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Knowledge discovery is a concept of the field of computer science that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It is often described as deriving knowledge from the input data. This complex topic can be categorized according to 1) what kind of data is searched; and 2) in what form is the result of the search represented. Image File history File links Emblem-important. ... Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. ... For other uses, see Data (disambiguation). ... This article needs additional references or sources for verification. ... This article needs additional references or sources for verification. ... For other uses, see Data (disambiguation). ...

The most well-known branch of knowledge discovery is data mining, also known as Knowledge Discovery in Databases (KDD). Just as many other forms of knowledge discovery it creates abstractions of the input data. The knowledge obtained through the this process may become additional data that can be used for further usage and discovery. Kurt Thearling, An Introduction to Data Mining (also available is a corresponding online tutorial) Dean Abbott, I. Philip Matkovsky, and John Elder IV, Ph. ... Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. ... abstraction in general. ...

Another promising application of knowledge discovery is in the area of software modernization which involves understanding existing software artifacts. This process is related to a concept of reverse engineering. Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An entity relationship is a frequent format of representing knowledge obtained from existing software. Object Management Group (OMG) developed specification Knowledge Discovery Metamodel (KDM) which defines an ontology for the software assets and their relationships for the purpose of performing knowledge discovery of existing code. Knowledge discovery from existing software systems, also known as software mining is closely related to data mining, since existing software artifacts contain enormous business value, key for the evolution of software systems. Instead of mining individual data sets, software mining focuses on metadata, such as database schemas. // Software Modernization is the process of understanding and evolving existing software assets. ... Reverse engineering (RE) is the process of taking something (a device, an electrical component, a software program, etc. ... The Entity-relationship model or Entity-relationship diagram (ERD) is a data model or diagram for high-level descriptions of conceptual data models, and it provides a graphical notation for representing such data models in the form of entity-relationship diagrams. ... Object Management Group (OMG) is a consortium, originally aimed at setting standards for distributed object-oriented systems, and is now focused on modeling (programs, systems and business processes) and model-based standards in some 20 vertical markets. ... Knowledge Discovery Metamodel (KDM) is publicly available specification from the Object Management Group (OMG). ... Software mining is a promising application of Knowledge discovery in the area of software modernization which involves understanding existing software artifacts. ... Kurt Thearling, An Introduction to Data Mining (also available is a corresponding online tutorial) Dean Abbott, I. Philip Matkovsky, and John Elder IV, Ph. ... A data set (or dataset) is a collection of data, usually presented in tabular form. ... Software mining is a promising application of Knowledge discovery in the area of software modernization which involves understanding existing software artifacts. ... Metadata is data about data. ...

Input data for knowledge discovery

• Databases
  • Relational data
  • Database
  • Document warehouse
  • Data warehouse
• Software Mining
• Text
  • Concept mining
• Graphs
  • Molecule mining
• Sequences
  • Data stream mining
  • Learning from time-varying data streams under concept drift
• Web

Kurt Thearling, An Introduction to Data Mining (also available is a corresponding online tutorial) Dean Abbott, I. Philip Matkovsky, and John Elder IV, Ph. ... Relational data mining is a data mining technique for relational databases. ... This article is about computing. ... A document warehouse tells you why things have happened, instead of what has happened as in data warehousing. ... A data warehouse is the main repository of an organizations historical data, its corporate memory. ... Software mining is a promising application of Knowledge discovery in the area of software modernization which involves understanding existing software artifacts. ... Text mining, sometimes alternately referred to as text data mining, refers generally to the process of deriving high quality information from text. ... It has been suggested that Taxonomic classification be merged into this article or section. ... This page describes mining for molecules. ... Sequence mining or string mining is a special case of Structured Data Mining. ... Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. ... In predictive analytics, the term concept refers to the quantity you are looking to predict. ... Web mining - is the application of data mining techniques to discover patterns from the Web. ...

Output formats for discovered knowledge

• Data model
• Metadata
• Metamodels
• Ontology
• Knowledge representation
• Business rule
• Knowledge Discovery Metamodel (KDM)
• Business Process Modeling Notation (BPMN)
• Intermediate representation
• Resource Description Framework (RDF)
• Software metrics