A soft computing approach to big data summarization

The added value of a dataset lies in the knowledge a domain expert can extract from it. Considering the continuously increasing volume and velocity of these datasets, efficient tools have to be defined to generate meaningful, condensed and human-interpretable representations of big datasets. In the...

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Bibliographic Details
Published in:Fuzzy sets and systems 2018-10, Vol.348, p.4-20
Main Authors: Smits, Grégory, Pivert, Olivier, Yager, Ronald R., Nerzic, Pierre
Format: Article
Language:English
Subjects:
Computer Science
Data personalisation
Knowledge extraction
Linguistic summaries
Soft computing
Specificity measure
Visualization
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Summary:The added value of a dataset lies in the knowledge a domain expert can extract from it. Considering the continuously increasing volume and velocity of these datasets, efficient tools have to be defined to generate meaningful, condensed and human-interpretable representations of big datasets. In the proposed approach, soft computing techniques are used to define an interface between the numerical and categorical space of data definition and the linguistic space of human reasoning. Based on the expert's own vocabulary about the data, a personal summary composed of linguistic terms is efficiently generated and graphically displayed as a term cloud offering a synthetic view of the data properties. Using dedicated indexing strategies linking data and their subjective linguistic rewritings, exploration functionalities are provided on top of the summary to let the user browse the data. Experimentations confirm that the space change operates in linear time wrt. the size of the dataset making the approach tractable on large scale data.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2018.02.017