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In new generation of Internet applications such as smart healthcare and digital finance, sharing data to gain its value has become popular. However, some data contains personal privacy, and resource-limited devices generally delegate data to a Cloud Service Provider (CSP). CSP may not only be curious, but also selfish to save computation or download bandwidth. Therefore, the verifiable searchable encryption (VSE) scheme is applied to let CSP queries on encrypted data and ensure the integrity of results. Unfortunately, most existing VSE schemes require a trusted third party, which makes it difficult to provide reliable proof or make fair judgments when there occur disputes. With the assistance of blockchain technology, our proposed scheme blockchain-based verifiable searchable encryption (BCVSE) scheme has the following benefits: (1) Trusted proof: The traceability nature of blockchain can provide reliable transaction proof to help resolve disputes. (2) Fair judgment: Smart contracts that are natively supported by blockchain can publicly, transparently, and fairly conduct dispute adjudication and verify the integrity of results without trusted third parties. Furthermore, existing schemes generally make fuzzy queries by extracting data features first and then using similarity-matching algorithms. This leads to an increase in more false positives and a massive result set, making them more suitable for image data rather than text data which have more accurate query conditions. Therefore, we build the Verifiable Accumulator Trie (VAT), which is the core authenticated data structure for both verifiability and fuzzy query on text data. Formal security analysis and performance simulation demonstrate that BCVSE is secure and suitable for various blockchains. [ABSTRACT FROM AUTHOR]

Today, due to the massive amount of information, NoSQL databases are used to compute complex data. These databases are used to store semi-structured and unstructured data for big data management. In this paper, fuzzy queries are executed by users on the information stored in the Neo4j graph database, which show results in defuzzy manner. To evaluate the efficiency of the proposed method, we considered a database of manufactured company, two price and quality fuzzy variable were considered and used to define and execute the fuzzy queries that the results can verify the performance of the proposed method. Additionally, a counselor (who has information about the data) defines fuzzy terms, membership functions, and fuzzy rules table. The most important process in this method is to find the center of gravity in order to defuzzify the final result. So, an algorithm has been implemented for this purpose by C # programming language. The greatest query time is due to find center of gravity. The evaluation results show that the query time increasing by using the proposed approach than the query time by using Cypher language, is acceptable due to the complexity of fuzzy concepts; therefore, the proposed solution will be suitable for using ambiguous and fuzzy queries in large databases.

The massive production of data in the web environment increases the need for efficient and effective solutions to store and manage data. NoSQL databases with the capability of storing various types of data were introduced to be used in the management of big data. Besides, supporting flexible querying has significant impacts on data management usability and users prefer to ask their requests using fuzzy terms and get crisp results to make efficient decisions. In this paper, a fuzzy‐based approach is presented which adopts fuzzy queries and delivers crisp recommendations to the users. For this purpose, a counselor, which acts as a fuzzy agent and has knowledge about the data, defines fuzzy terms, membership functions and fuzzy rule tables in a way that each user can ask queries through fuzzy terms. The evaluation results show the efficiency of the proposed approach which removes the need for high‐level programming skills for users. As a result, our approach's efficiency and user acceptance are increased as well and made it an appropriate solution for using ambiguous queries in big graph databases. [ABSTRACT FROM AUTHOR]

Due to the high demands of searchability over encrypted data, searchable encryption (SE) has recently received considerable attention and been widely suggested in encrypted cloud storage. Typically, the cloud server is assumed to be honest-but-curious in most SE-based cloud storage systems, i.e., the cloud server should follow the protocol to return valid and complete search results to users. However, this trust assumption is not always true due to some unanticipated situations, such as misconfigurations and malfunctions. Therefore, the function of verifiability of search results becomes crucial for the success of SE-based cloud storage systems. For this reason, many verifiable SE schemes have been proposed; however, they either fail to support query operators “OR”, “AND”, “ $\ast$ * ” and “?” simultaneously, or require many time-consuming operations. Aiming at addressing this problem, in this paper, we propose a new verifiable SE scheme for encrypted cloud storage. The proposed scheme is characterized by integrating various techniques, i.e., bitmap index, radix tree, format preserving encryption, keyed-hash message authentication code and symmetric key encryption, for achieving efficient and verifiable conjunctive and fuzzy queries over encrypted data in the cloud. Detailed security analysis shows that our proposed scheme holds the confidentiality of data and verifiability of search results at the same time. In addition, extensive experiments are conducted, and the results demonstrate our proposed scheme is efficient and suitable for users to retrieve their data from the cloud to their mobile devices. [ABSTRACT FROM AUTHOR]

Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements. [ABSTRACT FROM AUTHOR]

With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall, precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper. [ABSTRACT FROM AUTHOR]

Today, due to massive amount of information, NoSQL databases are used to compute complex data. These databases are used to store semi-structured and unstructured data for big data management. In this paper, fuzzy queries are executed by users on the information stored in the Neo4j graph database, which show results in defuzzy manner. To evaluate the efficiency of the proposed method, we considered a database of manufacturing company. Price and quality fuzzy variables were considered and used to define and execute the fuzzy queries. The results can verify the performance of the proposed method. Additionally, a counselor (who has information about the data) defines fuzzy terms, membership functions, and fuzzy rules table. The most important process in this method is to find the center of gravity in order to defuzzify the final result. So, an algorithm has been implemented for this purpose by C # programming language. The greatest query time is due to find center of gravity. The evaluation results show that the increase in query time by using the proposed approach in comparison to query time by using Cypher language is acceptable due to the complexity of fuzzy concepts. Therefore, the proposed solution will be suitable for using ambiguous and fuzzy queries in large databases. [ABSTRACT FROM AUTHOR]

Increase in the volume of stored and processed information imposes special requirements to methods and tools for information search and processing. One of the indicators characterizing the quality of information retrieval is the query results relevance. This article purpose is to develop a methodology to quantify the relevance of query results. The query results’ relevance evaluation is important for the correct information retrieval. The presented method identifies and evaluates the query results for the three types of found object compliance to the request. For objects that do not completely correspond to the required specification, it is proposed to calculate the diversity factor between the found object and the query, as in some cases it is impossible to find an object that would at least partially satisfy the requirements. The presented method uses fuzzy sets to describe objects and queries to databases in order to facilitate objects’ searching and grouping by temporal characteristics, as well as allows to evaluate quantitatively the query results’ relevance for the correct information retrieval assessing.

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The fuzzy object oriented database model is often used to handle the existing imprecise and complicated objects for many real-world applications. The main focus of this paper is on fuzzy queries and tries to analyze a complicated and complex query to get more meaningful and closer responses. The method permits the user to provide the possibility of allocating the weight to various parts of the query, which makes it easier to follow better goals and return the target objects.

Structured Query Language (SQL) is used to obtain data from relational databases. Fuzzy improvement of SQL queries has advantages in cases when the user cannot unambiguously define selection criteria or when the user wants to examine data that almost meet the given criteria. In this paper we examine a realization of the fuzzy querying concept. For this purposes the fuzzy generalized logical condition for the WHERE part of the SQL is created. It allows users to create queries by linguistic terms. The proposed model is an extension of the SQL so that no modification inside databases has to be undertaken.

Retreieving the desired data accurately and quickly is as important as keeping it in an orderly manner. In database systems, data can be accessed by crisp queries. However, by nature, the user does not always use crisp queries. This leads to retrieval of redundant data for the queries. In nature, not everything is built upon exact values and thus needs flexibility. The subjective change of the data from one individual to another makes the query process harder. Thus, there is always a need for flexible query systems. Fuzzy queries and fuzzy databases give us the required flexibility. In this study, a human resources evaluation system was developed into a more flexible one by applying fuzzy databases and fuzzy queries. This is a hard subject to resolve by using crisp data. The data entered into the knowledge base was evaluated through the rule bases that were created and reporting with desired flexibity was achieved.

In order to meet the needs of ordinary users’ fuzzy query, and to take into account of the complexity and precision rate of implemented system, this paper summarizes existed user-query-oriented theory and presents a method for fuzzy query based on PBFS and NQL language. This paper proposes a SQL language at first with simple, intuitive features; Secondly, we propose a PBFS algorithm which can automatically generate SPARQL in the system based on the fuzzy input, so as to obtain accurate results. This algorithm has low complexity, convenient input, high accuracy and other characteristics; Finally, this schema is verified through the experiments. The results of experiments show that this program can effectively improve the user search efficiency according to its accurate, complex characteristics. [ABSTRACT FROM AUTHOR]

With the widespread acceptance of RDF (Resource Description Framework) as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. Efficient querying of RDF data is therefore of increasing importance. Preference queries and fuzzy queries are required for personalized queries, and have been extensively investigated in the context of databases. Focusing on SPARQL (Simple Protocol and RDF Query Language), we propose a fuzzy extension of SPARQL with preferences in this paper. First, we extend SPARQL with fuzzy sets by allowing the occurrence of fuzzy terms, fuzzy operators and fuzzy relations. Second, we introduce multi-dimensional preferences into the fuzzy SPARQL. Based on fuzzy set theory, we also propose a set of translation rules to convert the fuzzy SPARQL with preferences into crisp SPARQL. Finally we propose an effective scoring function to calculate the order of query results, which integrates the preferences and membership degrees. The proposed approach is evaluated with experiments as well. [ABSTRACT FROM AUTHOR]

Lots of evaluations in work life are made under uncertain conditions and conventional databases that do not have effective records; users could not always have the information they need. Moreover, usage of crisp value criteria in queries makes impossible to have the records that very close to provide query criteria. Because of these reasons, in the receiving information from a database, creation of queries that increase significant results and define as possible as same information for all users is gaining importance. In this study, solution of the identification problem of communication (base) stations operating out of residential areas which already installed in our country is aimed. Usage of fuzzy queries in classical databases method taken into consideration to improve a method to solve the stated problem for examined enterprise in this context. By using fuzzy queries in related database with an interface in the frame of identified rules and assumptions, the reporting and evaluation of the results are proved. [ABSTRACT FROM AUTHOR]

Increase in the volume of stored and processed information imposes special requirements to methods and tools for information search and processing. One of the indicators characterizing the quality of information retrieval is the query results relevance. This article purpose is to develop a methodology to quantify the relevance of query results. The query results' relevance evaluation is important for the correct information retrieval. The presented method identifies and evaluates the query results for the three types of found object compliance to the request. For objects that do not completely correspond to the required specification, it is proposed to calculate the diversity factor between the found object and the query, as in some cases it is impossible to find an object that would at least partially satisfy the requirements. The presented method uses fuzzy sets to describe objects and queries to databases in order to facilitate objects' searching and grouping by temporal characteristics, as well as allows to evaluate quantitatively the query results' relevance for the correct information retrieval assessing. [ABSTRACT FROM AUTHOR]

Customer information is critical to customer relationship management. The goal of this research is to improve the efficiency of customer relationship management through developing a customer information system. Fuzzy terms with linguistic variables can help specific queries to be more versatile and user-friendly for customer data mining. In this paper, we propose a method integrating cluster analysis with linguistic variables in the context of fuzzy query logistics. Based on the proposed method, this research constructed a customer information system that can offer the user useful information as regards with strategy, decision-making, and better resource allocation methods. Results show the proposed method can decrease total execution time and increase the practicability with the feature of customer information cluster analysis. [ABSTRACT FROM AUTHOR]

Abstract: The data of production engineering industry has gradually increased after years of development, but it lacks of a unified, interactive, visual information query and show platform. This paper focuses on production engineering construction equipment and scheme of the system architecture of the simulation system, it analyzes the basic required function of the system–model show, stratigraphic simulation, and production data query,and also introduces the key technology of system, including the equipment model transformation, the stratigraphic of the simulation, the realization of human-computer interaction. The system can provide users with complete database information query, intuitively shows the problems of the component parts information and production measures, etc, so it is helpful for engineering design personnel to design program, reporting and analysis. [Copyright &y& Elsevier]

Traditional database systems can only adequately cope with perfect data .It handles data, which is crisp and precise in nature. However our reasoning and decision-making process is uncertain, imprecise, inapplicable and vague in nature. Fuzzy techniques are the mathematical tool and most possibly the best for treating uncertain, vague, or subjective information. This paper gives an insight into the world of uncertainty and the concept of fuzziness in databases and the ways of handling the fuzzy queries to databases [ABSTRACT FROM AUTHOR]