Abstract
Indexing is one of the most important techniques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is partially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a balanced tree-structured index in particular when datasets become larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a wellbalanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up manner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.
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This work was supported by grant of the Research Grants Council of the Hong Kong SAR, China (14209314).
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Weihuang Huang received her BS and MS degrees from Tsinghua University, China in 2010 and 2013. She is currently a PhD student at the Department of Systems Engineering and Engineering Management in The Chinese University of Hong Kong, China. Her main research interest is indexing.
Jeffrey Xu Yu has held teaching positions at the Institute of Information Sciences and Electronics, University of Tsukuba, Japan, and at the Department of Computer Science, Australian National University, Australia. Currently, he is a professor in the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong, China.
Zechao Shang received his PhD degree from The Chinese University of Hong Kong (CUHK), China in 2015. He is currently a postdoctoral fellow at the Department of Systems Engineering and Engineering Management, CUHK. His main research interest is large scale graph data processing system.
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Huang, W., Yu, J.X. & Shang, Z. Handling query skew in large indexes: a view based approach. Front. Comput. Sci. 12, 146–162 (2018). https://doi.org/10.1007/s11704-016-5525-3
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DOI: https://doi.org/10.1007/s11704-016-5525-3