlled vocabulary terms organized into a structure of parent-child relationships. Each term is in at least one relationship with another term in the taxonomy. Each parent's relationship with all of its children are of only one type (whole-part, genus-species, or type-instance). The addition of associative relationships creates a thesaurus." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A definitional hierarchy of concepts. Traditional taxonomies are tree-structured (a concept is assumed to have exactly one superconcept and multiple subconcepts). The higher a concept is positioned in the definitional hierarchy, the more individuals it describes (the comprehension of the concept), but the less definitional properties are needed (the meaning of a concept)." (Marcus Spies & Said Tabet, "Emerging Standards and Protocols for Governance, Risk, and Compliance Management", 2012) 

"A hierarchical representation of metadata. The top level is the category, and each subsequent level provides a refinement (detail) of the top-level term." (Charles Cooper & Ann Rockley, "Managing Enterprise Content: A Unified Content Strategy" 2nd Ed., 2012)

"A hierarchical structure of information components, for example, a subject, business–unit, or functional taxonomy, any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Managing Electronic Records: Methods, Best Practices, and Technologies", 2013)

"A classification of text" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A hierarchical structure of information components (e.g., a subject, business unit, or functional taxonomy), any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"provides context within the ontology. Taxonomies are used to capture hierarchical relationships between elements of interest. " (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Taxonomy is the science and practice of classification. Taxonomies are used when categorizing real-life as well as artificial phenomenon and the aim is to make systematic studies easier." (Ulf Larson et al, "Guidance for Selecting Data Collection Mechanisms for Intrusion Detection", 2015)

"A taxonomy is a hierarchy that is created by a set of interconnected class inclusion relationship." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"A hierarchical structure of information components, for example, a subject, business unit, or functional taxonomy, any part of which can be used to classify a content item in relation to other items in the structure." (Robert F Smallwood, "Information Governance for Healthcare Professionals", 2018)

06 June 2013

🎓Knowledge Management: Ontology (Definitions)

"A data model that represents the entities that are defined and evaluated by its own attributes, and organized according to a hierarchy and a semantic. Ontologies are used for representing knowledge on the whole of a specific domain or on of it." (Gervásio Iwens et al, "Programming Body Sensor Networks", 2008)

"An ontology specifies a conceptualization, that is, a structure of related concepts for a given domain." (Troels Andreasen & Henrik Bulskov, "Query Expansion by Taxonomy", 2008)

"A semantic structure useful to standardize and provide rigorous definitions of the terminology used in a domain and to describe the knowledge of the domain. It is composed of a controlled vocabulary, which describes the concepts of the considered domain, and a semantic network, which describes the relations among such concepts. Each concept is connected to other concepts of the domain through semantic relations that specify the knowledge of the domain. A general concept can be described by several terms that can be synonyms or characteristic of different domains in which the concept exists. For this reason the ontologies tend to have a hierarchical structure, with generic concepts/terms at the higher levels of the hierarchy and specific concepts/terms at the lover levels, connected by different types of relations." (Mario Ceresa, "Clinical and Biomolecular Ontologies for E-Health", Handbook of Research on Distributed Medical Informatics and E-Health, 2009)

"In the context of knowledge sharing, the chapter uses the term ontology to mean a specification of conceptual relations. An ontology is the concepts and relationships that can exist for an agent or a community of agents. The chapter refers to designing ontologies for the purpose of enabling knowledge sharing and re-use." (Ivan Launders, "Socio-Technical Systems and Knowledge Representation", 2009)

 "The systematic description of a given phenomenon, which often includes a controlled vocabulary and relationships, captures nuances in meaning and enables knowledge sharing and reuse. Typically, ontology defines data entities, data attributes, relations and possible functions and operations." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"Those things that exist are those things that have a formal representation within the context of a machine. Knowledge commits to an ontology if it adheres to the structure, vocabulary and semantics intrinsic to a particular ontology i.e. it conforms to the ontology definition. A formal ontology in computer science is a logical theory that represents a conceptualization of real world concepts." (Philip D. Smart, "Semantic Web Rule Languages for Geospatial Ontologies", 2009)

"A formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain." (Yong Yu et al, "Social Tagging: Properties and Applications", 2010)

"Is set of well-defined concepts describing a specific domain." (Hak-Lae Kim et al, "Representing and Sharing Tagging Data Using the Social Semantic Cloud of Tags", 2010)

"An ontology is a 'formal, explicit specification of a shared conceptualisation'. It is composed of concepts and relations structured into hierarchies (i.e. they are linked together by using the Specialisation/Generalisation relationship). A heavyweight ontology is a lightweight ontology (i.e. an ontology simply based on a hierarchy of concepts and a hierarchy of relations) enriched with axioms used to fix the semantic interpretation of concepts and relations." (Francky Trichet et al, "OSIRIS: Ontology-Based System for Semantic Information Retrieval and Indexation Dedicated to Community and Open Web Spaces", 2011)

"The set of the things that can be dealt with in a particular domain, together with their relationships." (Steven Woods et al, "Knowledge Dissemination in Portals", 2011) 

"In semantic web and related technologies, an ontology (aka domain ontology) is a set of taxonomies together with typed relationships connecting concepts from the taxonomies and, possibly, sets of integrity rules and constraints defining classes and relationships." (Marcus Spies & Said Tabet, "Emerging Standards and Protocols for Governance, Risk, and Compliance Management", 2012)

"High-level knowledge and data representation structure. Ontologies provide a formal frame to represent the knowledge related with a complex domain, as a qualitative model of the system. Ontologies can be used to represent the structure of a domain by means of defining concepts and properties that relate them." (Lenka Lhotska et al, "Interoperability of Medical Devices and Information Systems", 2013)

"(a) In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about concepts. (b) In philosophy, ontology is the study of the nature of being, becoming, existence , or reality , as well as the basic categories of being and their relations. Traditionally listed as a part of the major branch of philosophy known as metaphysics, ontology deals with questions concerning what entities exist or can be said to exist, and how such entities can be grouped, related within a hierarchy, and subdivided according to similarities and differences." (Ronald J Lofaro, "Knowledge Engineering Methodology with Examples", 2015)

"It is a shared structure which classify and organizes all the entities of a given domain." (T R Gopalakrishnan Nair, "Intelligent Knowledge Systems", 2015)

"The study of how things relate. Used in big data to analyze seemingly unrelated data to discover insights." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"An ontology is a formal, explicit specification of a shared conceptualization." (Fu Zhang et al, "A Review of Answering Queries over Ontologies Based on Databases", 2016)

03 June 2013

🎓Knowledge Management: Explicit Knowledge (Definitions)

"Knowledge that is easily codified, shared, documented, and explained." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The knowledge that deals with objective, rational, and technical knowledge (data, policies, procedures, software, documents, etc.)." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Explicit knowledge is information that is captured in a way that it can be shared. People can learn it without having to rely directly on other people. In knowledge management practice, explicit knowledge is contrasted with tacit knowledge, which is knowledge that is inside people’s heads." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2012)

"Recorded information, for example, a written policy or procedure." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"Knowledge that can be codified using symbols such as words, numbers, and pictures." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

02 June 2013

🎓Knowledge Management: Knowledge Management (Definitions)

"The conscious and systematic facilitation of knowledge creation or development, diffusion or transfer, safeguarding, and use at the individual, team- and organizational level." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"The field of study that relates to the centralized management of a company’s corporate knowledge and information assets in order to provide this knowledge to as many company staff members as possible and thus encourage better and more consistent decision making." (Evan Levy & Jill Dyché, "Customer Data Integration", 2006)

"Discipline that intends to provide, at its most suitable level, the accurate information and knowledge for the right people, whenever they may be needed and at their best convenience." (J Ares, "Guidelines for Deploying a Knowledge Management System", 2008)

"The process of creating, capturing and organizing knowledge objects. A knowledge object might be a research report, a budget for the development of a new product, or a video presentation. Knowledge Management programs seek to capture objects in a repository that is searchable and accessible in electronic form." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"The process established to capture and use specific knowledge in an organization for the purpose of improving organizational performance." (Murray E Jennex, "Technologies in Support of Knowledge Management Systems", 2009)

"1.The management of an environment where people generate tacit knowledge, render it into explicit knowledge, and feed it back to the organization. The cycle forms a base for more tacit knowledge, which keeps the cycle going in an intelligent learning organization. (Brackett 2011) 2.The discipline that fosters organizational learning and the management of intellectual capital as an enterprise resource." (DAMA International, "The DAMA Dictionary of Data Management" 1st Ed., 2010)

"The process that helps organizations identify, select, organize, disseminate, and transfer important information and expertise that are part of the organization's memory and that may reside in unstructured form within the organization." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Knowledge management is a set of practices related to how organizations learn from their own experiences. Many of these practices focus on ensuring that what employees know and learn is captured in a shareable form (explicit knowledge)." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The accumulation, organization, and use of experience and lessons learned, which can be leveraged to improve future decision-making efforts. KM often involves listing and indexing subject matter experts, project categories, reports, studies, proposals, and other intellectual property sources or outputs that are retained to build corporate memory. Good KM systems help train new employees and reduce the impact of turnover and retirement of key employees." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"The process of capturing, using, leveraging, and sharing organizational knowledge." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"The intentional process of creation, acquisition and sharing of knowledge and its utilization as a key factor in the creation of added value. It is an inextricably human and cooperative process." (António C Moreira & Ricardo A Zimmermann, "Electronic Government: Challenges for Public Services Consumer Behaviour and Value Creation", 2015)

"Knowledge management is considered as a systematic process of managing knowledge assets, processes, and environment to facilitate the creation, organization, sharing, utilization, and measurement of knowledge to achieve the strategic aims of an organization." (Haitham Alali et al, "Knowledge Sharing Success Model of Virtual Communities of Practice in Healthcare Sector", 2016)

"Knowledge management promotes activities and processes to acquire, create, document, and share formal explicit knowledge and informal implicit knowledge. Knowledge management involves identifying a group of people who have a need to share knowledge, developing technological support that enables knowledge sharing, and creating a process for transferring and disseminating knowledge." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"The process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieving organizational objectives by making the best use of knowledge." (Izabella V Lokshina et al, "Internet of Things and Big Data-Driven Data Analysis Services for Third Parties", 2019)

"The methods and underlying policies for sharing information effectively so that the sum of the skills, experience and entrepreneurial attributes of all stakeholders is greater than the sum of the individual parts. If done well, each stakeholder also benefits, thus increasing the ‘sum of the individual parts’ that go on to increase the ‘sum of the whole’ in a virtuous circle." (Sue Milton, "Data Privacy vs. Data Security", 2021)

01 June 2013

🎓Knowledge Management: Knowledge (Definitions)

"Justified true belief, the know-what/-how/-who/-why that individuals use to solve problems, make predictions or decisions, or take actions." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"An individual’s understanding of facts or information. Knowledge provides the basis for performing a skill that an individual must have to perform a task successfully." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management" 2nd Ed., 2009)

"1.Generally, expertise; familiarity gained through experience or association; cognizance, the fact or condition of knowing something; the acquaintance with or the understanding of something; the fact or condition of being aware of something, of apprehending truth or fact." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The body of information and facts about a specific subject. Knowledge implies familiarity, awareness, and understanding of information as it applies to an environment. A key characteristic of knowledge is that 'new' knowledge can be derived from 'old' knowledge." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"The fact or condition of knowing something with familiarity gained through experience or association. Knowledge adds understanding and retention to information." (Craig S Mullins, "Database Administration", 2012)

"The metadata about all the changes that a participant has seen and maintains." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A collection of specialized facts, procedures, and judgment rules. Knowledge refers to what one knows and understands. Knowledge is categorized as unstructured, structured, explicit, or implicit. What we know we know we call explicit knowledge. Knowledge that is unstructured and understood, but not clearly expressed, we call implicit knowledge." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence 3rd Ed.", 2017)

"A mixture of experience, values and beliefs, contextual information, intuition, and insight that people use to make sense of new experiences and information." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide)", 2017)

"Knowing something with the familiarity gained through experience, education, observation, or investigation; it is understanding a process, practice, or technique, or how to use a tool." (Project Management Institute, "Project Manager Competency Development Framework" 3rd Ed., 2017)

"That array of facts and relationships that an individual has available to him or her for the performance of work, it may be part or all of an accepted body of knowledge, or knowledge that has been produced as largely self-generated content by the individual." (Catherine Burke et al, "Systems Leadership" 2nd Ed., 2018)

"The sum of a person’s - or mankind’s - information about and ability to understand the world." (Open Data Handbook)

24 March 2010

🎓Knowledge Management: Definitions (Part I: The Stored Procedure Case)

I just started to read the SQL Server Programmers Blog’s post on Stored Procedures  and I’m having a problem with the first sentence: "A stored procedure is a group of Transact-Sql statements compiled into a single execution plan". Not sure if the respective statement was supposed to define what a stored procedure is or highlight its most important characteristics; if it's the first choice then, if I’m not mistaking, also a user-defined function (UDF) (referring here especially to multi-statement UDFs) is a group of compiled T-SQL Statements for which is created an execution plan, and a user-defined function is not a stored procedure! In general I’m having a simple rule I’m trying to use when defining concepts - if in doubt with a definition for a given concept stick to the documentation or to the "classics" – definitions provided by well known professionals or sources. (SQL Server 2016 introduced natively compiled UDFs)

From the few books I roughly reviewed on SQL Server-related topics I liked P. Petrovic’s approach for defining the stored procedure, he introducing first the batch defined as “a sequence of Transact-SQL statements and procedural extensions” that “can be stored as a database object, as either a stored procedure or UDF [3]. Now even if I like the approach I’m having a problem on how he introduced the routine because he haven’t gave a proper definition and just mentioned that a routine can be either a stored procedure or UDF [3]. On the other side it’s not always necessary to introduce terms part of the common shared conceptual knowledge, though I find it useful and constructive if such a definition would have been given. 

A definition should be clear, accurate, comprehensive, simple and should avoid confusion, eventually by specifying the intrinsic/extrinsic characteristics that differentiate the defined object from other objects, and the context in which is used. Unfortunately there are also definitions given by professionals that don't meet this criteria,  for example A. Watt  defines a stored procedure as "a module of code that allows you to reuse a desired piece of functionality and call that functionality by name" [5], while R. Dewson defines it as “a collection of compiled T-SQL commands that are directly accessible by SQL Server” [2], R. Rankins et. al as “one or more SQL commands stored in a database as an executable object” [4] or D. Comingore et Al “stored collections of queries or logic used to fulfill a specific requirement” [1]. All definitions are valid and in spite similarities I find them incomplete because they could be used as well for defining an UDF.

      Of course, by reading the chapter or the whole book in which the definition is given, by comparing the concept with other concepts, people arrive to a more accurate form of the definition(s), though that’s not always efficient and constructive because the reader has to “fish” for all the direct-related concepts and highlight the similarities/differences between them (e.g. stored procedures vs. UDF vs. views vs. triggers). Usually for easier assimilation and recall I like to use Knowledge Mapping structures/techniques like Mind Maps or Concept Maps that allows seeing the relations (including similarities/differences) between concepts and even identify new associations between them. In addition, when learning concepts it matters also the form/sequence in which the concepts are presented (maybe that’s why many people prefer a PowerPoint presentation than reading a whole book).

Actually a definition could be built starting from the identified characteristics/properties of concepts and the similarities/differences with other concepts  For example Bill Inmon defines the data warehouse as “a subject-oriented, integrated, time-varying, non-volatile collection of data in support of the management's decision-making process” and even if we could philosophy also on this subject and the intrinsic characteristics of a data warehouse, it reflects Bill Inmon’s conception in a clear, simplistic and direct manner. I could attempt to define a stored procedure using the following considerations:
- it encapsulates T-SQL statements and procedural extensions (input/output parameters, variables, loops, etc.);
- it can be executed as a single statement and thus not reused in other DML or DDL statements;
- it’s a database object;
- caches, optimizes and reuses query execution plans;
- allows code modularization allowing thus code reuse, easier/centralized code maintenance, move business logic (including validation) on the backend;
- minimizes the risks of SQL injection attacks;
- can return multiple recordsets and parameterized calls reducing thus network traffic;
- enforce security by providing restricted access to tables;
- allows some degree of portability and standardized access (given the fact that many RDBMS feature stored procedures).
- allows specifying execution context;
- allows using output parameters, cursors, temporary tables, nested calls (procedure in procedure), create and execute dynamic queries, access to system resources, trap and customize errors, perform transaction-based operations.

Some of the mentioned characteristics apply also to other database objects or they are not essential to be mentioned in a general definition, thus giving a relatively accurate definition for stored procedures is not an easy task. Eventually I could focus on the first three points mentioned above, thus an approximate definition would reduce to the following formulation: a stored procedure is a database object that encapsulates T-SQL statements and procedural extensions, the object being executed exclusively within a single statement using its name and the eventual parameters”. This definition might not be the best, though it’s workable and could be evolved in case new knowledge is discovered or essential new functionality changes are introduced.

Note:
Unfortunately, in several books read on data warehousing topics I found similar incomplete/vague definitions that seem inadequate given the complexity of the subjects exposed, letting thus lot of place for divagations. Of course, in technical books the weight is more on the use of exposed concepts rather on concepts’ definition, though I’m expecting more from such books!

Disclaimer:

I’m not trying to question the impressive work of other professionals, and I'm aware that I can mistake as well! I’m just trying to point out a fact I remarked and I consider as important: trying to give a proper accurate definition of the terms introduced in a book or any other form of communication can prove to be challenging.

References:
[1] Comingore D.,  Hinson D. (2006). Professional SQL Server™ 2005 CLR Programming. Wiley Publishing. ISBN: 978-0-470-05403-1.
[2] Dewson R. (2008). Beginning SQL Server 2008 for Developers: From Novice to Professional. Apress. ISBN: 978-1-4302-0584-5
[3] Petkovic D. (2008). Microsoft® SQL Server™ 2008: A Beginner’s Guide. McGraw-Hill. ISBN: 0-07-154639-1
[4] Rankins R.,  Bertucci P., Gallelli C., Silverstein A.T,  (2007) Microsoft® SQL Server 2005 Unleashed. Sams Publishing. ISBN: 0-672-32824-0
[5] Watt A. (2006). Microsoft SQL Server 2005 for Dummies. Wiley Publishing. ISBN: 978-0-7645-7755-0.
[6] Inmon W.H. (2005) Building the Data Warehouse, 4th Ed. Wiley Publishing. ISBN: 978-0-7645-9944-6

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Koeln, NRW, Germany
IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.