The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.

This paper proposes a distributed built-in self-test (BIST) technique and its test design platform for VLSIs. This BIST has lower hardware overhead pattern generators, compressors and controller. The platform cuts down on the number of complicated operations needed for the BIST insertion and evaluation, so the BIST implementation turn-around-time (TAT) is dramatically reduced. Experimental results for the 110 k-gate arithmetic execution blocks of an image-processing LSI show that using this BIST structure and platform enables the entire BIST implementation within five days. The implemented BIST has a 1% hardware overhead and 96% fault coverage. This platform will significantly reduce testing costs for time-to-market and mass-produced LSIs.