Performing Machine Learning (ML) tasks on large-scale datasets, as well as simply storing them for subsequent analysis or for long-term archival, could require large computational power. The described approach builds on the technique known as “Bayesian Generation” to produce synthetic datasets in such a way that the probability distribution in the source dataset is maintained as much as possible in the new synthetic ones, even if they are much smaller than the original (large) dataset. In fact, this study investigates the impact of generating smaller synthetic datasets for training ML models in place of the original dataset, adopting a twofold perspective. Firstly, the impact on the effectiveness of ML models trained on these smaller synthetic datasets is assessed. Secondly, the amount of computational resources required to generate the synthetic datasets, train ML models on them, and perform the testing phase is measured. Specifically, both execution time and main memory usage are taken into account. Finally, this research work shows that the loss in terms of effectiveness remains consistently limited and stable, and it identifies the scenarios and ML techniques for which incorporating the generation of small synthetic datasets into the ML pipeline can be beneficial for practical deployment in environments with constrained computational resources, such as mobile or industrial devices.

How to introduce soft querying based on fuzzy sets in the novel J-CO-QL + query language (specifically designed to query collections of JSON documents from NoSQL databases) has been investigated by the authors in their past work. Specifically, capabilities for defining fuzzy operators and fuzzy aggregators were introduced through two distinct concepts on which two different language constructs were based. This paper proposes the unified concept of “fuzzy evaluator”, by means of which it is possible to define complex methods for evaluating the membership degrees of JSON documents to fuzzy sets, so as to capture complex semantics while analyzing data in a soft way. The paper both provides a formal meta-model for fuzzy evaluators, and proposes a novel statement for the J-CO-QL + language, so as to further foster soft-querying capabilities.

Soft conditions are a powerful and established formal tool to select data on the basis of linguistic predicates. In previous work, the J-CO Framework (and its query language) was used to perform Soft Web Intelligence, i.e., a practical interpretation of the concept of Web Intelligence that exploits soft conditions to search for desired items in JSON datasets acquired from Web sources. However, the effectiveness of soft conditions depends on how elementary conditions are combined: in this sense, a plethora of proposals are available, such as the vector p-norm. This paper shows how a generic concept, named "user-defined fuzzy evaluator", that has been recently introduced in the query language, actually allows users to define their own operators, so as to express advanced operators such as "and possibly". The paper also shows how the AND operator defined as a vector p-norm actually behaves, depending on different configurations of parameters, so as to let the reader understand how to use it in practice.

The complexity of reality has driven the evolution of Fuzzy-Set Theory from the initial proposal made by Zadeh in 1965, towards more complex models. Moving from a quick survey of the evolution of Fuzzy-Set Theory, this paper highlights the aspects that are common to many Fuzzy-Set Models, in order to define a meta-model that is capable of providing a unified view to a wide variety of fuzzy-set models. In particular, this work focuses the attention on the family of “Multi-grade Fuzzy Sets”, which are fuzzy sets characterized by more than one degree. The lack of tools capable of querying the large amount of data that are nowadays available in NoSQL databases, has pushed us to devise the J-CO Framework: it is a platform-independent tool that is capable to manage, transform and query collections of JSON documents; the J-CO Framework relies on J-CO-QL+, which is a high-level, general-purpose language with soft-querying capabilities. The latest advancements of J-CO-QL+ allow for defining and exploiting user-defined Multi-grade Fuzzy-Set Models and Operators. In the paper, a case-study demonstrates the effectiveness of the J-CO Framework in performing a non-trivial soft query based on a Multi-grade Fuzzy-Set Model defined by the user.

In our previous work, we proposed Soft Web Intelligence as the interpretation of the general notion of Web Intelligence in the current technological panorama, in such a way 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. Specific extensions to the J-CO Framework and to its query language (named J-CO-QL+) made possible to practically implement the concept. However, any “data intelligence” activity does not exclude aggregating data, but J-CO-QL+ did not provide statements for defining “user-defined fuzzy aggregators”. In this paper, we present the novel constructs introduced into J-CO-QL+ to allow users to define and use their own fuzzy aggregators, so as to evaluate membership degrees to fuzzy sets moving from array fields within processed JSON documents. This way, complex soft queries are enabled, so as to enhance Soft Web Intelligence.

Since the last two decades, Web Intelligence has denoted a plethora of approaches to discover useful knowledge from the vast World-Wide Web; however, dealing with the immense variety of the Web is not easy and the challenge is still open. In this paper, we moved from the previous functionalities provided by the J-CO Framework (a research project under development at University of Bergamo Italy), to identify a vision ofWeb Intelligence scopes in which capabilities of soft computing and soft querying provided by a stand-alone tool can actually create novel possibilities of making useful analysis of JSON data sets directly coming from Web sources. The paper identifies some extensions to the J-CO Framework, which we implemented; then it shows an example of soft querying enabled by these extensions.

GeoJSON documents have become important sources of information over the Web, because they describe geographical information layers. Supposing to have such documents stored in some JSON store, the problem of querying them in a flexible and easy way arises. In this paper, we propose a soft-querying model to easily express queries on features (i.e., data items) within GeoJSON documents, based on linguistic predicates. These are fuzzy predicates that evaluate the membership degree to fuzzy sets; this way, imprecise conditions can be expressed and features can be ranked, accordingly. The paper presents a rewriting technique that translates soft queries on GeoJSON documents into fuzzy JCO-QL queries: this is the query language of the J-CO Framework, an Internet-based framework able to get, manipulate and save collections of JSON documents in a way totally independent of the source JSON store.

In the era of Web 2.0, users strongly contribute to blogs, providing their opinions and useful information about specific topics; typically, users that are interested in a topic looks for related blogs in pull mode. But social networks have become even more effective in disseminating opinions and information in push mode, because users directly receive tweets and posts.