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A cognition graph approach for insights generation from event sequences

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Abstract

In recent years, cognition map techniques for human insights have already played a significant part in complex or ill-structured problem solving. There are increasing interests on computational methods rather than hand-drawing methods to build an cognition graph for insights generation. In this paper, a systematic approach called Temporal-IdeaGraph is proposed to build a directed cognition graph based on event sequences. Firstly, an algorithm of frequent sequence mining is employed to capture sequential patterns and a method is then designed to remove duplicate patterns. Secondly, relevant patterns are merged and visualized into a directed cognition graph. An algorithm is further proposed to identify bridge events and bridge patterns which would trigger human’s deeper insights for better decision making. Finally, two real case studies validate the effectiveness of proposed approach.

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Acknowledgements

This work is supported by Natural Science Foundation of China (Grant Nos. 61672501, 61303164, 61402447 and 61502466). This work is also sponsored by Development Plan of Outstanding Young Talent from Institute of Software, Chinese Academy of Sciences (ISCAS2014-JQ02).

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    Authors and Affiliations

    1. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

      Wei Wang, Chen Zhang, Hao Wang & Yang Gao

    2. School of Geophysics and Information Technology, China University of Geosciences, Beijing, 100083, China

      Yuanman Zheng

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    1. Wei Wang
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    2. Chen Zhang
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    3. Hao Wang
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    Correspondence to Chen Zhang, Hao Wang, Yang Gao or Yuanman Zheng.

    Additional information

    Wei Wang and Chen Zhang are the co-first authors. Hao Wang and Chen Zhang contribute main idea to this paper as the corresponding authors. Yang Gao and Yuanman Zheng are also the corresponding authors.

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    Wang, W., Zhang, C., Wang, H. et al. A cognition graph approach for insights generation from event sequences. Cluster Comput 20, 1679–1690 (2017). https://doi.org/10.1007/s10586-017-0744-4

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