Fuzzy Query Research Articles

Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

F-KGQA: A fuzzy question answering system for knowledge graphs

The wide usage of large-scale knowledge graphs (KGs) motivates the development of user-friendly interfaces so that knowledge graphs become more readily accessible to a larger population. Natural language-based question answering (QA) systems are widely investigated and developed in the context of KGs, which can provide users with a natural means to retrieve the information they need from KGs without expecting them to know the query language. It is very common that natural language contains linguistic terms (fuzzy terms), and fuzzy (flexible) query has been widely investigated in the context of databases. This paper contributes a QA system with fuzzy terms over KGs called f-KGQA. f-KGQA can deal with different types of questions, including simple questions, complex questions, and questions with fuzzy terms. More importantly, users are provided with a channel to flexibly define their fuzzy terms based on their understanding. Our experimental results demonstrate the effectiveness and applicability of f-KGQA in handling questions with fuzzy terms.

Tuning fuzzy SPARQL queries

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called FASILL that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the FASILL tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to FASILL transformation, data fuzzification and query customization in FSA-SPARQL become FASILL tuning problems. We have validated the approach with queries against datasets from online communities.

Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these systems have many limitations. Therefore, in order to represent and manage fuzzy data, it is necessary to have a fuzzy interrogation system to facilitate non-expert users. To solve this challenge, the paper proposes two different approaches to increase the flexibility of the fuzzy interrogation system. Firstly, based on similarity measures and fuzzy logic, we develop three fuzzy query processing algorithms for single-condition and multi-condition cases such as FQSIMSC (Fuzzy Query Sim Single Condition), FQSIMMC (Fuzzy Query Sim Multi-Condition) and FQSEM (Fuzzy Query SEM). Secondly, we combine the fuzzy clustering algorithm EMC (Expectation maximization Coefficient) and the query processing algorithm that is based on fuzzy partitions FQINTERVAL (Fuzzy Query Interval). With this approach, we not only improve query processing cost but also support applications and devices equipped with intelligent interactive function that easily interacts with the fuzzy query system. The results of our theoretical and experimental analysis, it can be seen that both the proposed methods significantly reduce the processing time and memory space for a data set (extracted from UCI) that has a fuzzy and incomplete natural element with the resulting data size being optimal

Multi-Criteria Decision-Making Framework with Fuzzy Queries for Multimedia Data Fusion

Multimedia Internet of Things (MIoT) is widely explored in many smart applications for connectivity with wireless communication. Such networks are not like ordinary networks because it has to collect a massive amount of data and are further forwarded to processing systems. As MIoT is very limited in terms of resources for healthcare, smart homes, etc., therefore, energy efficiency with reliable data transmission is a significant research challenge. As smart applications rely on bounded constraints, therefore duplicate and unnecessary data transmission should be minimized. In addition, the timely delivery of data in crucial circumstances has a significant impact on any proposed system. Consequently, this paper presents a fuzzy logic-based edge computing framework to provide cooperative decision-making while avoiding inefficient use of the sensing power of smart devices. The proposed framework can be applied to critical applications to improve response time and processing cost. It consists of the following two functional components: Firstly, it provides the automated routing process with a natural language interface at the sink node. Secondly, to ensure reasonable performance, it also transmits semantic data between sensors using fuzzy queries and security. According to the performance evaluation, the proposed framework significantly outperformed related studies in terms of energy consumption, packet overhead, network throughput, and end-to-end delay.

Architectural Framework for Development of Fuzzy Integrated Data Repository (FIDR) for Medicinal Plants in Nigeria.

Nigeria is a tropical country known to be home to a vast array of plants, most of which are plants that have been found to be medicinal and have been used in traditional healthcare delivery for centuries. Traditional Medicine Practitioners (TMPs) have been the major custodians of the knowledge of herbal medicine in Nigeria. It is noted that there is lack of documentation of the practice of herbal medicine which has resulted in the loss of a vast majority of the knowledge. The absence of a centralized digital data repository on medicinal plants and their uses in Nigeria hampers the drive for standardization, scientific validation and quality control of herbal medicine products in Nigeria. This consequently undermines the health and wellbeing of those dependent on traditional herbal medicine for their primary health care needs. Much of the claims on the efficacy of herbal medicine are found to be based on traditional beliefs and myth and have not been subjected to scientific evaluation. This accounts for the introduction of fuzzy logic into the design of an architectural framework for design of Fuzzy Integrated Data Repository that will allow access to information using sharp and fuzzy queries. This will be a resource platform that will resolve the information impasse by providing adequate and correct information to all stakeholders in the traditional herbal medicine healthcare delivery as well as foster synergy between orthodox medicine and herbal medicine practitioners, improve research that will lead to standardization and quality control of herbal medicine products in Nigeria.

BCVSE: Verifiable Searchable Encryption Scheme With Blockchain Supporting Fuzzy Query

BCVSE: Verifiable Searchable Encryption Scheme With Blockchain Supporting Fuzzy Query

Interactive system for English online vocabulary teaching based on database fuzzy query algorithm

Interactive system for English online vocabulary teaching based on database fuzzy query algorithm

Examination Database and Online Paper Forming Algorithm for Mobile Personalized Learning Test

It is crucial to advance network education exam informatization and mobile learning. One of them is the creation of a database system for exams, which contains crucial information. This study aims to analyze and solves key technologies including constraint check paper forming algorithms. This study using fuzzy query methods and spatial queries design a single exam table structure and exam papers for the exam database. The process of this study including collect and sort the complete set of GIS exams at major universities across the country, design a variety of GIS single questions, and build an exam database system. The result of this research is a personalized random paper formation system and query and spatial analysis by college and area for exam papers for the first time. It describes rich functions and stable performance by testing. The built system becomes an indispensable technical support instead of paper-based exams for information exams and scientific exams of many colleges and universities.

MM-FOOD: a high-dimensional index structure for efficiently querying content and concept of multimedia data

The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure’s construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large.

Практический метод реализации нечетких запросов для реляционных баз данных

Information systems that use databases are flexible to the extent that they allow users to request the data they need. SQL is limited to precise data processing and does not directly express fuzzy concepts of natural language. Therefore, giving SQL some flexibility can help users improve interaction with information systems without requiring them to learn a completely new language. The task of reducing the labor intensity of the process of integrating the mechanisms of fuzzy requests to existing information systems is urgent. The article shows the limitations of clear queries, considers various forms of fuzzy queries. Known approaches to implementing fuzzy queries to relational databases were analyzed. Provides a detailed analysis of fuzzy queries, as well as their conversion to standard SQL queries using MySQL. The proposed method for implementing the ability to work with fuzzy queries is based on expanding a clear database with stored functions, without changing the structure and composition of its tables. The advantages of the method are: increased readability and understanding of SQL queries; ease of integration with existing databases of information systems; flexible adjustment of the linguistic variable membership function in accordance with the needs of the database user. The application of the method is shown by the example of adapting a MySQL database. The proposed adaptation method can be widely used to implement fuzzy queries to databases of various DBMS that support work with stored functions.

Dynamic Change Characteristics of Soil Moisture and Its Relationship with Precipitation in Hani Rice Terraces Water Source Area

Precipitation in the Hani Rice Terraces water source area is redistributed through the soil, providing a continuous water flow to the terraces downstream and keeping a healthy regional ecosystem. The dynamic characteristics of soil moisture and its relationship with precipitation need to be further investigated. In this study, we examined the dynamics of soil moisture content (SMC) in different site types (based on vegetation), at varying depths, and at varying time scales from 2012 to 2016, using time-series data. Fitting and fuzzy query methods were used to determine the greatest positive correlation between daily/accumulated precipitation and SMC. The results showed that: (1) the dynamic characteristics of soil moisture at depths between 0–100 cm substantially varied between three site types; (2) the relationship graphs of Pearson’s coefficient r between SMC and prior 0–360 day’s daily precipitation were sine curves in three site types; the daily precipitation in the half a year before the test day has a positive effect on SMC at 0–100 cm depth, and the 2–3 month’s daily precipitation has the greatest effect from the test day’s SMC; (3) the influence of daily precipitation on SMC is limited, while cumulative precipitation is the main factor affecting SMC; and (4) there is a linear relationship between accumulated precipitation and SMC. A linear equation was constructed using the strongest correlation and was used to predict SMC. These models are useful to assess the sustainability of the water supply to the terraces downstream, and also to provide a basic understanding of the effect of different sites (by vegetation type) on soil moisture. This study is of great significance for the protection and sustainable utilization of Hani Rice Terraces.

Efficient Query Processing For Imprecise Data

In real world applications we often need to test the queries based on fuzzy data. For example, some one can specify as “find students’ whose age is around 17 years old.”; “find tall person”. “find employee with high salary”; “find country with low population” etc. This fuzziness in measurement is captured in this paper. To test such fuzzy queries, we have developed an algorithm that is applicable universally to any type of database. In this paper first we have designed architecture to test fuzzy query. In the architecture we have defined an algorithm to find the membership value for each tuple of the relation based on the fuzzy attributes on which fuzzy query is made. Next Decision Maker (DM) will supply a threshold value or -cut based on which corresponding SQL of the given fuzzy query will be generated. This SQL will retrieve the resultant tuples from the database. Finally we have tested our algorithm with an example.

A Multi-Factor Classification Framework for Completing Users’ Fuzzy Queries (Student Abstract)

Intent identification is the key technology in dialogue system. However, not all online queries are clear or complete. To identify users' intents from those fuzzy queries accurately, this paper proposes a multi-factor classification framework on the query level. Experimental results on our online serving system JIMI demonstrate the effectiveness of our proposed framework.

A Fuzzy Query Method Oriented to Knowledge Graph

With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the fuzzy query of the knowledge graph, this paper introduces the unclear expansion syntax of aggregate function based on summarizing and analyzing the existing SPARQL fuzzy expansion. Supplements the qualitative preference analysis method then proposes and SPARQL1. 1 Corresponding fuzzy SPARQL1.1 (f-SPARQL1.1) simultaneously provides strategies and techniques for rewriting ambiguous queries into classic SPARQL queries. In addition, this article also provides a visual interface for defining the membership functions of fuzzy terms and provides three interactive methods for setting fuzzy membership functions. Compared with the previous fuzzy query methods for RDF models, the method proposed in this paper has more vital fuzzy query ability and more flexible universality. The experimental results show the usability of the method proposed in this paper.

For Fuzzy Classification of Databases with Fuzzy Classification Query Language

Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Based on this, we develop the Fuzzy Classification Query Language, FCQL, which enables fuzzy queries to the extended database schema using linguistic variables and converts them into SQL statements to the database. With this, we give the user a data mining tool so that he can start extended queries on his databases based on a pre-defined fuzzy classification and obtain an improved basis for decision making. As a result, the fuzzy classification query language enables marketers to improve customer value, launch useful programs, automate overall customization, and refine business campaigns.

Modeling and Querying Fuzzy SOLAP-Based Framework

Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analytical processing (OLAP), developed for this purpose, provides appropriate data structures and supports querying multidimensional numeric and alphanumeric data. However, uncertainty and fuzziness are inherent in the data in many complex database applications, especially in spatiotemporal database applications. Therefore, there is always a need to support flexible queries and analyses on uncertain and fuzzy data, due to the nature of the data in these complex spatiotemporal applications. FSOLAP is a new framework based on fuzzy logic technologies and spatial online analytical processing (SOLAP). In this study, we use crisp measures as input for this framework, apply fuzzy operations to obtain the membership functions and fuzzy classes, and then generate fuzzy association rules. Therefore, FSOLAP does not need to use predefined sets of fuzzy inputs. This paper presents the method used to model the FSOLAP and manage various types of complex and fuzzy spatiotemporal queries using the FSOLAP framework. In this context, we describe how to handle non-spatial and fuzzy spatial queries, as well as spatiotemporal fuzzy query types. Additionally, while FSOLAP primarily includes historical data and associated queries and analyses, we also describe how to handle predictive fuzzy spatiotemporal queries, which typically require an inference mechanism.

A fuzzy database engine for mongoDB

Big Data are a paradigm through which valuable information is achieved through the analysis of a large amount of data. The sources of these data can be varied, from data streams that will be processed in real time, to the exploitation of transactional data stored in databases. For this last use, due to their scalability, the NoSQL databases, like mongoDB, a DBMS oriented to documents, have been consolidated as a powerful tool for the storage and processing of large volumes of data. On the other hand, information sources for Big Data algorithms can contain imprecise information, and the way to obtain, aggregate and present results can have an imprecise nature as well. For this reason, it is useful to provide fuzzy extensions to these DBMSs. In the case of MongoDB, there are few proposals and not very complete. This paper describes fzMongoDB, a fuzzy database engine that provides the mongoDB database with the capacity to store documents with imprecise information and to retrieve them in a flexible way. It is implemented and integrated on the mongoDB server using the resources it provides. The model and implementation of fzMongoDB also includes an indexing mechanism that accelerates the retrieval process on fuzzy queries. Also, the performance of these indexing mechanisms is evaluated.

PeGraph: A System for Privacy-Preserving and Efficient Search Over Encrypted Social Graphs

With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud’s well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users’ preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

For fuzzy classification of databases with FCQL

Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Therefore, we extend the relational database schema with a context model for fuzzy classification. Based on this, we develop the fuzzy classification query language, FCQL, which allows fuzzy queries to the extended database schema using linguistic variables and converts them into SQL calls to the database. With this we give the user a data mining tool so that he can start extended queries on his databases based on a predefined fuzzy classification and obtain an improved basis for decision-making.