Furthermore, different professional figures are involved in the process, possibly with different skills (database technicians, domain experts, employees): the lack of common knowledge and of a unifying framework significantly obstructs the effective and efficient definition and continuous generation of reports. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Many organizations must produce many reports for various reasons. Specifically, the paper provides the following contributions: (1) It presents a soft-computing technique for integrating data sets describing public places, without any preliminary pre-processing, cleaning and training, which can be applied from scratch (2) it presents current capabilities for soft integration of JSON data sets, provided by the J-CO Framework (3) it demonstrates the effectiveness of the soft integration technique (4) it shows how a stand-alone tool able to support soft computing (as the J-CO Framework) can be effective and efficient in performing data-integration tasks from scratch.
This paper demonstrates that a methodology based on soft integration (i.e., data integration performed through soft computing and fuzzy sets) can now be effectively applied from scratch, through the J-CO Framework, which is a stand-alone tool devised to process JSON data sets stored within JSON document stores, possibly by performing soft querying on data sets. Analysts usually need to perform such an integration from scratch, without performing complex and long preprocessing or data-cleaning tasks, as well as without performing training activities that require tedious and long labeling of data furthermore, analysts now have to deal with the popular JSON format and with data sets stored within JSON document stores. The possibility offered by the current technology to collect and store data sets regarding public places located on the Earth globe is posing new challenges, as far as the integration of these data sets is concerned.
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