Shinichiro Tago

Mining frequent partite episodes with partwise constraints

In this paper, we study the problem of efficiently mining frequent partite episodes that satisfy partwise constraints from an input event sequence. Through our constraints, we can extract episodes related to events and their precedent-subsequent relations, on which we focus, in a short time. This improves the efficiency of data mining using trial and error processes. A partite episode of length k is of the form P=〈P1,...,Pk〉 for sets Pi(1≤i≤k) of events. We call Pi a part of P for every 1≤i≤k. We introduce the partwise constraints for partite episodes P, which consists of shape and pattern constraints. A shape constraint specifies the size of each part of P and the length of P. A pattern constraint specifies subsets of each part of P. We then present a backtracking algorithm that finds all of the frequent partite episodes satisfying a partwise constraint from an input event sequence. By theoretical analysis, we show that the algorithm runs in output polynomial time and polynomial space for the total input size. In the experiment, we show that our proposed algorithm is much faster than existing algorithms for mining partite episodes on an artificial and a real-world datasets.

Chimera: stream-oriented XML filtering/querying engine

In this paper, we study the problem of filtering and querying massive XML data against a large set of XPath patterns in Univariate XPath. Based on an efficient matching engine XSIGMA for linear XPath patterns with Boolean expression over keywords and a twig evaluator over event streams, we propose an XPath filtering/querying engine Chimera, which runs fast and stably for any XPath patterns without heavy pre- processing techniques for queried data often used by existing native XMLDBs and RDBs. Chimera also runs much faster than those engines against thousands of XPath patterns. We implemented Chimera and showed its effectiveness by several experiments on artificial and real datasets.

EVIS: a fast and scalable episode matching engine for massively parallel data streams

We propose a fast episode pattern matching engine EVIS that detects all occurrences in massively parallel data streams for an episode pattern, which represents a collection of event types in a given partial order. There should be important applications to be addressed with this technology, such as monitoring stock price movements, and tracking vehicles or merchandise by using GPS or RFID sensors. EVIS employs a variant of non-deterministic finite automata whose states are extended to maintain their activated times and activating streams. This extension allows EVISs episode pattern to have 1) interval constraints that enforce time-bound conditions on every pair of consequent event types in the pattern, and 2) stream constraints by which two interested series of events are associated with each other and found in arbitrary pairs of streams. The experimental results show that EVIS performs much faster than a popular CEP engine for both artificial and real world datasets, as well as that EVIS effectively works for over 100,000 streams.