💎SQL Reloaded: Misusing Views and Pseudo-Constants

Views as virtual tables can be misused to replace tables in certain circumstances, either by storing values within one or multiple rows, like in the below examples:

-- parameters for a BI solution
CREATE VIEW dbo.vLoV_Parameters
AS
SELECT Cast('ABC' as nvarchar(20)) AS DataAreaId
 , Cast(GetDate() as Date) AS CurrentDate 
 , Cast(100 as int) AS BatchCount 

GO

SELECT *
FROM dbo.vLoV_Parameters

GO

-- values for a dropdown 
CREATE VIEW dbo.vLoV_DataAreas
AS
SELECT Cast('ABC' as nvarchar(20)) AS DataAreaId
, Cast('Company ABC' as nvarchar(50)) AS Description 
UNION ALL
SELECT 'XYZ' DataAreaId 
, 'Company XYZ'

GO

SELECT *
FROM dbo.vLoV_DataAreas

GO

These solutions aren’t elegant, and typically not recommended because they go against one of the principles of good database design, namely “data belong in tables”, though they do the trick when needed. Personally, I used them only in a handful of cases, e.g. when it wasn’t allowed to create tables, when it was needed testing something for a short period of time, or when there was some overhead of creating a table for 2-3 values. Because of their scarce use, I haven’t given them too much thought, not until I discovered Jared Ko’s blog posting on pseudo-constants. He considers the values from the first view as pseudo-constants, and advocates for their use especially for easier dependency tracking, easier code refactoring, avoiding implicit data conversion and easier maintenance of values.

All these are good reasons to consider them, therefore I tried to take further the idea to see if it survives a reality check. For this I took Dynamics AX as testing environment, as it makes extensive use of enumerations (aka base enums) to store list of values needed allover through the application. Behind each table there are one or more enumerations, the tables storing master data abounding of them.  For exemplification let’s consider InventTrans, table that stores the inventory transactions, the logic that governs the receipt and issued transactions are governed by three enumerations: StatusIssue, StatusReceipt and Direction.

-- Status Issue Enumeration 
 CREATE VIEW dbo.vLoV_StatusIssue
 AS
 SELECT cast(0 as int) AS None
 , cast(1 as int) AS Sold
 , cast(2 as int) AS Deducted
 , cast(3 as int) AS Picked
 , cast(4 as int) AS ReservPhysical
 , cast(5 as int) AS ReservOrdered
 , cast(6 as int) AS OnOrder
 , cast(7 as int) AS QuotationIssue

GO

-- Status Receipt Enumeration 
 CREATE VIEW dbo.vLoV_StatusReceipt
 AS
SELECT cast(0 as int) AS None
 , cast(1 as int) AS Purchased
 , cast(2 as int) AS Received
 , cast(3 as int) AS Registered
 , cast(4 as int) AS Arrived
 , cast(5 as int) AS Ordered
 , cast(6 as int) AS QuotationReceipt

GO

-- Inventory Direction Enumeration 
 CREATE VIEW dbo.vLoV_InventDirection
 AS
 SELECT cast(0 as int) AS None
 , cast(1 as int) AS Receipt
 , cast(2 as int) AS Issue

To see these views at work let’s construct the InventTrans table on the fly:

-- creating an ad-hoc table  
SELECT *
INTO  dbo.InventTrans
FROM (VALUES (1, 1, 0, 2, -1, 'A0001')
, (2, 1, 0, 2, -10, 'A0002')
, (3, 2, 0, 2, -6, 'A0001')
, (4, 2, 0, 2, -3, 'A0002')
, (5, 3, 0, 2, -2, 'A0001')
, (6, 1, 0, 1, 1, 'A0001')
, (7, 0, 1, 1, 50, 'A0001')
, (8, 0, 2, 1, 100, 'A0002')
, (9, 0, 3, 1, 30, 'A0003')
, (10, 0, 3, 1, 20, 'A0004')
, (11, 0, 1, 2, 10, 'A0001')
) A(TransId, StatusIssue, StatusReceipt, Direction, Qty, ItemId)

 Here are two sets of examples using literals vs. pseudo-constants:

--example issued with literals 
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
WHERE ITR.StatusIssue = 1 
  AND ITR.Direction = 2

GO
--example issued with pseudo-constants
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
      JOIN dbo.vLoV_StatusIssue SI
        ON ITR.StatusIssue = SI.Sold
      JOIN dbo.vLoV_InventDirection ID
        ON ITR.Direction = ID.Issue

GO

--example receipt with literals 
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
WHERE ITR.StatusReceipt= 1
   AND ITR.Direction = 1

GO

--example receipt with pseudo-constants
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
      JOIN dbo.vLoV_StatusReceipt SR
        ON ITR.StatusReceipt= SR.Purchased
      JOIN dbo.vLoV_InventDirection ID
        ON ITR.Direction = ID.Receipt

As can be seen the queries using pseudo-constants make the code somehow readable, though the gain is only relative, each enumeration implying an additional join. In addition, when further business tables are added to the logic (e.g. items, purchases or sales orders)  it complicates the logic, making it more difficult to separate the essential from nonessential. Imagine a translation of the following query:

-- complex query 
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
              <several tables here>
WHERE ((ITR.StatusReceipt<=3 AND ITR.Direction = 1)
  OR (ITR.StatusIssue<=3 AND ITR.Direction = 2))
  AND (<more constraints here>)

The more difficult the constraints in the WHERE clause, the more improbable is a translation of the literals into pseudo-constraints. Considering that an average query contains 5-10 tables, each of them with 1-3 enumerations, the queries would become impracticable by using pseudo-constants and quite difficult to troubleshoot their execution plans.

The more I’m thinking about, an enumeration data type as global variable in SQL Server (like the ones available in VB) would be more than welcome, especially because values are used over and over again through the queries. Imagine, for example, the possibility of writing code as follows:

-- hypothetical query
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
WHERE ITR.StatusReceipt = @@StatusReceipt .Purchased
  AND ITR.Direction = @@InventDirection.Receipt

From my point of view this would make the code more readable and easier to maintain. Instead, in order to make the code more readable, one’s usually forced to add some comments in the code. This works as well, though the code can become full of comments.

-- query with commented literals
SELECT top 100 ITR.*
FROM dbo.InventTrans ITR
WHERE ITR.StatusReceipt <=3 -- Purchased, Received, Registered 
   AND ITR.Direction = 1-- Receip

In conclusion, pseudo-constants’ usefulness is only limited, and their usage is  against developers’ common sense, however a data type in SQL Server with similar functionality would make code more readable and easier to maintain.

Notes:
1) It is possible to simulate an enumeration data type in tables’ definition by using a CHECK constraint.
2) The queries work also in SQL databases in Microsoft Fabric (see file in GitHub repository). You might want to use another schema (e.g. Test), not to interfere with the existing code. 

Happy coding!

🔬Data Science: Data Exploration (Definitions)

Data exploration: "The process of examining data in order to determine ranges and patterns within the data." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

Data Exploration: "The part of the data science process where a scientist will ask basic questions that helps her understand the context of a data set. What you learn during the exploration phase will guide more in-depth analysis later. Further, it helps you recognize when a result might be surprising and warrant further investigation." (KDnuggets)

"Data exploration is the first step of data analysis used to explore and visualize data to uncover insights from the start or identify areas or patterns to dig into more." (Tibco) [source]

"Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest. This process isn’t meant to reveal every bit of information a dataset holds, but rather to help create a broad picture of important trends and major points to study in greater detail." (Sisense) [source]

"Data exploration is the process through which a data analyst investigates the characteristics of a dataset to better understand the data contained within and to define basic metadata before building a data model. Data exploration helps the analyst choose the most appropriate tool for data processing and analysis, and leverages the innate human ability to recognize patterns in data that may not be captured by analytics tools." (Qlik) [source]

"Data exploration provides a first glance analysis of available data sources. Rather than trying to deliver precise insights such as those that result from data analytics, data exploration focuses on identifying key trends and significant variables." (Xplenty) [source]

🔬Data Science: Data Preparation (Definitions)

Data preparation: "The process which involves checking or logging the data in; checking the data for accuracy; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures. This process includes preparation and assignment of appropriate metadata to describe the product in human readable code/format." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data Preparation describes a range of processing activities that take place in order to transform a source of data into a format, quality and structure suitable for further analysis or processing. It is often referred to as Data Pre-Processing due to the fact it is an activity that organises the data for a follow-on processing stage." (experian) [source]

"Data preparation [also] called data wrangling, it’s everything that is concerned with the process of getting your data in good shape for analysis. It’s a critical part of the machine learning process." (RapidMiner) [source]

"Data preparation is an iterative-agile process for exploring, combining, cleaning and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and BI/analytics." (Gartner)

"Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data." (Talend) [source]

🔬Data Science: Data Augmentation (Definitions)

"1.The process of adding to something to make it more or greater than the original. 2.In logic, a relationship where if X leads to Y, then XZ will lead to YZ." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A technique for improving the performance of a model by enriching the training data, e.g. by generating additional instances of minority classes."  (Vincent Karas & Björn W Schuller, "Deep Learning for Sentiment Analysis: An Overview and Perspectives", 2021)

🔬Data Science: Feature Extraction (Definitions)

"A technique that attempts to combine or transform predictors to make clear the information contained within them. Feature extraction methods include factor analysis, principal components analysis, correspondence analysis, multidimensional scaling, partial least square methods, and singular value decomposition." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Extracting or deriving some useful information from the initially obtained data." (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)

"A process of finding features of words and map them to vector space." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"A digital signal processing algorithm, which extracts distinctive values from the input signal." (Andrej Zgank et al, "Embodied Conversation: A Personalized Conversational HCI Interface for Ambient Intelligence", 2021)

"Feature extraction is a procedure in dimensionality reduction of extracting principal variables (features) from some random variables under consideration, usually achieved by extracting one principal variable (feature) as mapping from multiple random variables." (Usama A Khan & Josephine M Namayanja, "Reevaluating Factor Models: Feature Extraction of the Factor Zoo", 2021)

🔬Data Science: Feature Selection (Definitions)

"A method by which to decide on which features (columns) to keep in the analysis that will be done by the data mining algorithms. One of the first things to be done in a data mining project; this uncovers the most important variables among the set of predictor variables. Many of the predictor variables in a data set may not really be important for making an accurate predictive model, and only dilute/reduce the accuracy score of the model if included." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"The process a cybersecurity engineer uses to choose the features in which a given attack may manifest." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"Feature selection is the process of selecting important principal variables (features) from some random variables under consideration, usually achieved by selecting a principal variable (feature) as one of the random variables." (Usama A Khan & Josephine M Namayanja, "Reevaluating Factor Models: Feature Extraction of the Factor Zoo", 2021)

"It is used to select appropriate features from the available data for improving efficiency of machine learning algorithms." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

🔬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]