Literature DB >> 30136954

MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration.

Po-Ming Law, Zhicheng Liu, Sana Malik, Rahul C Basole.   

Abstract

Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.

Entities:  

Year:  2018        PMID: 30136954     DOI: 10.1109/TVCG.2018.2864886

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  1 in total

1.  Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records.

Authors:  Cinyoung Hur; JeongA Wi; YoungBin Kim
Journal:  Int J Environ Res Public Health       Date:  2020-11-10       Impact factor: 3.390

  1 in total

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