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DBMS and Data Warehouses
(1) in this writing assignment, you will create a brochure advertising your services as a data repository. Describe the features of a data mart. Differentiate between unsupervised and supervised data mining.
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Your Data Is Critical to Your Growth!
Three reasons why your business needs to collect and analyze data:
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The functions of a data warehouse include integrating the diverse and often disparate legacy database and transactional systems of an enterprise into a single, unified system of record (Benander, Benander, Fadlalla, Gregory, 2000). Data warehouses are also require an intensive amount of integration platform support. Of the many integration technologies now being adopted to unify data warehouses, XML is increasingly being adopted as a standard (Van, 2002). Data warehouses are often designed to support multiple ontologies of data structures essential for the running of a business as well, which forces a logical file and data structure throughout the entire entity (Selma, Ilyes, Ladjel, Eric, Stephane, Michael, 2012). A second function of a data warehouse is to segment out the data that is used more frequently for queries from the actual data warehouse which is used for reporting and analysis. This second aspect of a data warehouse ensures higher levels of performance on more frequent queries.
A data mart is often a smaller subset of a data warehouse or other data platform, often designed to support the needs of a given business strategy or objective (Benander, Benander, Fadlalla, Gregory, 2000). The term data mart can apply equally to evaluational data, operational data, metadata, or spatial data. Data marts are designed to align with the strategic information requirements of a given business unit or functional area of an organization (Benander, Benander, Fadlalla, Gregory, 2000). Examples of this include specific data marts dedicated to accounting, finance, marketing, sales and service. A data mart is designed often as a one-dimensional model or star schema composed as a factor table or multi-dimensional table (Benander, Benander, Fadlalla, Gregory,…
References
(Benander, Benander, Fadlalla, Gregory, 2000)
Benander, A., Benander, B., Fadlalla, A., & Gregory, J. (2000). Data warehouse administration and management. Information Systems Management, 17(1), 71-80.
Choudhary, A.K., Harding, J.A., & Tiwari, M.K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501-521.
He, Z., Lee, B.S., & Snapp, R. (2005). Self-tuning cost modeling of user-defined functions in an object-relational DBMS. ACM Transactions on Database Systems, 30(3), 812-812.
The data load for CRM systems is less significant than those of Accounting and Financial systems and applications. Those specific fields captured by customers are often compressed into measures of activity as well, and as a result the data load is less significant than in the areas of Accounting and Financial Systems and Reservation and Booking Systems. Even when customized reports are created based on CRM data; the data
Since poor data quality within a system often results in poor business decisions being made from this data, it is very important that each administrator or system architect look at each customer or end-user differently, in their own unique light. Since each end-user is different, and the needs of the customer often stem from the warehouse's ability to accurately store quality information, a system that dates back to the 1970's
Enterprise DBMS Implementing an Enterprise Database Management System Enterprise Database Management Systems (DBMS) are the catalyst and foundation of many of the enterprise software applications, platforms and enterprise systems in place today. Having progressed from being a relational database in the past to often being used as the foundation for complex process workflow and transaction systems (Casati, Castano, Fugini, 2001) the DBMS architecture has taken on a central role in enterprise
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Each of the databases exports a view of its tables or objects that conforms to the shared data model, so that queries can be expressed using a common set of names for properties and relationships regardless of the database. The queries are then translated so that they are actually run against the local data using local names in the local query language; in the reverse direction results may be
Database Differentiation Description of the data models A detailed description of all the tables that a database consists of is referred to as a data dictionary. A data dictionary will contain all the attribute characteristics and names for every table found within a database system. A data dictionary can also be referred to as a metadata repository. This is because it contains information like relationships with other data, meaning, usage, format, and