Implementing imperfect information in fuzzy databases

Information in real-world applications is often vague, imprecise and uncertain. Ignoring the inherent imperfect nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several substantial information, which may be very useful in several data-intensive applications. In database context, several fuzzy database models have been proposed. In these works, fuzziness is introduced at different levels. Common to all these proposals is the support of fuzziness at the attribute level. This paper proposes first a rich set of data types devoted to model the different kinds of imperfect information. The paper then proposes a formal approach to implement these data types. The proposed approach was implemented within a relational object database model but it is generic enough to be incorporated into other database models.ou

Design Model of Application Measurement Imperfect Information to Procesing Data Surveys Level of Website Learning With Fuzzy Query Basis Data Method

Abstract— Mastery of information technology applied in the design of information systems in the form of the web at this time becomes an absolute necessity in implementing business processes of an institution and organization. The level of students 'ability in information systems in web design is a goal to increase students' competitive value in global trading climate. In an effort to increase the mastery of students in designing a web needs to measure the level of mastery, so that the material evaluation of lecturers in the process of teaching and learning activities, especially web courses. Method Fuzzy Query Database is one method to measure the level of imperfect data and information precision (Imperfect Information). In the process of survey level mastery of programming materials and web design data collected not only the exact data but it can be data that contains doubt, imperfection and uncertainty so that in the process of decision-making occurs imperfect information so ineffective and accurate. This research is expected to assist computer lecturers in evaluating the achievement of learning, lecture materials and teaching techniques in the lecture hall

MỘT PHƯƠNG PHÁP XỬ LÝ TRUY VẤN CON TRONG CƠ SỞ DỮ LIỆU MỜ THEO CÁCH TIẾP CẬN ĐẠI SỐ GIA TỬ

Trong bài báo này, chúng tôi giới thiệu ngôn ngữ truy vấn con để thao tác dữ liệu trong mô hình cơ sở dữ liệu mờ theo cách tiếp cận đại số gia tử. Ngôn ngữ thao tác dữ liệu được đề xuất phù hợp với mô hình cơ sở dữ liệu mờ theo cách tiếp cận mới. Các phương pháp biến đổi truy vấn con thành truy vấn tương ứng cũng được đề xuất trong bài báo này

A fuzzy approach to similarity in Case-Based Reasoning suitable to SQL implementation

The aim of this paper is to formally introduce a notion of acceptance and similarity, based on fuzzy logic, among case features in a case retrieval system. This is pursued by rst reviewing the relationships between distance-based similarity (i.e. the standard approach in CBR) and fuzzy-based similarity, with particular attention to the formalization of a case retrieval process based on fuzzy query specication. In particular, we present an approach where local acceptance relative to a feature can be expressed through fuzzy distributions on its domain, abstracting the actual values to linguistic terms. Furthermore, global acceptance is completely grounded on fuzzy logic, by means of the usual combinations of local distributions through specic dened norms. We propose a retrieval architecture, based on the above notions and realized through a fuzzy extension of SQL, directly implemented on a standard relational DBMS. The advantage of this approach is that the whole power of an SQL engine can be fully exploited, with no need of implementing specic retrieval algorithms. The approach is illustrated by means of some examples from a recommender system called MyWine, aimed at recommending the suitable wine bottles to a customer providing her requirements in both crisp and fuzzy way

Supporting Uncertainty in Standard Database Management Systems

Management of uncertain data in numerous real life applications has attracted the attention of database and artificial intelligent research communities. This has resulted in development of new database management systems (DBMS) in which uncertainty is treated as first class citizens. We follow a different approach in this thesis and develop a system (to which we refer as DBMS with Uncertainty, or UDBMS) which is capable of representing and manipulating uncertain data at the application level on top of a standard relational DBMS. Compared to the first approach which treats uncertainty as its first class citizens, the proposed approach may be considered as “light weight” because it is built upon existing database technologies. As the underlying uncertainty formalism, we consider the Information Source Tracking (IST) method, which is essentially probabilistic. We extend the standard SQL language with uncertainty (to which we refer as USQL), to express queries and transactions in our context. The query processing and optimization techniques are extended accordingly to take into account the presence of uncertainty. To evaluate the performance of UDBMS, we conducted extensive experiments using USQL queries and IST relations obtained by extending the standard TPC-H benchmark queries and generated data. We compare and discuss the two approaches mentioned for uncertainty management. Our results indicate that the performance of the proposed UDBMS is reasonably good when the relations involved can be loaded completely into the main memory

Knowledge Discovery Based Simulation System in Construction

Uncertainty is an entrenched characteristic of most construction projects. Typically, probability distributions are utilized to accommodate uncertainty when estimating duration of project’s activities. Distributions are fitted, based on the collected data from construction projects, to estimate activity durations, to assess productivity and cost, and to identify resource bottlenecks using simulation. The subjectivity in selecting these fitted probability distributions is an imprecise process and may significantly affect simulation outputs. Most research works in simulating construction operations has focused predominantly on modeling and has neglected to study the effect of subjective variables on simulation process. Therefore, there is a need for a system, which: (1) handles uncertainty, fuzziness, missing data, and outliers in input data, (2) effectively utilizes historical data, (3) models the effect of qualitative and quantitative variables on the simulation process, (4) enhances simulation modeling capabilities, and (5) optimizes simulation system output(s). The main objective of this research is to develop a knowledge discovery based simulation system for construction operations, which achieves the abovementioned necessities. This system comprises three stages: (i) a Knowledge Discovery Stage (KDS), (ii) a Simulation Stage (SS), and (iii) an Optimization Stage (OS). In the KDS, raw data are prepared for the SS where patterns, which represent knowledge implicitly stored or captured in large databases, are extracted using Fuzzy K-means technique. During the KDS, the effect of qualitative and quantitative variables on construction operation(s) is modelled using Fuzzy Clustering technique. This stage improves the efficiency of data modeling by 10% closer to real data. The movement of units is modeled in the SS where the interaction between flow units and idle times of resources can be examined to discover any bottlenecks and estimate the operation's productivity and cost. The OS, using Pareto ranking technique, assists in selecting and ranking feasible productivity-cost solution(s) for diverse resource combinations under different conditions. An automated general purpose construction simulation language (KEYSTONE) is developed using C#. The developed system is validated and verified using several case studies with sound and satisfactory results, i.e. 4% - 11% digression. The developed research/system benefits both researchers and practitioners because it provides robust simulation modeling tool(s) and optimum resources allocation for construction operations

Efficient Processing of Nested Fuzzy SQL Queries in a Fuzzy Database

... In this paper, we extend the unnesting techniques to process several types of nested fuzzy queries. An extended merge-join is used to evaluate the unnested fuzzy queries. As shown by both theoretical analysis and experimental results, the unnesting techniques with the extended merge-join significantly improve the performance of evaluating nested fuzzy queries