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MC-Tree: Dynamic index Structure for Partially Clustered multi-dimensional Database

Tsinghua Science and Technology 2003年第2期8卷 174-180页

作者：靳晓明王丽坤陆玉昌石纯一 State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China

index structure that enables efficient similarity queries in high-dimensional space is crucial for many applications. This paper discusses the indexing problem in dataset composed of partially clustered data, which exists in many applications. Current index methods are inefficient with partially clustered datasets. The dynamic and adaptive index structure presented here, called a multi-cluster tree (MC-tree), consists of a set of height-balanced trees for indexing. This index structure improves the querying efficiency in three ways: 1) Most bounding regions achieve uniform distributions, which results in fewer splits and less overlap compared with a single indexing tree. 2) The clusters in the dataset are dynamically detected when the index is updated. 3) The query process does not involve a sequential scan. The MC-tree was shown to be better than hierarchical and cluster-based indexes for the partially clustered datasets.

关键词： MC-tree multi-dimensional index similarity query partially clustered dataset

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Handling query skew in large indexes： a view based approach

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Frontiers of Computer Science 2018年第1期12卷 146-162页

作者： Weihuang HUANG Jeffrey Xu YU Zechao SHANG Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong China

indexing is one of the most important tech- niques 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 par- tially 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 bal- anced tree-structured index in particular when datasets be- come 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 well- balanced 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 man- ner. 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.

关键词： multi-dimensional index query adaptive index-view

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