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Extracting entity-based information in cyber-physical systems

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Listed:
  • Yuchen Yang
  • Lijie Li
  • Guisheng Yin

Abstract

This article proposes a framework P 3 E for evaluating entity extraction problem over cyber-physical system data. As known by us, this article is the first work focusing on this problem, which is an important step for identifying entities in cyber-physical system data. Directed by the initial entities, utilizing the relaxation and verification techniques, P 3 E provides a path expression–based solution for entity extraction problem, which has following characteristics. First, using path expressions, P 3 E provides a condensed presentation for entity locations whose size may get very large when scaling up the data size. Second, requiring only one entity example to indicate the interests, using relaxation technique, P 3 E can discover other similar entities automatically. Third, by adjusting the example given to P 3 E , users can specify their own interesting entities and control the entities discovered by P 3 E . Besides, utilizing the idea of sharing computations, by extending previous automaton techniques, an efficient implementation of P 3 E is provided. Experimental results are reported, which show that P 3 E can provide an effective and efficient solution to the entity extraction problem.

Suggested Citation

  • Yuchen Yang & Lijie Li & Guisheng Yin, 2017. "Extracting entity-based information in cyber-physical systems," International Journal of Distributed Sensor Networks, , vol. 13(4), pages 15501477177, April.
  • Handle: RePEc:sae:intdis:v:13:y:2017:i:4:p:1550147717702653
    DOI: 10.1177/1550147717702653
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    References listed on IDEAS

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    1. Kejia Zhang & Qilong Han & Guisheng Yin & Haiwei Pan, 2016. "OFDP: a distributed algorithm for finding disjoint paths with minimum total length in wireless sensor networks," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1623-1641, May.
    2. Longjiang Guo & Yingshu Li & Zhipeng Cai, 2016. "Minimum-latency aggregation scheduling in wireless sensor network," Journal of Combinatorial Optimization, Springer, vol. 31(1), pages 279-310, January.
    3. Xianmin Liu & Jianzhong Li, 2015. "Algorithms and complexity results for labeled correlation clustering problem," Journal of Combinatorial Optimization, Springer, vol. 29(2), pages 488-501, February.
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