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Query Tuning in Semantic Inference Fuzzy Logic Algorithm for Real-Time Recommendations
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During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making.
Abstract: Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions.These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO,CO2 and O2 vis- à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy
logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws,policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.
This article presents various forms of fuzzy queries, a detailed analysis of these queries and their conversion into standard SQL queries using Oracle 11g XE. The actions discussed above point out to the methods of obtaining fuzzy information from the database that have been easy to implement. This research takes into account the fact that obtaining this type of information is not supported by any commercial database management system.
Selection of suitable sites for solar power plants requires spatial evaluation taking technical, economic, and environmental considerations into account. This research has applied a fuzzy logic model to carry out spatial site selection for solar power plants in Markazi Province of Iran. Geographical Information System (GIS) capabilities have been used for spatial analysis and visualization of the research results. The suitable areas for solar power plants installation were identified by employing Boolean logic and defining a range of selection criteria all of which were evaluated by fuzzy functions. The raster layers of Boolean and fuzzy logic have been combined to come up with suitable sites with solar energy potential. The results identified some areas in the vicinity of Mahalat and Zarandineh cities as suitable for solar energy utilization. The research also validated the employed combined method as a suitable site selection approach for solar power plants.
A popular method in machine learning for supervised classification is a decision tree. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver. We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data.
This article presents various forms of fuzzy queries, a detailed analysis of these queries and their conversion into standard SQL queries by means of Oracle 11g XE. A qualitative and quantitative study about the use of fuzzy queries on relational databases has been included in this article, as well. Given that fuzzy queries arrange results according to the degree in which they meet the conditions of the query, it is easier to analyze the results and the risk of obtaining an empty result is reduced thanks to an extended interpretation of the conditions of the query. The provided examples of conversion of fuzzy queries into standard SQL queries by means of Oracle 11g XE point to easy to implement methods of obtaining fuzzy information from the database, and thereby expand its functionality.
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems.
In many cases information is found to be naturally fuzzy or imprecise, that's why fuzzy query systems have become indispensable to represent and manage this information and especially facilitate interrogation to a non-expert user. In this paper, we present a brief study of fuzzy querying relational databases, we start by the design and operation of a fuzzy system, and we present different types architectures of fuzzy interrogation systems of database. Finally, a comparison of most relevant characteristic in fuzzy query systems of database is included in this work, as well.
We propose an approach for indexing fuzzy data based on inverted files that speeds up retrieval considerably by stopping the traversal of postings lists early. This is possible because the entries in the postings lists are organized in a way that guarantees that there are no matching items beyond a certain point in a list. Consequently, we can reduce the number of false positives significantly, leading to an increase in retrieval performance. We have implemented our approach and evaluated it experimentally, including a test on skewed and real-world data, comparing it to an approach that has previously been shown to be superior to other methods.
Data stream management systems usually have to process many long-running queries that are active at the same time. Multiple queries can be evaluated more efficiently together than indepen- dently, because it is often possible to share state and computa- tion. Motivated by this observation, various Multi-Query Opti- mization (MQO) techniques have been proposed. However, these approaches suffer from two limitations. First, they focus on very specialized workloads. Second, integrating MQO techniques for CQL-style stream engines and those for event pattern detection en- gines is even harder, as the processing models of these two types of stream engines are radically different. In this paper, we propose a rule-based MQO framework. This framework incorporates a set of new abstractions, extending their counterparts, physical operators, transformation rules, and streams, in a traditional RDBMS or stream processing system. Within this framework, we can integrate new and existing MQO techniques through the use of transformation rules. This allows us to build an expressive and scalable stream system. Just as relational optimizers are crucial for the success of RDBMSes, a powerful multi-query optimizer is needed for data stream processing. This work lays the foundation for such a multi-query optimizer, creating opportunities for future research. We experimentally demonstrate the efficacy of our approach.
Query optimisation is a significant unsolved problem in the development of multidatabase systems. The main reason for this is that the query cost functions for the component database systems may not be known to the global query optimiser. In this paper, we describe a method, based on a classical clustering algorithm, for classifying queries which allows us to derive accurate approximations of these query cost functions. The experimental results show that the cost functions derived by the clustering algorithm yield a lower average error as compared to the error produced by a manual classification.
This paper is concerned with techniques for fuzzy query processing in a database system. By a fuzzy query we mean a query which uses imprecise or fuzzy predicates (e.g. AGE = “VERY YOUNG”, SALARY = “MORE OR LESS HIGH”, YEAR-OF-EMPLOYMENT = “RECENT”, SALARY ⪢ 20,000, etc.). As a basis for fuzzy query processing, a fuzzy retrieval system based on the theory of fuzzy sets and linguistic variables is introduced. In our system model, the first step in processing fuzzy queries consists of assigning meaning to fuzzy terms (linguistic values), of a term-set, used for the formulation of a query. The meaning of a fuzzy term is defined as a fuzzy set in a universe of discourse which contains the numerical values of a domain of a relation in the system database.The fuzzy retrieval system developed is a high level model for the techniques which may be used in a database system. The feasibility of implementing such techniques in a real environment is studied. Specifically, within this context, techniques for processing simple fuzzy queries expressed in the relational query language SEQUEL are introduced.
Using query language for dealing with databases is always a professional and complex problem. This complexity causes the userpsilas usage of data existing in database limits to use definite reports there are in some pre implemented softwares. However, you can create this opportunity that each none professional user transfers his questions and requirements to computer in natural language and derives his desired data by natural language processing. In this paper we represent a method for building a ldquonatural languages interfaces to data basesrdquo (NLIDB) system. This system prepares an ldquoexpert systemrdquo implemented in prolog which it can identify synonymous words in any language. It first parses the input sentences, and then the natural language expressions are transformed to SQL language.
This paper investigates and extends the use of fuzzy relation
equations for the representation and study of fuzzy inference systems.
Using the generalized sup-t (t is a triangular norm) composition of
fuzzy relations and the study of sup-t fuzzy relation equations,
interesting results are provided concerning the completeness and the
theoretical soundness of the representation, as well as the ability to
mathematically formulate and satisfy application-oriented design
demands. Furthermore, giving a formal study of fuzzy partitions and some
useful aspects of fuzzy associations and fuzzy systems, the paper can be
used as a theoretical background for designing consistent fuzzy
inference systems
An important issue in extending database management systems
functionalities is to allow the expression of imprecise queries to
enable these systems to satisfy the user needs more closely. This paper
deals with imprecise querying of regular relational databases. The basic
idea is to extend an existing query language, namely SQL. In this
context, two important points must be considered: one concerns the
integration in the extended language of many propositions that have been
made elsewhere, in particular those concerning fuzzy aggregation
operators; and the second point is to know whether the equivalences
which are valid in SQL still hold in the extended language. Both these
topics are investigated in this paper
Two fuzzy database query languages are proposed. They are used to query fuzzy databases that are enhanced from relational databases in such a way that fuzzy sets are allowed in both attribute values and truth values. A fuzzy calculus query language is constructed based on the relational calculus, and a fuzzy algebra query language is also constructed based on the relational algebra. In addition, a fuzzy relational completeness theorem such that the languages have equivalent expressive power is proved
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model
Fuzzy Logic Applications for Decision Support
- S Kusumadewi
- H Purnomo
Dynamic Query Refinement for Interactive Data Exploration
- Kalinin
Dynamic Query Refinement for Interactive Data Exploration
- Alexander Kalinin
- Ugur
- Cetintemel
A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments
- S V Ahn
- A Couture
- K Cuzzocrea
- G M Dam
- C K Grasso
- K L Leung