Fuzzy Query Research Articles

A Study on Fuzzy Query with Weights Based on Fuzzy Logic

Natural Language Processing (NLP) is the electronic tactic to analyzing text that is depends on both a set of ideas and a set of technologies. Natural Language Processing (NLP) is a subfield of artificial intelligence and etymology it thinks about the issues of computerized era and comprehension of regular human dialects. Common dialect era frameworks change over data from PC databases into ordinary sounding human dialect, and normal dialect understanding frameworks change over specimens of human dialect into more formal representations that are less demanding for PC projects to control. The Fuzzy logic-based approach provides another alternative for effective natural language analysis. It is commonly recognized that many phenomena in natural language lend themselves to descriptions by Fuzzy mathematics, including Fuzzy sets, Fuzzy relations and Fuzzy logic. By defining a Fuzzy logic system and acquiring proper rules, we hope that difficulties in analysis of speech can be alleviated. The goal of NLP is to enable communication between people and computers without resorting to memorization of complex commands and procedures.

A fuzzy logic‐based approach for fuzzy queries over NoSQL graph database

AbstractThe 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.

High-Efficient Fuzzy Querying With HiveQL for Big Data Warehousing

Querying and reporting from large volumes of structured, semistructured, and unstructured data often requires some flexibility. This flexibility provided by fuzzy sets allows for categorization of the surrounding world in a flexible, human-mind-like manner. Apache Hive is a data warehousing framework working on top of the Hadoop platform for big data processing. Hive allows executing queries and aggregating and analyzing data stored in Hadoop distributed file system and other repositories. Hive responds to the current needs for efficient big data warehousing, which is impossible with traditional data warehouses due to their rigid nature. This article presents the FuzzyHive library that extends the Hive framework with fuzzy sets based techniques for querying, analyzing, and reporting on big data warehouses. We formalize the fuzzy techniques used while operating on Hive-based data warehouses (including fuzzy filtering on dimensional attributes, projection with fuzzy transformation, fuzzy grouping, and joining). We also show how we embedded these operations in Hive query language, which was not studied so far. Such extensions make big data warehousing more flexible and contribute to the portfolio of tools used by the community of people working with fuzzy sets and data analysis. The FuzzyHive library complements the spectrum of available solutions for fuzzy data processing and querying in large datasets. We investigate Hive fuzzy querying performance, effectiveness, and scalability for various data storage formats (text, Avro, and Parquet). Our experiments demonstrate that the proposed extensions introduce more elasticity and are also efficient for big data warehousing, which is the first such kind of solution for this environment.

Privacy-Aware Fuzzy Range Query Processing Over Distributed Edge Devices

Range query processing is a common edge computing and service in the Internet of things, which can extract user-interest information from distributed edge devices. How to design lightweight privacy-preserving range query processing methods remains a challenging task. Existing secure range query approaches suffer from both high communication cost and long response time, which makes them unsuitable for edge computing over resource-constrained edge devices. In this article, we propose two privacy-aware fuzzy query processing schemes based on fuzzy theory. Linguistic range variables, fuzzy overlap information, and its recovery mechanism are introduced. In addition, two distributed privacy-aware fuzzy range query processing algorithms are devised. Our approaches not only serve for privacy protection, but also aim to provide other optimal performances in terms of reliability, energy efficiency, and real-time response. Theoretical analysis and experimental evaluations based on real-world datasets validated our motivation.

Fuzzy querying with SQL: Fuzzy view-based approach

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.

Indexing possibilistic numerical data using Interval B+-trees

When record sets become large, indexing becomes a required technique for speeding up querying. This paper proposes an indexing technique for interval data. Such data are common in possibility based relational databases but are also frequently used in other applications. Our approach is an adaptation of a B+-tree, which is currently still one of the most efficient indexing techniques. Because it can store interval data, we name it the Interval B+-tree (IBPT). It is illustrated how an IBPT index can be built and applied in practice to speed up the evaluation of fuzzy queries on possibilistic relational databases.

Fuzzy Object Oriented Database for Big Data

Due to the huge amount of uncertain data collection, mankind facing a colossal amount and fast data having such a composite configurations. The data can be transact on the web and manually exchanges, interpersonal organizations and through our everyday life exercises. A very helpful result of designing an appropriate Big Data in different zones, for example, medical services, human services, executives and administrators. Managing the Big Data efficiently, new investigation perspective has been secured at this point the viewpoints about huge information (Big Data) requires extra-long stretch innovative interests. The Fuzzy Logic s have been implemented here for Big Data because of their capacities to handle the vagueness and uncertainties in the information. A couple of imaginative approach for Big Data processing is presented previously. To abbreviate the current duties and extant a point of view of further enhancements. We survey the different examinations and concentrated on that there are constrained Fuzzy systems have been adopted in large information (Big Data preparing. We also examine the various advantages of Fuzzy Logic s in large information (Big Data) issues. Therefore in this paper a Fuzzy object-oriented database is designed for Big Data and perform some Fuzzy queries to check the performance of the designed Fuzzy object-oriented database. We focused on the continuously propelled augmentations of Fuzzy sets and their blends in with various contraptions could offer a novel promising planning condition.

Fuzzy request handler for Mongo QL derived from SQL

Relational DBMS are often used to store fuzzy values, but problems arise with putting such data in a tabular form. Moreover, there appears a problem of storing both the crisp and fuzzy data related to one subject domain in one column of a relational table. This article considers the mechanism of storing crisp and fuzzy values and linguistic variables in the document-oriented Mongo DBMS. The data are stored in the collection as GeoJSON geometry; different geometries are used for different data options. The possibility of storing crisp scalar values, crisp value sets, crisp value intervals and fuzzy values in the collection documents is described. For data processing by means of SQL queries, the context-free grammar of the SQL subset is described, according to which lexer and parser are generated. In order to form the structure of an abstract syntactic tree, a corresponding object model has been implemented. A translator application has been developed, which allows converting SQL queries related to the crisp and fuzzy data into Mongo QL queries. The algorithm of fuzzy queries translation process is suggested; the geometrical interpretation of data comparison operations is described. The examples show the options of fuzzy comparison operations for different value options.

Feature mining and analysis of gray privacy products

PurposeExcavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products.Design/methodology/approachThis research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear.FindingsEstablishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions.Originality/valueThe research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.

A Blocky and Layered Management Schema for Remote Sensing Data

In the era of rapid data expansion and computer technology development, discrete storage, multiband push and fuzzy query remote sensing data management methods are no longer suitable for the data analysis needs of users, including the needs for long time series, global regions and multidata fusion. After analyzing traditional data management techniques, this paper discusses the existing achievements and development trends of current technologies. This paper aims to solve the problem of data sharing difficulties and organizational inconsistency caused by the use of different formats for the same spatial object. Based on a discrete global grid, this paper studies the blocky division method and coding specification of Google S2 and then accomplishes the layered storage of remote sensing data in HBase. Finally, Kylin is used to build a cube model to discuss the information mining analysis changes in the new data management model. Experiments show that the blocky and layered management schema (BLMS) can realize the unified management of global remote sensing data with multisource, heterogeneous, multiscale, and long-term characteristics and provide accurate data services on demand.

UFleSe: User-Friendly Parametric Framework for Expressive Flexible Searches

We present a parametric framework (UFleSe) with a user-friendly interface having a search engine that enables regular users (without the need of neither technical nor theoretical knowledge) to define their fuzzy concepts, rules, similarity relations, synonyms, antonyms, and personalizing their definitions for different users, and to link them with the crisp database fields for performing flexible, expressive queries in a language close to natural language. It works over multiple modern and conventional data formats, such as JSON, SQL, Prolog, CSV, XLS, and XLSX. We present the syntax involved in the construction of our various flexible searching criteria and their personalizations. Furthermore, we present the architecture of this novel system that combines fuzzy, crisp data, and similarity relations in its queries to return constructive answers ordered by a degree of searching criteria satisfaction (truth-value between 0 and 1). Finally, we include a comparative analysis of different fuzzy querying systems here, and we provide various experiments, to show the system behavior, performance, efficiency, and scalability as well.

ILogBook: Enabling Text-Searchable Event Query Using Sparse Vehicle-Mounted GPS Data

Querying an incident (i.e., an occurrence of seemingly minor importance) from coarse-grained driving log (i.e., GPS trace) has been a daunting task. For example, “Which restaurant did I drive by at exactly 4 pm yesterday?” The question seems very simple but is nontrivial, because the question is semantics-driven while the actual log data are GPS coordinate-based. Especially, the practical GPS log is very sparse and inaccurate for high-speed mobile objects such as vehicles. This paper seeks to answer any fuzzy query over sparse vehicles GPS data. Our system, called iLogBook, achieves these two goals by leveraging tensor technique and latent semantic analysis to high-precision trajectory recovery and similarity matching. We have implemented and evaluated the iLogBook with the GPS data of over 13, 798 taxicabs collected in eight days in Shenzhen, China. Our results show about 97% accuracy in trajectory inference. Moreover, the system handles about 90% daily queries among 10 marked drivers.

Implementation of Tahani Fuzzy Logic Method for Selection of Optimal Tourism Site

Indonesia has a variety of tourism industries. Travel makes people’s interest increase in visiting tourist sites. A recommendation application in the field of tourism is needed for the selection of the right tourism object was to be implemented in Central Java. This application is expected to provide information on prospective tourists in choosing tourist attractions effectively. This application can facilitate prospective tourists to choose tourist objects that match the criteria by applying the fuzzy tahani method. Fuzzy Tahani describe a fuzzy query processing method, based on language manipulation known as SQL (Structured Query Language). The Fuzzy Tahani model is very appropriate to be used in the process of finding the right and accurate data. Variables used in this application are variable ticket prices, number of facilities, number of visitors, distance traveled and types of tourist attractions.

Research on a Novel Improved KMP Fuzzy Query Algorithm

The rapid development of computer technology has led to an increasing demand for database management information systems. Most of the data queries existing in the current system use accurate query methods, which leads to inefficient query and cannot solve the fuzzy matching problem between strings. Based on the Knuth-Morris-Pratt algorithm, this paper introduces the concept of ambiguity and proposes an improved KMP fuzzy query algorithm, which is applied to the disease query system to verify the feasibility of the algorithm. The improved KMP fuzzy query algorithm not only has a high matching speed between strings, but also satisfies the fuzzy matching between strings. Compared with the traditional BF algorithm, KMP algorithm and other algorithms, the improved KMP fuzzy query algorithm has superiority in terms of fuzzy matching.

Achieve Efficient and Verifiable Conjunctive and Fuzzy Queries over Encrypted Data in Cloud

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”, “ <inline-formula><tex-math notation="LaTeX">$\ast$</tex-math></inline-formula> ” 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.

Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

Combining P-type iterative learning (IL) control, fuzzy logic control and artificial bee colony (ABC) algorithm, a new optimal fuzzy IL controller is designed for active vibration control of piezoelectric smart structures. In order to accelerate the learning speed of feedback gain, the fuzzy logic controller is integrated into the ANSYS finite element (FE) models by using APDL (ANSYS Parameter Design Language) approach to adjust adaptively the learning gain of P-type IL control. For improving the performance and robustness of the fuzzy logic controller as well as diminishing human intervention in the operation process, ABC algorithm is used to automatically identify the optimal configurations for values in fuzzy query table, fuzzification parameters and defuzzification parameters, and the main program of ABC algorithm is operated in MATLAB. The active vibration equations are driven from the FE equations for the dynamic response of a linear elastic piezoelectric smart structure. Considering the vibrations generated by various external disturbances, the optimal fuzzy IL controller is numerically investigated for a clamped piezoelectric smart plate. Results demonstrate that the proposed control approach makes the feedback gain has a fast learning speed and performs excellent in vibration suppression. This is demonstrated in the results by comparing the new control approach with the P-type IL control.

Retrieving knowledgeable information from cloud using trapezoid-based multi-keyword query ranking with weighted distributed function

Collection of information is available in cloud and growing at an exponential rate and this scope makes the organisation and accessing of data critical for efficient use of knowledgeable information. In order to obtain the desired outcome, users heavily rely upon the cloud information retrieval methods. Many research works have been conducted in the field of knowledge related information retrieval. However, effective ranking of complex query pose significant challenges. In this paper, to efficiently rank the query on cloud services a method called, weighted fuzzy multi-keyword rank query (WFM-KRQ) is introduced. This method is based on associating a query with a fuzzy rule, applying a weighted distribution function and a pruning technique based on a threshold value. The proposed encrypted RepeatKeyRotate algorithm effectively improves the performance rate of cloud data sharing services by reducing the processing time and improving the retrieval rate on handling complex queries when compared to the state-of-the-art works.

Clustering and Query Optimization in Fuzzy Object-Oriented Database

The purpose of the clustering method is to provide some meaningful partitioning of the data set. In general, finding separate clusters with similar members is essential. A problem in clustering is how to determine the number of optimal clusters that best fits the data set. Most clustering algorithms generate a partition based on input parameters (for example, cluster number, minimum density) which results in limiting the number of clusters. Therefore, the article proposes an improved EMC clustering algorithm that is more flexible in handling and manipulating those clusters, where input parameter values are assumed to be different clusters for different partitions of a data set. In addition, based on the above partitioning results, this article proposes a new approach to processing and optimizing fuzzy queries to improve efficiency in the manipulation and processing of specific data such as (less time consuming, less resource consuming)

Horizontal fragmentation for fuzzy querying databases

Fuzzy querying is one of the main research topics of database investigators. Several research works to date focused on building fuzzy data models, fuzzy query languages, and fuzzy database systems. However, such systems turn out to be less efficient when it comes to querying very large data. Therefore, improving the performance of such database systems is an important research issue. In this paper, we address this issue by proposing a complete fragmentation methodology. Especially, we propose an horizontal fragmentation algorithm as well as different query execution strategies whose aim is minimizing the number of fragment accesses. Extensive experimental evaluation demonstrates the efficiency of our framework scaling up to millions of tuples.

Fuzzy queries of social networks with FSA-SPARQL

Social networks have become a source of data which are of interest in all areas, and their querying and analysis is a hot topic in computer science. Our research group has developed a fuzzy extension of the Semantic Web query language SPARQL, called FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL). This extension provides mechanisms to express fuzzy queries against RDF data. FSA-SPARQL works with social networks. With this aim, FSA-SPARQL enables the transformation and fuzzification of social network API data. Fuzzification of social networks data is automatic and user-defined enabling a wide range of mechanisms for ranking and categorization, including sentiment analysis and topic detection. As a case study, FSA-SPARQL has been used to query three well-known social networks: Twitter, Foursquare and TMDb.