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    Integration of a Data Mining System with a Database or Data Warehouse System

    DB andDW systems, possible integration schemes include no coupling, loose coupling, semitight coupling, and tight coupling. We examine each of these s

    Chapter: Data Warehousing and Data Mining

    Chapter: Data Warehousing and Data Mining Integration of a Data Mining System with a Database or Data Warehouse System

    DB andDW systems, possible integration schemes include no coupling, loose coupling, semitight coupling, and tight coupling. We examine each of these schemes, as follows:

    Integration Of A Data Mining System With A Database Or Data Warehouse System

    DB andDW systems, possible integration schemes include , , and. We examine each of these schemes, as follows:

    1.No coupling: means that a DM system will not utilize any function of a DB or DWsystem. It may fetch data from a particular source (such as a file system), process data using some data mining algorithms, and then store the mining results in another file.2.Loose coupling: means that a DM system will use some facilities of a DB or DWsystem, fetching data from a data repository managed by these systems, performing data mining, and then storing the mining results either in a file or in a designated place in a database or data Warehouse. Loose coupling is better than no coupling because it can fetch any portion of data stored in databases or data warehouses by using query processing, indexing, and other system facilities.

    However, many loosely coupled mining systems are main memory-based. Because mining does not explore data structures and query optimization methods provided by DB or DW systems, it is difficult for loose coupling to achieve high scalability and good performance with large data sets.

    3.Semitight coupling: means that besides linking a DM system to a DB/DWsystem, efficient implementations of a few essential data mining primitives (identified by the analysis of frequently encountered data mining functions) can be provided in the DB/DW system. These primitives can include sorting, indexing, aggregation, histogram analysis, multi way join, and precomputation of some essential statistical measures, such as sum, count, max, min ,standard deviation,4.Tight coupling: means that a DM system is smoothly integrated into the DB/DWsystem. The data mining subsystem is treated as one functional component of information system. Data mining queries and functions are optimized based on mining query analysis, data structures, indexing schemes, and query processing methods of a DB or DW system.

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    Data Warehousing and Data Mining : Integration of a Data Mining System with a Database or Data Warehouse System |

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    What is the integration of a data mining system with a database system?

    What is the integration of a data mining system with a database system? - The data mining system is integrated with a database or data warehouse system so that ...

    What is the integration of a data mining system with a database system?

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    The data mining system is integrated with a database or data warehouse system so that it can do its tasks in an effective presence. A data mining system operates in an environment that needed it to communicate with other data systems like a database system. There are the possible integration schemes that can integrate these systems which are as follows −

    No coupling − No coupling defines that a data mining system will not use any function of a database or data warehouse system. It can retrieve data from a specific source (including a file system), process data using some data mining algorithms, and therefore save the mining results in a different file.

    Such a system, though simple, deteriorates from various limitations. First, a Database system offers a big deal of flexibility and adaptability at storing, organizing, accessing, and processing data. Without using a Database/Data warehouse system, a Data mining system can allocate a large amount of time finding, collecting, cleaning, and changing data.

    Loose Coupling − In this data mining system uses some services of a database or data warehouse system. The data is fetched from a data repository handled by these systems. Data mining approaches are used to process the data and then the processed data is saved either in a file or in a designated area in a database or data warehouse. Loose coupling is better than no coupling as it can fetch some area of data stored in databases by using query processing or various system facilities.Semitight Coupling − In this adequate execution of a few essential data mining primitives can be supported in the database/datawarehouse system. These primitives can contain sorting, indexing, aggregation, histogram analysis, multi-way join, and pre-computation of some important statistical measures, including sum, count, max, min, standard deviation, etc.Tight coupling − Tight coupling defines that a data mining system is smoothly integrated into the database/data warehouse system. The data mining subsystem is considered as one functional element of an information system.

    Data mining queries and functions are developed and established on mining query analysis, data structures, indexing schemes, and query processing methods of database/data warehouse systems. It is hugely desirable because it supports the effective implementation of data mining functions, high system performance, and an integrated data processing environment.

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    Data Integration in Data Mining

    A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

    Data Integration in Data Mining

    Difficulty Level : Basic

    Last Updated : 25 Dec, 2022

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    Data Integration is a data preprocessing technique that combines data from multiple heterogeneous data sources into a coherent data store and provides a unified view of the data. These sources may include multiple data cubes, databases, or flat files.The data integration approaches are formally defined as triple where,

    G stand for the global schema,

    S stands for the heterogeneous source of schema,

    M stands for mapping between the queries of source and global schema.

    There are mainly 2 major approaches for data integration – one is the “tight coupling approach” and another is the “loose coupling approach”.

    Tight Coupling:

    Here, a data warehouse is treated as an information retrieval component.

    In this coupling, data is combined from different sources into a single physical location through the process of ETL – Extraction, Transformation, and Loading.

    Loose Coupling:

    Here, an interface is provided that takes the query from the user, transforms it in a way the source database can understand, and then sends the query directly to the source databases to obtain the result.

    And the data only remains in the actual source databases.

    Issues in Data Integration:

    There are three issues to consider during data integration: Schema Integration, Redundancy Detection, and resolution of data value conflicts. These are explained in brief below.

    1. Schema Integration:

    Integrate metadata from different sources.

    The real-world entities from multiple sources are referred to as the entity identification problem.

    2. Redundancy Detection:

    An attribute may be redundant if it can be derived or obtained from another attribute or set of attributes.

    Inconsistencies in attributes can also cause redundancies in the resulting data set.

    Some redundancies can be detected by correlation analysis.

    3. Resolution of data value conflicts:

    This is the third critical issue in data integration.

    Attribute values from different sources may differ for the same real-world entity.

    An attribute in one system may be recorded at a lower level of abstraction than the “same” attribute in another.

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