Summary SQL — A fuzzy tool for data mining

doi:10.1016/S1088-467X(98)00009-2

Intelligent Data Analysis

Volume 1, Issues 1–4, 1997, Pages 49-58

Intelligent Data Ana...

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Abstract

The increasing use of computers for transactions and communication have created mountains of data that contain potentially valuable knowledge. To search for this knowledge we have to develop a new generation of tools, which have the ability of flexible querying and intelligent searching. In this paper we will introduce an extension of a fuzzy query language called Summary SQL which can be used for knowledge discovery and data mining. We show how it can be used to search for fuzzy rules.

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There are more references available in the full text version of this article.

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Financial literacy and psychological disaster preparedness: applicability of approach based on fuzzy functional dependencies
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These two approaches should not be considered as rivals, but rather as complementary approaches in explaining emotions in an emergency situation related to financial literacy. The concept of LSs is a mature one (introduced by Yager (1982) and for LSs with restriction by Rasmussen and Yager (1997). Since then, the theory of LSs has been improved and broadly applied in a variety of fields.
Recent catastrophes and crises emphasize the necessity of an individual's and household's preparedness for coping with these events. Previous research focuses mainly on short-term preparation and immediate response to catastrophes. The ex ante approach to managing long-term effects can rarely be found in literature, especially with an emphasis on financial preparedness. In this work, we use a soft computing method for analysing the social/psychological effects and implications. Specifically, we focus on data mining and linguistic interpretation of the effects of financial literacy on psychological preparedness for disasters, using fuzzy functional dependencies (FFDs). FFDs determine influences between features in datasets and examine neighbouring categorical answers with a certain degree of similarity, which overcomes indecisiveness when marking a categorical answer. Based on the surveyed data, we show that FFDs offer an approach for discovering additional patterns and explaining those patterns linguistically. Our results are as follows: financial literacy significantly influences anxiety, lack of control, nervousness, and fear of death (in order of intensity). Using this approach, we conclude that being better financially prepared can help in long-term disaster risk management. Financial preparedness is also helpful in mitigating psychological risks related to disasters. This paper provides results that can support the designing knowledge management solutions in order to improve psychological disaster preparedness.
Expression and efficient evaluation of fuzzy quantified structural queries to fuzzy graph databases
2019, Fuzzy Sets and Systems
This paper deals with fuzzy quantified queries in a graph database context. We study a particular type of structural quantified query and show how it can be expressed in an extension of the Neo4j Cypher query language. A processing strategy based on a compilation mechanism that derives regular (nonfuzzy) queries for accessing the relevant data is also described. Then, some experiments are performed that show the tractability of this approach.
Interpretability of fuzzy linguistic summaries This work is dedicated to Francesc Esteva, for his pioneering work in fuzzy logic and his dedication to the fuzzy community, especially in Europe
2016, Fuzzy Sets and Systems
Citation Excerpt :
It must be underlined that this approach does not generate all relevant sentences but checks the validity of some sentences queried by the user. FQuery [26], SummarySQL [27] or Quantirius [28] are instances of this approach. The computation complexity with these systems depends on the level of freedom in the query.
This paper investigates the question of the interpretability of fuzzy linguistic summaries, both at the sentence level and at the summary level, seen as a set of sentences. The individual sentence interpretability is examined as depending both on its representativity measured by a quality degree and on its linguistic expression. Different properties at the summary level are also discussed, namely their consistency, their non-redundancy and the information they convey.
Fuzzy quantification: A state of the art
2014, Fuzzy Sets and Systems
Citation Excerpt :
A simple example in this field using soft database queries is also provided in [90]. Some works developed a fuzzy query language called SummarySQL to let linguistic summaries be a part of the fuzzy queries, and then evaluate the summary by means of a validity measure [68,69]. In [5] it is developed a method for giving a linguistic summary of a numerical attribute involved in a fuzzy query.
Quantified sentences are a very powerful notion for modelling statements in Natural Language (NL), but in practice they have been used to solve several problems. This paper is intended to offer a global view of the development on this branch until now, focusing in the different approaches dealing with quantification, specially those involving imprecision, called fuzzy quantification. We put attention to the different mechanisms for defining them, the evaluation methods for measuring their fulfilment, as well as the properties they should satisfy.
Integration of data selection and classification by fuzzy logic
2012, Expert Systems with Applications
Citation Excerpt :
Although the SQL is a very powerful tool, it is unable to satisfy needs for data selection based on linguistic terms (vague predicates such as altitude near 200 m, high length of roads) and degrees of matching query conditions. To overcome this imperfection different approaches based on fuzzy logic have been proposed and many fuzzy query implementations have been designed, e.g. Kacprzyk and Zadrożny (1995), Rasmussen and Yager (1997), Bosc and Pivert (2000) and Wang, Lee, and Chen (2007). In order to bring benefit of the fuzzy logic to users and to make easy to use the querying tool, the fuzzy Generalized Logical Condition (GLC) capable to implement linguistic terms into the where part of the SQL has been created and described in Hudec (2009).
A concept of integration of fuzzy data selection and classification by fuzzy Generalized Logical Condition (GLC) is presented in this paper. The GLC that extends SQL queries with fuzzy logic was developed for the purpose of fuzzy data selection. In order to classify data by generating fuzzy queries from fuzzy rules, the extension of the GLC was created. The proposed methodology leads to the integration of data selection and data classification into one entity, while the access to relational databases remains unchanged. The obtained approach was presented on data from the municipal and urban statistical database. Data selection and classification problems can often be described more naturally in terms of natural language rather than by crisp numbers.
A functional interpretation of linguistic summaries of data
2012, Information Sciences
Citation Excerpt :
Section 3.3 stresses the needs for a new type of linguistic summaries, still in the form of a natural language sentence. The linguistic summaries proposed by Yager and co-workers [20–22,24,27] are quantified statements of type “Q X are A” or “Q B X are A” where Q is a linguistic quantifier [31], A and B are two gradual predicates defined by fuzzy sets. When dealing with relational databases, the crisp set X is a set of rows coming from the database (a relation from the database).
This article concerns the definition of linguistic summaries of data in the relational model of databases. Among the different approaches, the propositions calling on linguistic quantifiers are very appealing since they offer flexibility and a linguistic formulation which can be easily understood by a user. More precisely, these linguistic summaries are made of a quantified statement associated with a degree of validity (computed from the database). Such a summary shows that a quantity of information (quantity described by the linguistic quantifier) satisfies a constraint (defined by a fuzzy predicate). This article shows that the already proposed approaches do not sufficiently stress the relationship between these two aspects. As a consequence, we propose another kind of linguistic summaries made of a linguistic statement associated with a degree of validity having a clear meaning in terms of quantity and quality. It becomes possible to compare two linguistic summaries and to understand the difference in their degrees of validity. These summaries are called functional linguistic summaries because their interpretation relies on a particular set-oriented function. They present the advantages of using fuzzy terms to reflect that the user does not exactly know what can be expected from the database. However, the degree of validity provides a precise description of the stored information. In other words, starting from a rough idea of what can be expected as a summary, the obtained degree of truth provides a precise description of the information stored in the database. The relationship of the proposed linguistic summaries with the quantified statements are studied in this paper. It turns out that these linguistic summaries can be viewed as particular quantified statements having particular properties.

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^**: This work was performed while visiting the Machine Intelligence Institute at Iona College.

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Summary SQL — A fuzzy tool for data mining

Abstract

Information Sciences

Computing and Mathematics with Applications

J. Math. Anal. & Appl.

Knowledge Discovery in Databases