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CLTR: Collectively Linking Temporal Records Across Heterogeneous Sources

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Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10178))

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Abstract

A huge volume of data on the Web are continually made available, which provides users rich amount of information to learn more about entities. In addition to attribute values of entities, there is often additional relational information, such as friendship on social networks, coauthorship of papers. However, to understand how these facts across heterogeneous data sources are related is challenging for users due to entity evolution over time. In this paper, we propose a novel system to help users find how records are temporally related and understand how entity profiles evolve over time. Our system is able to Collectively Link Temporal Records (CLTR) by taking advantage of evidence from both attribute and relational information on multiple sources. We demonstrate how CLTR allows users to explore time-varying history of targeted entities and visualizes multi-type relations among entities.

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References

  1. Li, P., Dong, X.L., Maurino, A., Srivastava, D.: Linking temporal records. Proc. VLDB Endow. 4(11), 956–967 (2011)

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Author information

Authors and Affiliations

  1. Singapore University of Technology and Design, Singapore, Singapore

    Yanyan Zou & Kasun S. Perera

Authors
  1. Yanyan Zou
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  2. Kasun S. Perera
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Corresponding author

Correspondence to Yanyan Zou .

Editor information

Editors and Affiliations

  1. Arizona State University, Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. of Science and Technology, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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© 2017 Springer International Publishing AG

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Cite this paper

Zou, Y., Perera, K.S. (2017). CLTR: Collectively Linking Temporal Records Across Heterogeneous Sources. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_43

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  • DOI: https://doi.org/10.1007/978-3-319-55699-4_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55698-7

  • Online ISBN: 978-3-319-55699-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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