We proposed the notion of Soft Web Intelligence in our previous works: in short, it interprets the general notion of Web Intelligence in a modern key, by explicitly considering the current technological panorama. Specifically, JSON data sets are acquired from the Internet, stored within JSON document stores and then processed and queried by means of soft computing and soft querying methods. In this paper, we present the joint adoption of fuzzy operators and fuzzy aggregators to perform data-analysis tasks on aggregated data. Indeed, when data are processed through fuzzy sets to perform soft querying, data items with membership degrees to fuzzy sets could be aggregated, to obtain a novel membership to another fuzzy set belonging to a different universe; in this context, User-defined Fuzzy aggregators become critical for actually performing Soft Web Intelligence based on soft computing.

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SWI (Soft Web Intelligence) Powered by User-Defined Fuzzy Operators and Aggregators

  • Paolo Fosci,
  • Giuseppe Psaila

摘要

We proposed the notion of Soft Web Intelligence in our previous works: in short, it interprets the general notion of Web Intelligence in a modern key, by explicitly considering the current technological panorama. Specifically, JSON data sets are acquired from the Internet, stored within JSON document stores and then processed and queried by means of soft computing and soft querying methods. In this paper, we present the joint adoption of fuzzy operators and fuzzy aggregators to perform data-analysis tasks on aggregated data. Indeed, when data are processed through fuzzy sets to perform soft querying, data items with membership degrees to fuzzy sets could be aggregated, to obtain a novel membership to another fuzzy set belonging to a different universe; in this context, User-defined Fuzzy aggregators become critical for actually performing Soft Web Intelligence based on soft computing.