20 March 2017

Data Management: Data Structure (Definitions)

"A logical relationship among data elements that is designed to support specific data manipulation functions (trees, lists, and tables)." (William H Inmon, "Building the Data Warehouse", 2005)

"Data stored in a computer in a way that (usually) allows efficient retrieval of the data. Arrays and hashes are examples of data structures." (Michael Fitzgerald, "Learning Ruby", 2007)

"A data structure in computer science is a way of storing data to be used efficiently." (Sahar Shabanah, "Computer Games for Algorithm Learning", 2011)

"Data structure is a general term referring to how data is organized. In modeling, it refers more specifically to the model itself. Tables are referred to as 'structures'." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

[probabilistic *] "A data structure which exploits randomness to boost its efficiency, for example skip lists and Bloom filters. In the case of Bloom filters, the results of certain operations may be incorrect with a small probability." (Wei-Chih Huang & William J Knottenbelt, "Low-Overhead Development of Scalable Resource-Efficient Software Systems", 2014)

"A collection of methods for storing and organizing sets of data in order to facilitate access to them. More formally data structures are concise implementations of abstract data types, where an abstract data type is a set of objects together with a collection of operations on the elements of the set." (Ioannis Kouris et al, "Indexing and Compressing Text", 2015)

"A representation of the logical relationship between elements of data." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Is a schematic organization of data and relationship to express a reality of interest, usually represented in a diagrammatic form." (Maria T Artese  Isabella Gagliardi, "UNESCO Intangible Cultural Heritage Management on the Web", 2015)

"The implementation of a composite data field in an abstract data type" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"A way of organizing data so that it can be efficiently accessed and updated." (Vasileios Zois et al, "Querying of Time Series for Big Data Analytics", 2016)

"A particular way of storing information, allowing to a high level approach on the software implementation." (Katia Tannous & Fillipe de Souza Silva, "Particle Shape Analysis Using Digital Image Processing", 2018)

"It is a particular way of organizing data in a computer so that they can be used efficiently." (Edgar C Franco et al, "Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods...", 2019)

"Way information is represented and stored." (Shalin Hai-Jew, "Methods for Analyzing and Leveraging Online Learning Data", 2019)

"A physical or logical relationship among a collection of data elements." (IEEE 610.5-1990)

Data Management: Data Sharing (Definitions)

"The ability to share individual pieces of data transparently from a database across different applications." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"Exchange of data and/or meta-data in a situation involving the use of open, freely available data formats, where process patterns are known and standard, and where not limited by privacy and confidentiality regulations." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data sharing involves one entity sending data to another entity, usually with the understanding that the other entity will store and use the data. This process may involve free or purchased data, and it may be done willingly, or in compliance with regulations, laws, or court orders." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"The ability of subsystems or application programs to access data directly and to change it while maintaining data integrity." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"The ability of two or more DB2 subsystems to directly access and change a single set of data." (BMC)

Data Management: Information Overload (Definitions)

"A state in which information can no longer be internalized productively by the individual due to time constraints or the large volume of received information." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"Phenomena related to the inability to absorb and manage effectively large amounts of information, creating inefficiencies, stress, and frustration. It has been exacerbated by advances in the generation, storage, and electronic communication of information." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A situation where relevant information becomes buried in a mass of irrelevant information" (Josep C Morales, "Information Disasters in Networked Organizations", 2008)

"A situation where individuals have access to so much information that it becomes impossible for them to function effectively, sometimes leading to where nothing gets done and the user gives the impression of being a rabbit caught in the glare of car headlights." Alan Pritchard, "Information-Rich Learning Concepts", 2009)

"is the situation when the information processing requirements exceed the information processing capacities." (Jeroen ter Heerdt & Tanya Bondarouk, "Information Overload in the New World of Work: Qualitative Study into the Reasons", 2009)

"Refers to an excess amount of information, making it difficult for individuals to effectively absorb and use information; increases the likelihood of poor decisions." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed., 2011)

"The inability to cope with or process ever-growing amounts of data into our lives." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The state where the rate or amount of input to a system or person outstrips the capacity or speed of processing that input successfully." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The state in which a huge influx of information interferes with understanding an issue, making good decisions, and performance on the job." (Carol A. Brown, "Economic Impact of Information and Communication Technology in Higher Education", 2014)

"The difficulty a person can have understanding an issue and making decisions that can be caused by the presence of too much information." (Li Chen, "Mobile Technostress", Encyclopedia of Mobile Phone Behavior, 2015)

"Occurs when excess of information suffocates businesses and causes employees to suffer mental anguish and physical illness. Information overload causes high levels of stress that can result in health problems and the breakdown of individuals’ personal relationships." (Sérgio Maravilhas & Sérgio R G Oliveira, "Entrepreneurship and Innovation: The Search for the Business Idea", 2018)

"A set of subjective and objective difficulties, mainly originating in the amount and complexity of information available and people’s inability to handle such situations." (Tibor Koltay, "Information Overload", 2021)

19 March 2017

Data Management: Encryption (Definitions)

"A method for keeping sensitive information confidential by changing data into an unreadable form." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"The encoding of data so that the plain text is transformed into something unintelligible, called cipher text." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Reordering of bits of data to make it unintelligible (and therefore useless) to an unauthorized third party, while still enabling the authorized user to use the data after the reverse process of decryption." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"To transform information from readable plain text to unreadable cipher text to prevent unintended recipients from reading the data." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"The process of transforming data using an algorithm (called a cipher) to make it unreadable to anyone except those possessing special knowledge, usually referred to as a key." (Craig S Mullins, "Database Administration", 2012)

"The process of converting readable data (plaintext) into a coded form (ciphertext) to prevent it from being read by an unauthorized party." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The cryptographic transformation of data to produce ciphertext." (Manish Agrawal, "Information Security and IT Risk Management", 2014)

"The process of scrambling data in such a way that it is unreadable by unauthorized users but can be unscrambled by authorized users to be readable again." (Weiss, "Auditing IT Infrastructures for Compliance, 2nd Ed", 2015)

"The transformation of plaintext into unreadable ciphertext." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide, 8th Ed", 2018)

"In computer security, the process of transforming data into an unintelligible form in such a way that the original data either cannot be obtained or can be obtained only by using a decryption process." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"Encryption is about translating the data into complex codes that cannot be interpreted (decrypted) without the use of a decryption key. These keys are typically distributed and stored separately. There are two types of encryption: symmetric key encryption and public key encryption. In symmetric key encryption, the key to both encrypt and decrypt is exactly the same. Public key encryption has two different keys. One key is used to encrypt the values (the public key), and one key is used to decrypt the data (the private key)." (Piethein Strengholt, "Data Management at Scale", 2020)

"The process of encoding data in such a way to prevent unauthorized access." (AICPA)

16 March 2017

Data Management: Missing Data (Definitions)

"Noise in a bivalent testing input pattern in which one or more components have been changed from the correct value to a value midway between the correct and the incorrect value, i.e. a + 1, or a -1, has been changed to a O." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"Many databases have cases where not all the attribute values are known. These can be due to structural reasons (e.g., parity for males), due to changes or variations in data collection methodology, or due to nonresponses. In the latter case, it is important to distinguish between ignorable and nonignorable nonresponse. The former must be addressed even though the latter can (usually) be treated as random." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"Observations where one or more variables contain no value." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"data are said to be missing when there is no information for one or more pattern on one or more features in a research study." (Pedro J García-Laencina et al, "Classification with Incomplete Data", 2010)

"Missing data, also known as lost data, is the data that is lost in an inner join when rows of the tables being joined do not match with any other rows. Missing data can also occur with one-sided joins on the side that is not being preserved. This definition ignores all the other reasons for missing data." (Michael M David & Lee Fesperman, "Advanced SQL Dynamic Data Modeling and Hierarchical Processing", 2013)

"It refers that no data value is stored for the variable in the observation." (Liang-Ting Tsai et al, "Weighting Imputation for Categorical Data", 2014)

"Observations which were planned and are missing." (OECD)

"In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data." (Wikipedia)

15 March 2017

Data Management: Data Compression (Definitions)

"any kind of data reduction method that preserves the application-specific information." (Teuvo Kohonen, "Self-Organizing Maps 3rd Ed.", 2001)

"The process of reducing the size of data by use of mathematical algorithms." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"1.Algorithms or techniques that change data to a smaller physical size that contains the same information. 2.The process of changing data to be stored in a smaller physical or logical space." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Encoding information in such a way that its representation consumes less space in memory" (Hasso Plattner, "A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases 2nd Ed.", 2014)

"Compression is a data management technique that uses repeating patterns in data to reduce the storage needed to hold the data. A compression algorithm for databases should perform compression and decompression operations as fast as possible. This often entails a trade-off between the speed of compression/decompression and the size of the compressed data. Faster compression algorithms can lead to larger compressed data than other, slower algorithms." (Dan Sullivan, "NoSQL for Mere Mortals®", 2015)

"Reducing the amount of space needed to store a piece of data" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"The process of reducing the size of a data file by encoding information using fewer bits than the original file." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"A method that reduces the amount of space needed for storing data. See also client compression and hardware compression." (CommVault, "Documentation 11.20", 2018)

"Any technique used to reduce the amount of storage required to store data." (IEEE 610.5-1990)

14 March 2017

Data Management: Data Protection (Definitions)

"The protecting of data from damage, destruction, and unauthorized alteration." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Deals with issues such as data security, privacy, and availability. Data protection controls are required by regulations and industry mandates such as Sarbanes-Oxley, European Data Protection Law, and others." (Allen Dreibelbis et al, "Enterprise Master Data Management", 2008)

"A set of rules that aim to protect the rights, freedoms and interests of individuals when information related to them is being processed." (Maria Tzanou, "Data Protection in EU Law after Lisbon: Challenges, Developments, and Limitations", 2015)

"An umbrella term for various procedures that ensure information is secure and available only to authorized users." (Peter Sasvari & Zoltán Nagymate, "The Empirical Analysis of Cloud Computing Services among the Hungarian Enterprises", 2015)

"Protection of the data against unauthorized access by third parties as well as protection of personal data (such as customer data) in the processing of data according to the applicable legal provisions." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"Legal control over access to, and use of, data in computers." (Lucy Self & Petros Chamakiotis, "Understanding Cloud Computing in a Higher Education Context", 2018)

"Data protection is a task of safeguarding personal or sensitive data which are complex and widely distributed." (M Fevzi Esen & Eda Kocabas, "Personal Data Privacy and Protection in the Meeting, Incentive, Convention, and Exhibition (MICE) Industry", 2019)

"Process of protecting important information from corruption, compromise, or loss." (Patrícia C T Gonçalves, "Medical Social Networks, Epidemiology and Health Systems", 2021)

"The process involving use of laws to protect data of individuals from unauthorized disclosure or access." (Frank Makoza, "Learning From Abroad on SIM Card Registration Policy: The Case of Malawi", 2019)

"Is the process in information and communication technology that deals with the ability an organization or individual to safeguard data and information from corruption, theft, compromise, or loss." (Valerianus Hashiyana et al, "Integrated Big Data E-Healthcare Solutions to a Fragmented Health Information System in Namibia", 2021)

"The mechanisms with which an organization enables individuals to retain control of the personal data they willingly share, where security provides policies, controls, protocols, and technologies necessary to fulfill rules and obligations in accordance with privacy regulations, industry standards, and the organization's ethics and social responsibility." (Forrester)

06 March 2017

Data Management: Audit Trail (Definitions)

"Audit records stored in the sybsecurity database." (Karen Paulsell et al, "Sybase SQL Server: Performance and Tuning Guide", 1996)

"A record of what happened to data from its inception to its current state. Audit trails help verify the integrity of data." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"Data maintained to trace activity, such as a transaction log, for purposes of recovery or audit." (Craig S Mullins, "Database Administration", 2012)

"A chronological record of activities on information resources that enables the reconstruction and examination of sequences of activities on those information resources for later review." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition" 2nd Ed., 2013)

"A trace of a sequence of events in a clerical or computer system. This audit usually identifies the creation or modification of any element in the system, who did it, and (possibly) why it was done." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A chronological record of events or transactions. An audit trail is used for examining or reconstructing a sequence of events or transactions, managing security, and recovering lost transactions." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

 "A path by which the original input to a process (e.g. data) can be traced back through the process, taking the process output as a starting point. This facilitates result checking and allows a process audit to be carried out [after TMap]." (Software Quality Assurance)

05 March 2017

Data Management: System of Record (Definitions)

"The system that definitively specifies data values. In dealing with redundant data, you can have values that should be the same but disagree. The system of record is the system you go back to, in order to verify the true value of the data." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"The definitive and singular source of operational data. If data element ABC has a value of 25 in a database record but a value of 45 in the system of record, by definition, the first value is incorrect and must be reconciled. The system of record is useful for managing redundancy of data." (William H Inmon, "Building the Data Warehouse", 2005)

"The single authoritative, enterprise-designated source of operational data. It is the most current, accurate source of its data." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"A system that stores the 'official' version of a data attribute." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The system of record is a system that is charged with keeping the most complete or trustworthy representation of a set of entities. Within the practice of master data management, such representations are referred to as golden records and the system of record can also be called the system of truth." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Records from which information is retrieved by the name, identifying number, symbol, or other identifying particular assigned to the individual. Sometimes abbreviated as SOR." ( Manish Agrawal, "Information Security and IT Risk Management", 2014)

"An information storage system (commonly implemented on a computer system) that is the authoritative data source for a given data element or piece of information. The need to identify systems of record can become acute in organizations where management information systems have been built by taking output data from multiple-source systems, reprocessing this data, and then re-presenting the result for a new business use." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

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