Large-scale copy detection

  • Authors:

  • Xin Luna Dong;Divesh Srivastava

  • Affiliations:

  • AT&T Labs-Research, Florham Park, NJ, USA;AT&T Labs-Research, Florham Park, NJ, USA

  • Venue:
  • Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2011

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Abstract

The Web has enabled the availability of a vast amount of useful information in recent years. However, the web technologies that have enabled sources to share their information have also made it easy for sources to copy from each other and often publish without proper attribution. Understanding the copying relationships between sources has many benefits, including helping data providers protect their own rights, improving various aspects of data integration, and facilitating in-depth analysis of information flow. The importance of copy detection has led to a substantial amount of research in many disciplines of Computer Science, based on the type of information considered, such as text, images, videos, software code, and structured data. This tutorial explores the similarities and differences between the techniques proposed for copy detection across the different types of information. We also examine the computational challenges associated with large-scale copy detection, indicating how they could be detected efficiently, and identify a range of open problems for the community.