NDLI: An Effective Heuristic for Multi-dimensional Partitioning in Bottom-Up Computation for Data Cubes

Content Provider	IEEE Xplore Digital Library
Author	Teng-Sheng Moh Yeung, K.
Copyright Year	2009
Abstract	Bottom-Up Computation (BUC) is one of the most studied algorithms for data cube generation in on-line analytical processing. Its computation in the bottom upstyle allows the algorithm to efficiently generate a data cube for input data that can fit into the memory.When the entire input data cannot fit into the memory,many sources in literature suggest partitioning the data by a dimension and then running the algorithm on each of the single-dimensional partitioned data to generate a data cube. For very large sized input data,the partitioned data might still not be able to fit into the memory and partitioning by additional dimensions is required. However, this multi-dimensional partitioning is more complicated than single dimensional partitioning and it has not been fully discussed before. Our goal is to provide an effective heuristic implementation on multi-dimensional partitioning in BUC.
Starting Page	159
Ending Page	163
File Size	562133
Page Count	5
File Format	PDF
ISBN	9780769535388
DOI	10.1109/ICC.2009.61
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Lattices Data warehouses Data engineering Data structures Partitioning algorithms Information analysis Computer science Aggregates Data Cube Algorithm Warehousing Heuristic Algorithm Bottom-Up Computation
Content Type	Text
Resource Type	Article

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