Processing Sequential Patterns in Relational Databases

  • Xuequn Shang
  • Kai-Uwe Sattler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4380)


Integrating data mining techniques into database systems has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementations. Reasons for this are among others the prohibitive nature of the cost associated with extracting knowledge as well as the lack of suitable declarative query language support. Recent studies have found that for association rule mining and sequential pattern mining with carefully tuned SQL formulations it is possible to achieve performance comparable to systems that cache the data in files outside the DBMS. However, most of the previous pattern mining methods follow the method of Apriori, which still encounters problems when a sequential database is large and/or when sequential patterns to be mined are numerous and long.

In this paper, we present a novel SQL based approach that we recently proposed, called Prospad (PROjection Sequential PAttern Discovery). Prospad fundamentally differs from an Apriori-like candidate set generation-and-test approach. This approach is a pattern growth-based approach without candidate generation. It grows longer patterns from shorter ones by successively projecting the sequential table into subsequential tables. Since a projected table for a sequential pattern i contains all and only necessary information for mining the sequential patterns that can grow from i, the size of the projected table usually reduces quickly as mining proceeds to longer patterns. Moreover, a depth first approach is used to facilitate the projecting process in order to avoid creating and dropping costs of temporary tables.


Sequential Pattern Frequent Pattern Pattern Mining Association Rule Mining Frequent Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Xuequn Shang
    • 1
  • Kai-Uwe Sattler
    • 2
  1. 1.School of Computer Science, Northwestern Polytechnical University, 710072, ShaanxiChina
  2. 2.Department of Computer Science and Automation, Technical University of IlmenauGermany

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