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Efficient Longest Streak Discovery in Multidimensional Sequence Data

  • Wentao Wang
  • Bo Tang
  • Min ZhuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)

Abstract

This paper studies the problem of discovering longest streak in multidimensional sequence dataset. Given a multidimensional sequence dataset, the contextual longest streak is the longest consecutive tuples in a context subspace which match with a specific measure constraint. It has various applications in social network analysis, computational journalism, etc. The challenges of the longest streak discovery problem are (i) huge search space, and (ii) non-monotonicity property of streak lengths. In this paper, we propose a novel computation framework with a suite of optimization techniques for it. Our solutions outperform the baseline solution by two orders of magnitude in both real and synthetic datasets. In addition, we validate the effectiveness of our proposal by a real-world case study.

Keywords

Computational journalism Multidimensional sequence data 

Notes

Acknowledgement

This work was supported by the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina
  2. 2.College of Computer ScienceSichuan UniversityChengduChina

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