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Formal model of time point-based sequential data for OLAP-like analysis

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Numerous nowadays applications generate huge sets of data, whose natural feature is order, e.g,. sensor installations, RFID devices, workflow systems, Website monitors, health care applications. By analyzing the data and their order dependencies one can acquire new knowledge. However, nowadays commercial BI technologies and research prototypes allow to analyze mostly set oriented data, neglecting their order (sequential) dependencies. Few approaches to analyzing data of sequential nature have been proposed so far and all of them lack a comprehensive data model being able to represent and analyze sequential dependencies. In this paper, we propose a formal model for time point-based sequential data. The main elements of this model include an event and a sequence of events. Measures are associated with events and sequences. Measures are analyzed in the context set up by dimensions in an OLAP-like manner by means of the set of operations. The operations in our model are categorized as: operations on sequences, on dimensions, general operations, and analytical functions.
Słowa kluczowe
Rocznik
Strony
331--340
Opis fizyczny
Bibliogr. 35 poz., tab., rys.
Twórcy
autor
  • Institute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Poznań, Poland
autor
  • Institute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Poznań, Poland
  • Institute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Poznań, Poland
autor
  • Institute of Computing Science, Poznan University of Technology, 3a Piotrowo St., 90-965 Poznań, Poland
Bibliografia
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  • [15] C.K. Chui, B. Kao, E. Lo, and D. Cheung, “S-OLAP: an OLAP system for analyzing sequence data”, Proc. ACM SIGMOD Int. Conf. on Management of Data 1, 1131–1134 (2010).
  • [16] C.K. Chui, E. Lo, B. Kao, and W.-S. Ho, “Supporting ranking pattern-based aggregate queries in sequence data cubes”, Proc. ACM Conf. on Information and Knowledge Management (CIKM) 1, 997–1006 (2009).
  • [17] M. Liu, E. Rundensteiner, K. Greenfield, C. Gupta, S. Wang, I. Ari, and A. Mehta, “E-cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing”, Proc. ACM SIGMOD Int. Conf. on Management of Data 1, 889–900 (2011).
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  • [19] B. Bebel, M. Morzy, T. Morzy, Z. Królikowski, and R. Wrembel, “OLAP-Like analysis of time point-based sequential data”, ER Workshops Lecture Notes in Computer Science 7518, 153–161 (2012).
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  • [30] J.-W. Han, J. Pei, and X.-F. Yan, “From sequential pattern mining to structured pattern mining: a pattern-growth approach”, J. Comput. Sci. Technol. 19 (3), 257–279 (2004).
  • [31] N.R. Mabroukeh and C.I. Ezeife, “A taxonomy of sequential pattern mining algorithms”, ACM Comput. Surv. 43 (1), 3:1–3:41 (2010).
  • [32] F. Masseglia, M. Teisseire, and P. Poncelet, “Sequential pattern mining”, in Encyclopedia of Data Warehousing and Mining, pp. 1800–1805, IGI Global, London, 2009.
  • [33] A.Marascu and F.Masseglia, “Mining sequential patterns from data streams: a centroid approach”, J. Intell. Inf. Syst. 27 (3), 291–307 (2006).
  • [34] L.F. Mendes, B. Ding, and J. Han, “Stream sequential pattern mining with precise error bounds”, Proc. IEEE Int. Conf. on Data Mining (ICDM) 1, 941–946 (2008).
  • [35] Q. Zheng, K. Xu, and S. Ma, “When to update the sequential patterns of stream data?”, Proc. Pacific-Asia Conf. on Advances in Knowledge Discovery and Data Mining (PAKDD) 1, 545–550 (2003).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1a46097a-831b-4d33-a409-7b9bb8a07caf
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