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Supporting ranking pattern-based aggregate queries in sequence data cubes
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Many kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. In recent years, the concept of Sequence OLAP (S-OLAP) has been proposed. The biggest distinguishing feature of SOLAP from traditional OLAP is that data sequences managed by an S-OLAP system are characterized by the subsequence/substring patterns they possess. An S-OLAP system thus supports pattern-based grouping and aggregation. Conceptually, an S-OLAP system maintains a sequence data cube which is composed of sequence cuboids. Each sequence cuboid presents the answer of a pattern-based aggregate (PBA) query. This paper focuses on the I/O aspects of evaluating PBA queries. We study the problems of joining plan selection and execution planning, which are the core issues in the design of I/O-efficient cuboid materialization algorithms. Through an empirical study, we show that our algorithms lead to a very I/O-efficient strategy for sequence cuboid materialization.