Timing-IdeaGraph: A directed cognition graph approach for decision making based on temporal event sequences
Hao Wang, Siyu Lu, Chen Zhang, Qinyong Wang, Fanjiang Xu · 2016
Sequence pattern mining is an important mining task in data mining. However, most researches focus on improving the efficiency of algorithms, more and more attentions are paid to independently analyzing each frequent sequential pattern, which could bring biases to the final decisions. To understand the whole situation with sequence patterns, this paper proposes a systematic approach called Timing-IdeaGraph to build a directed cognition graph. Firstly, with consideration of big data on event sequences, an efficient algorithm is applied to capture frequent sequential patterns. Next, duplicate patterns are removed. After that, we merge relevant patterns and visualize them into a directed cognition graph. In order to make the approach human-centric, we propose an algorithm to identify bridge events and patterns which would proactively trigger human's deep cognition, e.g., creative design, for better decision making. Two real case studies are introduced to show how to use Timing-IdeaGraph in a computer supported cooperative environment.