Summarizing Event Sequence Database into Compact Big Sequence

Detecting the core structure of a database is one of the most objective of data mining. Many methods do so, in pattern set mining, by mining a small set of patterns that together summarize the dataset in efficient way. The better of these patterns, the more effective summarization of the database. Most of these methods are based on the Minimum Description Length principle. Here, we focus on the event sequence database. In this paper, rather than mining a small set of significant patterns, we propose a novel method to summarize the event sequence dataset by constructing compact big sequence namely, BigSeq. BigSeq conserves all characteristics of the original event sequences. It is constructed in efficient way via the longest common subsequence and the novel definition of the compatible event set. The experimental results show that BigSeq method outperforms the state-of-the-art methods such as Gokrimp with respect to compression ratio, total response time, and number of detected patterns.

@article{Hassaan2022,
  title     = {Summarizing Event Sequence Database into Compact Big Sequence},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Mosab Hassaan},
  doi       = {10.14569/IJACSA.2022.0130891},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130891}
}