Semigeometric Tiling of Event Sequences

  • Andreas Henelius
  • Isak Karlsson
  • Panagiotis Papapetrou
  • Antti Ukkonen
  • Kai Puolamäki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9851)

Abstract

Event sequences are ubiquitous, e.g., in finance, medicine, and social media. Often the same underlying phenomenon, such as television advertisements during Superbowl, is reflected in independent event sequences, like different Twitter users. It is hence of interest to find combinations of temporal segments and subsets of sequences where an event of interest, like a particular hashtag, has an increased occurrence probability. Such patterns allow exploration of the event sequences in terms of their evolving temporal dynamics, and provide more fine-grained insights to the data than what for example straightforward clustering can reveal. We formulate the task of finding such patterns as a novel matrix tiling problem, and propose two algorithms for solving it. Our first algorithm is a greedy set-cover heuristic, while in the second approach we view the problem as time-series segmentation. We apply the algorithms on real and artificial datasets and obtain promising results. The software related to this paper is available at https://github.com/bwrc/semigeom-r.

Keywords

Event sequences Tiling Covering Binary matrices 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Andreas Henelius
    • 1
  • Isak Karlsson
    • 2
  • Panagiotis Papapetrou
    • 2
  • Antti Ukkonen
    • 1
  • Kai Puolamäki
    • 1
  1. 1.Finnish Institute of Occupational HealthHelsinkiFinland
  2. 2.Department of Computer and Systems SciencesStockholm UniversityKistaSweden

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