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FB+-tree for Big Data Management
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DOI：10.1016/j.bdr.2015.11.003.png)
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Abstract
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Decades of research and experiences on managing large databases and current world's strong interests in massive data information conveyed many indexing methods to a new extent. From extensive experiments, FB+-tree has displayed its excellent potential for big data in-memory management. FB+-tree is an idea that builds fast indexing structure using multi-level key ranges, which is explained based on exploiting the B+-tree in this article. With FB+-tree, point searches and range searches are helped by early termination of searches for non-existent data. Range searches can be processed depth-first or breath-first. One group of multiple searches can be processed with one pass on the indexing structure to minimize total cost. Implementation options and strategies are explained to show the flexibility of this technology for easy adaption and high efficiency. FB+-tree can be tuned to speed up queries directed at popular ranges of index or index ranges of particular interest to the user. Extended experiments are presented particularly for testing its adaptability and performance for big data. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Indexing
Access method
Query performance
In-memory indexing
Big data
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