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Online Analytical Processing and Data-Cube Technologies

Online Analytical Processing and Data-Cube Technologies

Lixin Fu, Wen-Chen Hu
Copyright: © 2008 |Pages: 9
ISBN13: 9781599048574|ISBN10: 1599048574|EISBN13: 9781599048581
DOI: 10.4018/978-1-59904-857-4.ch057
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MLA

Fu, Lixin, and Wen-Chen Hu. "Online Analytical Processing and Data-Cube Technologies." Handbook of Research on Public Information Technology, edited by G. David Garson and Mehdi Khosrow-Pour, D.B.A., IGI Global, 2008, pp. 627-635. https://doi.org/10.4018/978-1-59904-857-4.ch057

APA

Fu, L. & Hu, W. (2008). Online Analytical Processing and Data-Cube Technologies. In G. Garson & M. Khosrow-Pour, D.B.A. (Eds.), Handbook of Research on Public Information Technology (pp. 627-635). IGI Global. https://doi.org/10.4018/978-1-59904-857-4.ch057

Chicago

Fu, Lixin, and Wen-Chen Hu. "Online Analytical Processing and Data-Cube Technologies." In Handbook of Research on Public Information Technology, edited by G. David Garson and Mehdi Khosrow-Pour, D.B.A., 627-635. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-857-4.ch057

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Abstract

Since the late ’80s and early ’90s, database technologies have evolved to a new level of applications: online analytical processing (OLAP), where executive management can make quick and effective strategic decisions based on knowledge in terms of queries against large amounts of stored data. Some OLAP systems are also regarded as decision support systems (DSSs) or executive information systems (EIS). The traditional, well-established online transactional processing (OLTP) systems such as relational database management systems (RDBMS) mainly deal with mission-critical daily transactions. Typically, there are a large number of short, simple queries such as lookups, insertions, and deletions. The main focus is transaction throughput, consistency, concurrency, and failure recovery issues. OLAP systems, on the other hand, are mainly analytical and informational. OLAP systems are usually closely coupled with data warehouses, which can contain very large data sets that may include historical data as well as data integrated from different departments and geographical locations. So the sizes of data warehouses are usually significantly larger than common OLTP systems. In addition, the workloads of OLAP are quite different from those of traditional transaction systems: The queries are unpredictable and much more complicated. For example, an OLAP query could be, “For each type of car and each manufacturer, list market share change in terms of car sales between the first quarter of 2005 and the first quarter of 2006.” The purpose of these queries is not for the daily operational maintenance of data; instead, it is for deeper knowledge from data used for decision support.

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