Efficient evaluation of parameterized pattern queries

Abstract

Many applications rely on sequence databases and use extensively pattern-matching queries to retrieve data of interest. This paper extends the traditional pattern-matching expressions to parameterized patterns, featuring variables. Parameterized patterns are more expressive and allow to define concisely regular expressions that would be very complex to describe without variables. They can also be used to express additional constraints on patterns' variables.We show that they can be evaluated without additional cost with respect to traditional techniques (e.g., the KnuthMorris-Pratt algorithm). We describe an algorithm that enjoys low memory and CPU time requirements, and provide experimental results which illustrate the gain of the optimized solution.

Querying Spatio-temporal Patterns in Mobile Phone-Call Databases

“…Topological, variables and more complex patterns can be specified and evaluated in an efficient way, while in those previous this is not possible. For moving object data, patterns have been examined in the context of query language and modeling issues [13], [14] as well as query evaluation algorithms [15], [16].…”

“…Our work differs in that we provide a more general and powerful query framework where queries can involve both fixed and variable regions as well as variables, negations, topological operators, temporal predicates, etc, and explicit ordering of the predicates along the temporal axis. In [16], a KMP-based algorithm [24] is used to process patterns in trajectorial achieves. This work, however, focuses only on range spatial predicates and cannot handle explicit and implicit temporal ordering of the predicates.…”

Querying trajectories using flexible patterns

“…For moving object data, patterns have been examined in the context of query language and modeling issues [6][16] [20] as well as query evaluation algorithms [9] [5]. In [9] we examined incremental ranking algorithms in the case of simple spatiotemporal pattern queries.…”

“…Trajectories differ since they have both spatial and temporal behavior. In spatiotemporal databases patterns have been examined in [5][6][9] [20]; as detailed in the related work section, these approaches either concentrate on language/modeling related issues, provide less query support (e.g. no temporal and/or numerical constraints) and have less efficient/general evaluation methods.…”

“…To summarize, the contributions of this paper are the following: (1) we define a simple yet powerful framework using a query language based on regular expressions; (2) we allow patterns to contain variables over the query space regions; (3) using lightweight index structures that can be easily implemented in most commercial DBMS nowadays, we propose two efficient evaluation algorithms; (4) finally, we present an extensive experimental evaluation of the proposed techniques against two other methods that we extended and implemented in our framework: a NFA-based method [2] and a KMP -based approach [5]. It should be noted that none of the (original) previous approaches can evaluate our proposed pattern query language.…”