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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2017

30.07.2016 | Regular Paper

Sequential pattern mining in databases with temporal uncertainty

verfasst von: Jiaqi Ge, Yuni Xia, Jian Wang, Chandima Hewa Nadungodage, Sunil Prabhakar

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2017

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Abstract

Temporally uncertain data widely exist in many real-world applications. Temporal uncertainty can be caused by various reasons such as conflicting or missing event timestamps, network latency, granularity mismatch, synchronization problems, device precision limitations, data aggregation. In this paper, we propose an efficient algorithm to mine sequential patterns from data with temporal uncertainty. We propose an uncertain model in which timestamps are modeled by random variables and then design a new approach to manage temporal uncertainty. We integrate it into the pattern-growth sequential pattern mining algorithm to discover probabilistic frequent sequential patterns. Extensive experiments on both synthetic and real datasets prove that the proposed algorithm is both efficient and scalable.
Vorheriger Artikel Minimizing conservativity violations in ontology alignments: algorithms and evaluation
Nächster Artikel A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet

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Metadaten
Titel
Sequential pattern mining in databases with temporal uncertainty
verfasst von
Jiaqi Ge
Yuni Xia
Jian Wang
Chandima Hewa Nadungodage
Sunil Prabhakar
Publikationsdatum
30.07.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-0977-1

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