Paper Info
Title
Efficient Pattern-Based Aggregation on Sequence Data
Abstract
A Sequence OLAP(S-OLAP) system provides a platform on which pattern-based aggregate (PBA) queries on a sequence database are evaluated. In its simplest form, a PBA query consists of a pattern template T and an aggregate function F. A pattern template is a sequence of variables, each is defined over a domain. Each variable is instantiated with all possible values in its corresponding domain to derive all possible patterns of the template. Sequences are grouped based on the patterns they possess. The answer to a PBA query is a sequence cuboid (s-cuboid), which is a multidimensional array of cells. Each cell is associated with a pattern instantiated from the query's pattern template. The value of each s-cuboid cell is obtained by applying the aggregate function F to the set of data sequences that belong to that cell. Since a pattern template can involve many variables and can be arbitrarily long, the induced s-cuboid for a PBA query can be huge. For most analytical tasks, however, only iceberg cells with very large aggregate values are of interest. This paper proposes an efficient approach to identifying and evaluating iceberg cells of s-cuboids. Experimental results show that our algorithms are orders of magnitude faster than existing approaches.
Year
DOI
Venue
2017
10.1109/TKDE.2016.2618856
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
Aggregates,Indexes,Algorithm design and analysis,Memory management,Systems architecture,Estimation
Aggregate function,Data mining,Randomized algorithm,Sequence database,Algorithm design,Computer science,Memory management,Cuboid,Systems architecture,Online analytical processing
Journal
Volume
Issue
ISSN
29
2
1041-4347
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Zhian He1492.97
Petrie Wong2162.01
Ben Kao32358194.98
Eric Lo481351.50
Reynold Cheng53069154.13
Ziqiang Feng6587.94