🔬Data Science: Data Visualization (Definitions)

"Technique for presentation and analysis of data through visual objects, such as graphs, charts, images, and specialized tabular formats." (Paulraj Ponniah, "Data Warehousing Fundamentals", 2001)

"Technique for presentation and analysis of data through visual objects, such as graphs, charts, images, and specialized tabular formats." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010) 

"Visual representation of data, aiming to convey as much information as possible through visual processes." (Alfredo Vellido & Iván Olie, "Clustering and Visualization of Multivariate Time Series", 2010)

"Techniques for graphical representation of trends, patterns and other information." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Information abstracted in a schematic form to provide visual insights into sets of data. Data visualization enables us to go from the abstract numbers in a computer program (ones and zeros) to visual interpretation of data. Text visualization means converting textual information into graphic representation, so we can see information without having to read the data, as tables, histograms, pie or bar charts, or Cartesian coordinates." (Anna Ursyn, "Visualization as Communication with Graphic Representation", 2015)

"[...] data visualization [is] a tool that, by applying perceptual mechanisms to the visual representation of abstract quantitative data, facilitates the search for relevant shapes, order, or exceptions." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"Presenting data and summary information using graphics, animation, and three-dimensional displays. Tools for visually displaying information and relationships often using dynamic and interactive graphics." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"Data Visualization is a way of representing the data collected in the form of figures and diagrams like tables, charts, graphs in order to make the data for common man more easily understandable." (Kirti R Bhatele, "Data Analysis on Global Stratification", 2020)

"Techniques for turning data into information by using the high capacity of the human brain to visually recognize patterns and trends. There are many specialized techniques designed to make particular kinds of visualization easy." (Information Management)

"The art of communicating meaningful data visually. This can involve infographics, traditional plots, or even full data dashboards." (KDnuggets)

"The practice of structuring and arranging data within a visual context to help users understand it. Patterns and trends that might be unrecognizable to the layman in text-based data can be easily viewed and digested by end users with the help of data visualization software." (Insight Software)

"Data visualization enables people to easily uncover actionable insights by presenting information and data in graphical, and often interactive graphs, charts, and maps." (Qlik) [source]

"Data visualization is the graphical representation of data to help people understand context and significance. Interactive data visualization enables companies to drill down to explore details, identify patterns and outliers, and change which data is processed and/or excluded." (Tibco) [source]

"Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from." (Techtarget) [source]

"Data visualization is the process of graphically illustrating data sets to discover hidden patterns, trends, and relationships in order to develop key insights. Data visualization uses data points as a basis for the creation of graphs, charts, plots, and other images." (Talend) [source]

"Data visualization is the use of graphics to represent data. The purpose of these graphics is to quickly and concisely communicate the most important insights produced by data analytics." (Xplenty) [source]

🔬Data Science: Optimization (Definitions)

"Term used to describe analytics that calculate and determine the most ideal scenario to meet a specific target. Optimization procedures analyze each scenario and supply a score. An optimization analytic can run through hundreds, even thousands, of scenarios and rank each one based on a target that is being achieved." (Brittany Bullard, "Style and Statistics", 2016)

"Optimization is the process of finding the most efficient algorithm for a given task." (Edward T Chen, "Deep Learning and Sustainable Telemedicine", 2020)

🔬Data Science: Speech Recognition (Definitions)

"Automatic decoding of a sound pattern into phonemes or words." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps" 2nd Ed., 2000)

"Speech recognition is a process through which machines convert words or phrases spoken into a machine-readable format." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

🔬Data Science: Data Model (Definitions)

"A model that describes in an abstract way how data is represented in an information system. A data model can be a part of ontology, which is a description of how data is represented in an entire domain" (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"Description of the node structure that defines its entities, fields and relationships." (Roberto Barbera et al, "gLibrary/DRI: A Grid-Based Platform to Host Muliple Repositories for Digital Content", 2009)

"An abstract model that describes how data are presented, organized and related to." (Ali M Tanyer, "Design and Evaluation of an Integrated Design Practice Course in the Curriculum of Architecture", 2010)

"The first of a series of data models that more closely represented the real world, modeling both data and their relationships in a single structure known as an object. The SDM, published in 1981, was developed by M. Hammer and D. McLeod." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management 9th Ed", 2011)

"The way of organizing and representing data is data model." (Uma V & Jayanthi G, "Spatio-Temporal Hot Spot Analysis of Epidemic Diseases Using Geographic Information System for Improved Healthcare", 2019)

🔬Data Science: Correlation (Definitions)

[correlation coefficient:] "A measure to determine how closely a scatterplot of two continuous variables falls on a straight line." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A metric that measures the linear relationship between two process variables. Correlation describes the X and Y relationship with a single number (the Pearson’s Correlation Coefficient (r)), whereas regression summarizes the relationship with a line - the regression line." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

[correlation coefficient:] "A measure of the degree of correlation between the two variables. The range of values it takes is between −1 and +1. A negative value of r indicates an inverse relationship. A positive value of r indicates a direct relationship. A zero value of r indicates that the two variables are independent of each other. The closer r is to +1 and −1, the stronger the relationship between the two variables." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"The degree of relationship between business and economic variables such as cost and volume. Correlation analysis evaluates cause/effect relationships. It looks consistently at how the value of one variable changes when the value of the other is changed. A prediction can be made based on the relationship uncovered. An example is the effect of advertising on sales. A degree of correlation is measured statistically by the coefficient of determination (R-squared)." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"A figure quantifying the correlation between risk events. This number is between negative one and positive one." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A mechanism used to associate messages with the correct workflow service instance. Correlation is also used to associate multiple messaging activities with each other within a workflow." (Bruce Bukovics, "Pro WF: Windows Workflow in .NET 4", 2010)

"Correlation is sometimes used informally to mean a statistical association between two variables, or perhaps the strength of such an association. Technically, the correlation can be interpreted as the degree to which a linear relationship between the variables exists (i.e., each variable is a linear function of the other) as measured by the correlation coefficient." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

"The degree of relationship between two variables; in risk management, specifically the degree of relationship between potential risks." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A predictive relationship between two factors, such that when one factor changes, you can predict the nature, direction and/or amount of change in the other factor. Not necessarily a cause-and-effect relationship." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Organizing and recognizing one related event threat out of several reported, but previously distinct, events." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"Association in the values of two or more variables." (Meta S Brown, "Data Mining For Dummies", 2014)

[correlation coefficient:] "A statistic that quantifies the degree of association between two or more variables. There are many kinds of correlation coefficients, depending on the type of data and relationship predicted." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"The degree of association between two or more variables." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A statistical measure that indicates the extent to which two variables are related. A positive correlation indicates that, as one variable increases, the other increases as well. For a negative correlation, as one variable increases, the other decreases." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

🔬Data Science: Parametric Estimating (Definitions)

[parametric:] "A statistical procedure that makes assumptions concerning the frequency distributions." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A simplified mathematical description of a system or process, used to assist calculations and predictions. Generally speaking, parametric models calculate the dependent variables of cost and duration on the basis of one or more variables." (Project Management Institute, "Practice Standard for Project Estimating", 2010)

"An estimating technique that uses a statistical relationship between historical data and other variables (e.g., square footage in construction, lines of code in software development) to calculate an estimate for activity parameters, such as scope, cost, budget, and duration. An example for the cost parameter is multiplying the planned quantity of work to be performed by the historical cost per unit to obtain the estimated cost." (Project Management Institute, "Practice Standard for Project Estimating", 2010)

"A branch of statistics that assumes the data being examined comes from a variety of known probability distributions. In general, the tests sacrifice generalizability for speed of computation and precision, providing the requisite assumptions are met." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"An estimating technique in which an algorithm is used to calculate cost or duration based on historical data and project parameters." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)

"Inferential statistical procedures that rely on sample statistics to draw inferences about population parameters, such as mean and variance." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

🔬Data Science: Non-Parametric Tests (Definitions)

[nonparametric:] "A statistical procedure that does not require a normal distribution of the data." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A branch of statistics that makes no assumptions on the underlying distributions of the data being examined. In general, the tests are far more generalizable but sacrifice precision and power." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Inferential statistical procedures that do not rely on estimating population parameters such as the mean and variance." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A family of methods which makes no assumptions about the population distribution. Non-parametric methods most commonly work by ignoring the actual values, and, instead, analyzing only their ranks. This approach ensures that the test is not affected much by outliers, and does not assume any particular distribution. The clear advantage of non-parametric tests is that they do not require the assumption of sampling from a Gaussian population. When the assumption of Gaussian distribution does not hold, non-parametric tests have more power than parametric tests to detect differences." (Soheila Nasiri & Bijan Raahemi, "Non-Parametric Statistical Analysis of Rare Events in Healthcare", 2017)

🔬Data Science: Gaussian Distribution (Definitions)

"Represents a conventional scale for a normally distributed bell-shaped curve that has a central tendency of zero and a standard deviation of one unit, wherein the units are called sigma (σ)." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"Also called the standard normal distribution, is the normal distribution with mean zero and variance one." (Dimitrios G Tsalikakis et al, "Segmentation of Cardiac Magnetic Resonance Images", 2009)

"A normal distribution with the parameters μ = 0 and σ = 1. The random variable for this distribution is denoted by Z. The z-tables (values of the random variable Z and the corresponding probabilities) are widely used for normal distributions." (Peter Oakander et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)

🔬Data Science: K-nearest neighbors (Definitions)

"A modeling technique that assigns values to points based on the values of the k nearby points, such as average value, or most common value." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A simple and popular classifier algorithm that assigns a class (in a preexisting classification) to an object whose class is unknown. [...] From a collection of data objects whose class is known, the algorithm computes the distances from the object of unknown class to k (a number chosen by the user) objects of known class. The most common class (i.e., the class that is assigned most often to the nearest k objects) is assigned to the object of unknown class." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"A method used for classification and regression. Cases are analyzed, and class membership is assigned based on similarity to other cases, where cases that are similar (or 'near' in characteristics) are known as neighbors." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"A prediction method, which uses a function of the k most similar observations from the training set to generate a prediction, such as the mean." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"K-Nearest Neighbors classification is an instance-based supervised learning method that works well with distance-sensitive data." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"An algorithm that estimates an unknown data item as being like the majority of the k-closest neighbors to that item." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)

"K-nearest neighbourhood is a algorithm which stores all available cases and classifies new cases based on a similarity measure. It is used in statistical estimation and pattern recognition." (Aman Tyagi, "Healthcare-Internet of Things and Its Components: Technologies, Benefits, Algorithms, Security, and Challenges", 2021)

🔬Data Science: Data Mining (Definitions)

"The non-trivial extraction of implicit, previously unknown, and potentially useful information from data" (Frawley et al., "Knowledge discovery in databases: An overview", 1991)

"Data mining is the efficient discovery of valuable, nonobvious information from a large collection of data." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Data mining is the process of examining large amounts of aggregated data. The objective of data mining is to either predict what may happen based on trends or patterns in the data or to discover interesting correlations in the data." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A data-driven approach to analysis and prediction by applying sophisticated techniques and algorithms to discover knowledge." (Paulraj Ponniah, "Data Warehousing Fundamentals", 2001)

"A class of undirected queries, often against the most atomic data, that seek to find unexpected patterns in the data. The most valuable results from data mining are clustering, classifying, estimating, predicting, and finding things that occur together. There are many kinds of tools that play a role in data mining. The principal tools include decision trees, neural networks, memory- and cased-based reasoning tools, visualization tools, genetic algorithms, fuzzy logic, and classical statistics. Generally, data mining is a client of the data warehouse." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"The discovery of information hidden within data." (William A Giovinazzo, "Internet-Enabled Business Intelligence", 2002)

"the process of extracting valid, authentic, and actionable information from large databases." (Seth Paul et al. "Preparing and Mining Data with Microsoft SQL Server 2000 and Analysis", 2002)

"Advanced analysis or data mining is the analysis of detailed data to detect patterns, behaviors, and relationships in data that were previously only partially known or at times totally unknown." (Margaret Y Chu, "Blissful Data", 2004)

"Analysis of detail data to discover relationships, patterns, or associations between values." (Margaret Y Chu, "Blissful Data ", 2004)

"An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques, and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"the process of analyzing large amounts of data in search of previously undiscovered business patterns." (William H Inmon, "Building the Data Warehouse", 2005)

"A type of advanced analysis used to determine certain patterns within data. Data mining is most often associated with predictive analysis based on historical detail, and the generation of models for further analysis and query." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)

"Refers to the process of identifying nontrivial facts, patterns and relationships from large databases. The databases have often been put together for a different purpose from the data mining exercise." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Data mining is the process of discovering implicit patterns in data stored in data warehouse and using those patterns for business advantage such as predicting future trends." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"Digging through data (usually in a data warehouse or data mart) to identify interesting patterns." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"Intelligently analyzing data to extract hidden trends, patterns, and information. Commonly used by statisticians, data analysts and Management Information Systems communities." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"The process of extracting valid, authentic, and actionable information from large databases." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"The process of retrieving relevant data to make intelligent decisions." (Robert D Schneider & Darril Gibson, "Microsoft SQL Server 2008 All-in-One Desk Reference For Dummies", 2008)

"A process that minimally has four stages: (1) data preparation that may involve 'data cleaning' and even 'data transformation', (2) initial exploration of the data, (3) model building or pattern identification, and (4) deployment, which means subjecting new data to the 'model' to predict outcomes of cases found in the new data." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Automatically searching large volumes of data for patterns or associations." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"The use of machine learning algorithms to find faint patterns of relationship between data elements in large, noisy, and messy data sets, which can lead to actions to increase benefit in some form (diagnosis, profit, detection, etc.)." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"A data-driven approach to analysis and prediction by applying sophisticated techniques and algorithms to discover knowledge." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010) 

"A way of extracting knowledge from a database by searching for correlations in the data and presenting promising hypotheses to the user for analysis and consideration." (Toby J Teorey, "Database Modeling and Design" 4th Ed., 2010)

"The process of using mathematical algorithms (usually implemented in computer software) to attempt to transform raw data into information that is not otherwise visible (for example, creating a query to forecast sales for the future based on sales from the past)." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

"A process that employs automated tools to analyze data in a data warehouse and other sources and to proactively identify possible relationships and anomalies." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Process of analyzing data from different perspectives and summarizing it into useful information (e.g., information that can be used to increase revenue, cuts costs, or both)." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The process of sifting through large amounts of data using pattern recognition, fuzzy logic, and other knowledge discovery statistical techniques to identify previously unknown, unsuspected, and potentially meaningful data content relationships and trends." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data mining, a branch of computer science, is the process of extracting patterns from large data sets by combining statistical analysis and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage." (T T Wong & Loretta K W Sze, "A Neuro-Fuzzy Partner Selection System for Business Social Networks", 2012)

"Field of analytics with structured data. The model inference process minimally has four stages: data preparation, involving data cleaning, transformation and selection; initial exploration of the data; model building or pattern identification; and deployment, putting new data through the model to obtain their predicted outcomes." (Gary Miner et al, "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", 2012)

"The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The process of exploring and analyzing large amounts of data to find patterns." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"An umbrella term for analytic techniques that facilitate fast pattern discovery and model building, particularly with large datasets." (Meta S Brown, "Data Mining For Dummies", 2014)

"Analysis of large quantities of data to find patterns such as groups of records, unusual records, and dependencies" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"The practice of analyzing big data using mathematical models to develop insights, usually including machine learning algorithms as opposed to statistical methods."(Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Data mining is the analysis of data for relationships that have not previously been discovered." (Piyush K Shukla & Madhuvan Dixit, "Big Data: An Emerging Field of Data Engineering", Handbook of Research on Security Considerations in Cloud Computing, 2015)

"A methodology used by organizations to better understand their customers, products, markets, or any other phase of the business." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Extracting information from a database to zero in on certain facts or summarize a large amount of data." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"It refers to the process of identifying and extracting patterns in large data sets based on artificial intelligence, machine learning, and statistical techniques." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"The process of exploring and analyzing large amounts of data to find patterns." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Term used to describe analyzing large amounts of data to find patterns, correlations, and similarities." (Brittany Bullard, "Style and Statistics", 2016)

"The process of extracting meaningful knowledge from large volumes of data contained in data warehouses." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A class of analytical applications that help users search for hidden patterns in a data set. Data mining is a process of analyzing large amounts of data to identify data–content relationships. Data mining is one tool used in decision support special studies. This process is also known as data surfing or knowledge discovery." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"The process of collecting, searching through, and analyzing a large amount of data in a database to discover patterns or relationships." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"Data mining involves finding meaningful patterns and deriving insights from large data sets. It is closely related to analytics. Data mining uses statistics, machine learning, and artificial intelligence techniques to derive meaningful patterns." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"The analysis of the data held in data warehouses in order to produce new and useful information." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The process of collecting critical business information from a data source, correlating the information, and uncovering associations, patterns, and trends." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems." (Dmitry Korzun et al, "Semantic Methods for Data Mining in Smart Spaces", 2019)

"A technique using software tools geared for the user who typically does not know exactly what he's searching for, but is looking for particular patterns or trends. Data mining is the process of sifting through large amounts of data to produce data content relationships. It can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This is also known as data surfing." (Information Management)

"An analytical process that attempts to find correlations or patterns in large data sets for the purpose of data or knowledge discovery." (NIST SP 800-53)

"Extracting previously unknown information from databases and using that data for important business decisions, in many cases helping to create new insights." (Solutions Review)

"is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends." (Accenture)

"the process of analyzing large batches of data to find patterns and instances of statistical significance. By utilizing software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective strategies for acquisition, as well as increase sales and decrease overall costs." (Insight Software)

"The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools." (Microsoft)

"The process of pulling actionable insight out of a set of data and putting it to good use. This includes everything from cleaning and organizing the data; to analyzing it to find meaningful patterns and connections; to communicating those connections in a way that helps decision-makers improve their product or organization." (KDnuggets)

"Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue. Data mining is also known as data discovery and knowledge discovery." (Techopedia)

"Data mining is an automated analytical method that lets companies extract usable information from massive sets of raw data. Data mining combines several branches of computer science and analytics, relying on intelligent methods to uncover patterns and insights in large sets of information." (Sisense) [source]

"Data mining is the process of analyzing data from different sources and summarizing it into relevant information that can be used to help increase revenue and decrease costs. Its primary purpose is to find correlations or patterns among dozens of fields in large databases." (Logi Analytics) [source]

"Data mining is the process of analyzing massive volumes of data to discover business intelligence that helps companies solve problems, mitigate risks, and seize new opportunities." (Talend) [source]

"Data Mining is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends." (Accenture)

"Data mining is the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques." (Gartner)

"Data mining is the process of extracting relevant patterns, deviations and relationships within large data sets to predict outcomes and glean insights. Through it, companies convert big data into actionable information, relying upon statistical analysis, machine learning and computer science." (snowflake) [source]

"Data mining is the work of analyzing business information in order to discover patterns and create predictive models that can validate new business insights. […] Unlike data analytics, in which discovery goals are often not known or well defined at the outset, data mining efforts are usually driven by a specific absence of information that can’t be satisfied through standard data queries or reports. Data mining yields information from which predictive models can be derived and then tested, leading to a greater understanding of the marketplace." (Informatica) [source]

🔬Data Science: Normalization (Definitions)

"Mathematical transformations to generate a new set of values that map onto a different range." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

[Min–max normalization:] "Normalizing a variable value to a predetermine range." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

[function point normalization:] "Dividing a metric by the project’s function points to allow you to compare projects of different sizes and complexities." (Rod Stephens, "Beginning Software Engineering", 2015)

"For metrics, performing some calculation on a metric to account for possible differences in project size or complexity. Two general approaches are size normalization and function point normalization." (Rod Stephens, "Beginning Software Engineering", 2015)

[size normalization:] "For metrics, dividing a metric by an indicator of size such as lines of code or days of work. For example, bugs/KLOC tells you how buggy the code is normalized for the size of the project." (Rod Stephens, "Beginning Software Engineering", 2015)

🔬Data Science: Hadoop (Definitions)

"An Apache-managed software framework derived from MapReduce and Bigtable. Hadoop allows applications based on MapReduce to run on large clusters of commodity hardware. Hadoop is designed to parallelize data processing across computing nodes to speed computations and hide latency. Two major components of Hadoop exist: a massively scalable distributed file system that can support petabytes of data and a massively scalable MapReduce engine that computes results in batch." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"An open-source software platform developed by Apache Software Foundation for data-intensive applications where the data are often widely distributed across different hardware systems and geographical locations." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"Technology designed to house Big Data; a framework for managing data" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"an Apache-managed software framework derived from MapReduce. Big Table Hadoop enables applications based on MapReduce to run on large clusters of commodity hardware. Hadoop is designed to parallelize data processing across computing nodes to speed up computations and hide latency. The two major components of Hadoop are a massively scalable distributed file system that can support petabytes of data and a massively scalable MapReduce engine that computes results in batch." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"An open-source framework that is built to process and store huge amounts of data across a distributed file system." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"Open-source software framework for distributed storage and distributed processing of Big Data on clusters of commodity hardware." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"A batch processing infrastructure that stores fi les and distributes work across a group of servers. The infrastructure is composed of HDFS and MapReduce components. Hadoop is an open source software platform designed to store and process quantities of data that are too large for just one particular device or server. Hadoop’s strength lies in its ability to scale across thousands of commodity servers that don’t share memory or disk space." (Benoy Antony et al, "Professional Hadoop®", 2016)

"Apache Hadoop is an open-source framework for processing large volume of data in a clustered environment. It uses simple MapReduce programming model for reliable, scalable and distributed computing. The storage and computation both are distributed in this framework." (Kaushik Pal, 2016)

"A framework that allow for the distributed processing for large datasets." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

 "Hadoop is an open source implementation of the MapReduce paper. Initially, Hadoop required that the map, reduce, and any custom format readers be implemented and deployed to the cluster. Eventually, higher level abstractions were developed, like Apache Hive and Apache Pig." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A batch processing infrastructure that stores files and distributes work across a group of servers." (Oracle)

"an open-source framework that is built to enable the process and storage of big data across a distributed file system." (Analytics Insight)

"Apache Hadoop is an open-source, Java-based software platform that manages data processing and storage for big data applications. Hadoop works by distributing large data sets and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel. Hadoop can process both structured and unstructured data, and scale up reliably from a single server to thousands of machines." (Databricks) [source]

"Hadoop is an open source software framework for storing and processing large volumes of distributed data. It provides a set of instructions that organizes and processes data on many servers rather than from a centralized management nexus." (Informatica) [source]

🔬Data Science: Semantics (Definitions)

 "The meaning of a model that is well-formed according to the syntax of a language." (Anneke Kleppe et al, "MDA Explained: The Model Driven Architecture: Practice and Promise", 2003)

"The part of language concerned with meaning. For example, the phrases 'my mother’s brother' and 'my uncle' are two ways of saying the same thing and, therefore, have the same semantic value." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"The study of meaning (often the meaning of words). In business systems we are concerned with making the meaning of data explicit (structuring unstructured data), as well as making it explicit enough that an agent could reason about it." (Danette McGilvray, "Executing Data Quality Projects", 2008)

"The branch of philosophy concerned with describing meaning." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Having to do with meaning, usually of words and/or symbols (the syntax). Part of semiotic theory." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The study of the meaning behind the syntax (signs and symbols) of a language or graphical expression of something. The semantics can only be understood through the syntax. The syntax is like the encoded representation of the semantics." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The study of meaning. In the context of Big Data, semantics is the technique of creating meaningful assertions about data objects. A meaningful assertion, as used here, is a triple consisting of an identified data object, a data value, and a descriptor for the data value. In practical terms, semantics involves making assertions about data objects (i.e., making triples), combining assertions about data objects (i.e., merging triples), and assigning data objects to classes; hence relating triples to other triples. As a word of warning, few informaticians would define semantics in these terms, but I would suggest that most definitions for semantics would be functionally equivalent to the definition offered here." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"Set of mappings forming a representation in order to define the meaningful information of the data." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"Semantics is a branch of linguistics focused on the meaning communicated by language." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

🔬Data Science: Data Profiling (Definitions)

"A process focused on generating data metrics and measuring data quality. The data metrics can be collected at the column level, e.g., value frequency, nullity measurements, and uniqueness/match quality measurements; at the table level, e.g., primary key violations; or cross-table relationships, e.g., foreign key violations." (Alex Berson & Lawrence Dubov, "Master Data Management and Customer Data Integration for a Global Enterprise", 2007)

"A set of techniques for searching through data looking for potential errors and anomalies, such as similar data with different spellings, data outside boundaries and missing values." (Keith Gordon, "Principles of Data Management", 2007)

"Data profiling (and analysis services) provides functionality to understand the quality, structure, and relationships of data across enterprise systems, from which data cleansing and standardization rules can be determined for improving the overall data quality and consistency." (Martin Oberhofer et al,"Enterprise Master Data Management", 2008)

"A process for looking at the data within the source systems and understanding the data elements and the anomalies." (Tony Fisher, "The Data Asset", 2009)

"An approach to data quality analysis, using statistics to show patterns of usage, and patterns of contents, and automated as much as possible. Some profiling activities must be done manually, but most can be automated." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data profiling is used to assess the existing state of data quality. It is also used to understand the duplicates in the master data or the gaps in linkages. It can be used to understand the scope of data enrichment to enhance the value of customer data assets." (Saumya Chaki, "Enterprise Information Management in Practice", 2015)

"An automated method of analyzing large amounts of data to determine its quality and integrity." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"Data profiling assesses a set of data and provides information on the values, the length of strings, the level of completeness, and the distribution patterns of each column." (Robert Hawker, "Practical Data Quality", 2023)

"The process of examining the data available in different data sources and collecting statistics and information about this data. Data profiling helps to assess the quality level of the data according to a defined goal." (Talend)

"Data profiling, a critical first step in data migration, automates the identification of problematic data and metadata and enables companies to correct inconsistencies, redundancies and inaccuracies in corporate databases." (Information Management)

"Data profiling is the act of examining, cleansing and analyzing an existing data source to generate actionable summaries. Proper techniques of data profiling verify the accuracy and validity of data, leading to better data-driven decision making that customers can use to their advantage." (snowflake) [source]

🔬Data Science: Pig (Definitions)

"A programming interface for programmers to create MapReduce jobs within Hadoop." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"A programming language designed to handle any type of data. Pig helps users to focus more on analyzing large datasets and less time writing map programs and reduce programs. Like Hive and Impala, Pig is a high-level platform used for creating MapReduce programs more easily. The programming language Pig uses is called Pig Latin, and it allows you to extract, transform, and load (ETL) data at a very high level. This greatly reduces the effort if this was written in JAVA code; PIG is only a fraction of that." (Benoy Antony et al, "Professional Hadoop®", 2016)

"An open-source platform for analyzing large data sets that consists of the following: (1) Pig Latin scripting language; (2) Pig interpreter that converts Pig Latin scripts into MapReduce jobs. Pig runs as a client application." (Oracle)