TUSQ: Targeted High-Utility Sequence Querying

TL;DR

This paper introduces TUSQ, a novel algorithm for targeted high-utility sequence querying that incorporates utility measurement into sequence pattern discovery, outperforming existing methods in efficiency and effectiveness.

Contribution

The paper proposes TUSQ, a new algorithm that integrates utility into sequence querying, with innovative upper bounds and data structures for improved performance.

Findings

TUSQ outperforms baseline algorithms in runtime.

TUSQ uses novel upper bounds for efficient pruning.

TUSQ reduces memory consumption during querying.

Abstract

Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds suffix remain utility and terminated descendants utility as well as a vertical Last Instance Table structure. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.