Knowledge discovery
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Information retrieval
Information retrieval is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text (or other content-based) indexing. Automated information retrieval systems are used to reduce what has been called " information overload ". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications . [ edit ] HistoryData mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), [ 1 ] an interdisciplinary subfield of computer science , [ 2 ] [ 3 ] [ 4 ] is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence , machine learning , statistics , and database systems . [ 2 ] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. [ 2 ] Aside from the raw analysis step, it involves database and data management aspects, data preprocessing , model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization , and online updating . [ 2 ]
Data mining
Data Warehouse Overview In computing , a data warehouse or enterprise data warehouse ( DW , DWH , or EDW ) is a database used for reporting and data analysis . It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right).
Data warehouse
Knowledge extraction
Knowledge Extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to Information Extraction ( NLP ) and ETL (Data Warehouse), the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. The RDB2RDF W3C group [ 1 ] is currently standardizing a language for extraction of RDF from relational databases.
Knowledge Retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology , cognitive neuroscience , logic and inference , machine learning and knowledge discovery , linguistics , and information technology . [ edit ] Overview
