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Big Data Management in the Context of Real-Time Data Warehousing

Big Data Management in the Context of Real-Time Data Warehousing

M. Asif Naeem, Gillian Dobbie, Gerald Weber
Copyright: © 2014 |Pages: 27
ISBN13: 9781466646995|ISBN10: 1466646993|EISBN13: 9781466647008
DOI: 10.4018/978-1-4666-4699-5.ch007
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MLA

Naeem, M. Asif, et al. "Big Data Management in the Context of Real-Time Data Warehousing." Big Data Management, Technologies, and Applications, edited by Wen-Chen Hu and Naima Kaabouch, IGI Global, 2014, pp. 150-176. https://doi.org/10.4018/978-1-4666-4699-5.ch007

APA

Naeem, M. A., Dobbie, G., & Weber, G. (2014). Big Data Management in the Context of Real-Time Data Warehousing. In W. Hu & N. Kaabouch (Eds.), Big Data Management, Technologies, and Applications (pp. 150-176). IGI Global. https://doi.org/10.4018/978-1-4666-4699-5.ch007

Chicago

Naeem, M. Asif, Gillian Dobbie, and Gerald Weber. "Big Data Management in the Context of Real-Time Data Warehousing." In Big Data Management, Technologies, and Applications, edited by Wen-Chen Hu and Naima Kaabouch, 150-176. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4699-5.ch007

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Abstract

In order to make timely and effective decisions, businesses need the latest information from big data warehouse repositories. To keep these repositories up to date, real-time data integration is required. An important phase in real-time data integration is data transformation where a stream of updates, which is huge in volume and infinite, is joined with large disk-based master data. Stream processing is an important concept in Big Data, since large volumes of data are often best processed immediately. A well-known algorithm called Mesh Join (MESHJOIN) was proposed to process stream data with disk-based master data, which uses limited memory. MESHJOIN is a candidate for a resource-aware system setup. The problem that the authors consider in this chapter is that MESHJOIN is not very selective. In particular, the performance of the algorithm is always inversely proportional to the size of the master data table. As a consequence, the resource consumption is in some scenarios suboptimal. They present an algorithm called Cache Join (CACHEJOIN), which performs asymptotically at least as well as MESHJOIN but performs better in realistic scenarios, particularly if parts of the master data are used with different frequencies. In order to quantify the performance differences, the authors compare both algorithms with a synthetic dataset of a known skewed distribution as well as TPC-H and real-life datasets.

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