1,720,994 research outputs found
A unified view of multi-grade fuzzy-set models in J-CO-QL+
Get PDFThe 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
Soft Querying Features in GeoJSON Documents: The GeoSoft Proposal
Get PDFSince the advent of JSON as a popular format for exchanging large amounts of data, a novel category of NoSQL database systems, named JSON document stores, has emerged for storing JSON data sets; in fact, these novel databases are able to natively manage collections of JSON documents. To help analysts and data engineers query and integrate JSON data sets persistently saved in JSON document stores, the J-CO Framework has been developed (at the University of Bergamo, Italy): it is built around a novel query language, named J-CO-QL + , that provides sophisticated features, including soft-querying capabilities. However, J-CO-QL + (as the other languages for querying JSON data sets) is designed to be general purpose; consequently, it can be cumbersome for users to apply it on specific data formats. This is the case of GeoJSON, a specific and popular JSON data format that is designed to represent geographical information layers. This paper presents the latest evolution of GeoSoft, a novel high-level “domain-specific language” that is specifically designed to express complex queries on the GeoJSON documents, including soft-queries. GeoSoft is inspired to the classical SQL language, so as to reduce the learning curve of potential users. GeoSoft queries are translated into J-CO-QL + scripts, to be actually executed
Soft Querying JSON Datasets with Personalized Preferences and Aggregations
No full textSoft 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
Evolving J-CO-QL+ with fuzzy evaluators for flexible queryisng of JSON data sets
Get PDFHow 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
Toward a product search engine based on User Reviews
No full textWe address the problem of developing a method for retrieving products exploiting product user-reviews that
can be found on the internet. For this purpose, we introduce a ranking model based on the concept of itemset
mining of frequent terms. The prototype search engine that implements the proposed retrieval model is
illustrated, and a preliminary evaluation on a real data set is discussed
Toward an Anayist-Oriented Polystore Framework for Processing JSON Geo-Data
No full textGeo-data sets represented as JSON documents are provided by many sources over the Internet. Consequently, many storage technologies are adopted for managing them, namely NoSQL databases such are MongoDB and ElasticSearch. To allow analysts to effectively perform complex analysis processes, such as integration and transformations possibly based on spatial properties, a framework that provides a unified view of different database systems based on different technology is necessary, so that analysts are unaware of specific characteristics of different database systems. In other words, we follow a polystore approach. The presented framework, named J-CO, provides a query language and an execution engine that, together, enable analysts to specify complex transformation processes on JSON geo-data sets. High-level operator natively support spatial operations, and completely hide technical details of the integrated database systems. This way, a novel data store component, named J-CO-DS, can be seamlessly introduced, to overcome limitations characterizing other NoSQL database systems. The paper presents the framework and the novel features (w.r.t. our previous works); then, it shows the effectiveness of the framework itself through an example
SWI (Soft Web Intelligence) Powered by User-Defined Fuzzy Operators and Aggregators
No full textWe 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
Linguistic Analogies in Word Embeddings: Where Are They?
Get PDFWord Embedding has greatly improved Natural-Language Processing. In word-embedding models, words are represented as vectors in a multi-dimensional space; these vectors are trained through neural networks, by means of very large corpora of textual documents. Linguistic analogies are claimed to be encoded within word-embedding models, in such a way that they can be dealt with through simple vector-offset operations. This paper aims to give an answer to the following research question: given a word-embedding model, are linguistic analogies really present? It seems rather unrealistic that complex semantic relationships are encoded within a word-embedding model, which is trained to encode positional relationships between words. The investigation methodology is presented, and the results are discussed. This leads to the following question: “Linguistic analogies in Word Embeddings: where are they?”
Intuitionistic Fuzzy Sets in J-CO-QL + ?
No full textIntuitionistic Fuzzy Sets extend the classical notion of Fuzzy Sets, so as to represent “hesitation”: indeed, an item has both a membership degree and a non-membership degree, whose sum could be less than 1; the difference denotes the “hesitation” about the fact that the item belongs or not to the fuzzy set. Similarly, Intuitionistic Fuzzy Relations involve two domains. Supposing that Intuitionistic Fuzzy Sets and Relations are provided as JSON data sets, is there a stand-alone tool to process them? This paper studies if the constructs currently provided by J-CO-QL + (the query language of the J-CO Framework) for managing fuzzy sets can actually deal with Intuitionistic Fuzzy Sets and Relations. The results will suggest how to extend J-CO-QL + to deal with classical and Intuitionistic Fuzzy Sets in an integrated way
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