Inconsistent Information Systems Research Articles

Implementation of Application Specific Instruction set Processor for Approximate Computing

The performance, energy efficiency, power dissipation, and cost of the processors used in Internet of Things edge devices can be improved by introducing approximate computing. In this paper, the design and synthesis of an application-specific instruction set processor for approximate computing is proposed. Approximate computing can also be defined by using Artificial Intelligence-based technique like the Fractal theory and Inconsistent Information System. Application Specific Instruction Set Processor is a new processor design area and is used to design a new instruction set using existing processor configuration available on the EDA tool. From the results, it is observed that significant improvement is achieved in processor performance using the TIE application as compared to without the TIE application which is applied to user-designed instruction.

DESIGNING ARCHITECTURE TECHNOLOGY BASED CLOUD COMPUTING IN BUILDING MATERIALS MANUFACTURER

Users can access the necessary services from anywhere and at any time thanks to the cloud computing technology, which places the majority of processing and computing on the Internet. However, in practice, each business develops its own information systems architecture and requirements. This brings up a number of issues, including the potential for redundant information systems, the lack of data structure standards, the inability of information systems to consolidate their data, and inconsistent information system development. The motivation behind developing a model of information technology architecture using the idea of cloud computing stems from some of these issues. A cloud-based information technology architectural model was created in line with the requirements and needs of the organization based on the analysis of the company's state and the use of the chosen framework. Another Indonesian company is anticipated to benefit from the research, primarily as a resource for constructing the information technology architecture.

Concept drift detection based on decision distribution in inconsistent information system

Concept drift has been a widely concerned problem in data analysis and many important achievements have been obtained. However, there are few discussions on concept drift detection in inconsistent information system that is very common in various application fields. In this paper, a concept drift detection algorithm based on the decision distribution (IDP-DD) is proposed. To begin, we present the notion of decision distribution function by means of inclusion degree and delineate the decision empirical vector. Subsequently, we expound upon decision empirical distance and analyze the limits characteristics of sampling; thirdly, we display the algorithm implementation process of IDP-DD. Lastly, we evaluate the efficacy of IDP-DD with a some commonly employed datasets and a real-word example dataset. Theory analysis and experiment verification indicate that the algorithm IDP-DD can not only quantify the severity of concept drift, but also visually present the region where concept drift occurs. At the same time, combined with sampling method, this algorithm does not need to traverse all samples at one time, so the complexity and calculation cost can be effectively reduced. So IDP-DD is more stable and can effectively facilitate data-driven decision problems.

Efficient feature selection for inconsistent heterogeneous information systems based on a grey wolf optimizer and rough set theory

Efficient feature selection for inconsistent heterogeneous information systems based on a grey wolf optimizer and rough set theory

Matrix approach to spanning matroids of rough sets and its application to attribute reduction

Rough set theory is a granular computing tool used to deal with ambiguity and uncertainty in information systems. However, many optimization problems in rough sets, such as attribute reduction, are NP-hard problems, and most of the algorithms for these problems are greedy. As a complex mathematical structure, matroids provide a powerful tool for solving combinatorial optimization problems related to attribute reduction. Therefore, it is necessary to study the combination of matroids and rough sets. This paper uses rough sets and matrix approaches to spanning matroids. Moreover, the features of spanning matroids are applied to attribute reduction in decision information systems. Firstly, we construct spanning sets based on equivalence relations which can induce matroids called spanning matroids. Secondly, some features of spanning matroids, such as closed sets, bases, are studied by matrix method. Finally, the judgment theorems with the features of spanning matroids are proposed for addressing the problems about attribute reduction in decision information systems. Simultaneously, the sufficient and necessary conditions for distinguishing upper approximation reduction in inconsistent decision information systems are obtained from the viewpoint of spanning matroids.

Eliminating medicine waste in a Finnish university hospital \u2014 a qualitative study

BackgroundMedicine waste in hospitals leads to severe economic loss. This waste emerges for a number of reasons. Medicines are often ordered in too large quantities, which leads to stock expiring without being dispensed. Wastage can also be a consequence of poor management practices. Technical aids, such as automatic dispensers, have been suggested to reduce waste, but they too have shortcomings. Information systems can arguably contribute to waste reduction, but this area has not been widely researched.In this exploratory case study, we scrutinized the management of medicines waste in a hospital from an information systems perspective and examined how information systems are used to manage the medicine supply chain and medicine waste. Our research case was a Finnish university hospital, its central pharmacy, and, more widely, the medicine supply chain within the hospital.MethodsThis is a qualitative case study, based on data gathered through interviews and a survey and a review of other information sources, including annual reports and other relevant collateral. The study participants included pharmacy staff members and other hospital staff involved in medicine supply. The interviews were conducted in two rounds, first capturing the main themes and then exploring them further in the later study stages.ResultsThe findings outline a picture of unfit technology and inconsistent and unreliable information. This is compensated for by manual practices and processes that cause an excessive administrative burden and ultimately increased wastage. An infrequent ordering process combined with the lack of recycling practices increase the wastage even more.ConclusionMedicine supply and waste management remain a manual administrative task. Inconsistent information and unfit information systems make this task challenging, and the process relies on the medicine supply staff’s experience and assumptions.

Lower Approximation Reduction Based on Discernibility Information Tree in Inconsistent Ordered Decision Information Systems

Attribute reduction is an important topic in the research of rough set theory, and it has been widely used in many aspects. Reduction based on an identifiable matrix is a common method, but a lot of space is occupied by repetitive and redundant identifiable attribute sets. Therefore, a new method for attribute reduction is proposed, which compresses and stores the identifiable attribute set by a discernibility information tree. In this paper, the discernibility information tree based on a lower approximation identifiable matrix is constructed in an inconsistent decision information system under dominance relations. Then, combining the lower approximation function with the discernibility information tree, a complete algorithm of lower approximation reduction based on the discernibility information tree is established. Finally, the rationality and correctness of this method are verified by an example.

Attribute reductions in an inconsistent decision information system

Attribute reductions in an inconsistent decision information system

A prediction method for the cellphone replacement: Rough set-based analysis

Owing to the communication customers’ data are large scale, inconsistent and incomplete, it is hard to predict who will replace their cellphones in the near future. In order to solve this problem, a prediction method based on dominance-based rough set approach (DRSA) is proposed in this paper. Firstly, the customers’ features are extracted based on experts’ knowledge. So, we get the customer decision table. After that, the maximum certainty of each class union is used as the corresponding variable threshold value to control the decision rule extraction program. Therefore, the extracted decision rules can reflect the data characteristics effectively. In this process, it can learn knowledge automatically without setting the threshold values of consistency level depending on prior domain knowledge or by a complex tail-and-error procedure. Additionally, it strengthens the performance for dealing with inconsistent information systems. The efficiency of this method is illustrated by simulation experiments.

Generalized binary discernibility matrix for attribute reduction in incomplete information systems

AbstractTo extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.

Information sharing and its integrative role

Purpose The purpose of this paper is to describe the volume and quality of information and communication channel use at various stages of the malt barley value chain (MBVC) in Ethiopia and to investigate how metrics of these variables influence the extent of integration of the chain. Design/methodology/approach The study is based on survey data collected from 320 farmers and 100 traders and interview responses compiled from 76 respondents. Descriptive statistics and ordered logistic regression were used for data analysis. Findings The descriptive statistics show a lower volume and poor quality of information is being shared at farmer-trader interface and that value chain integration (VCI) is weak at all studied interfaces. Results of ordered logistic regression show that information volume and quality positively influence VCI, whereas a positive relationship between channel use and VCI was found only at farm level interfaces. Evidences found suggested that inconsistent information systems, lack of information sharing plans, low level of members’ awareness about the value of information, and lack of trust to share information were factors that inhibited information sharing in the MBVC. Originality/value The study offers pioneering evidence of the relative role of information volume and quality and channel use as factors that influence the extent of integration of the value chain.

Attribute reduction of set-valued decision information system based on dominance relation

A new dominance relation is defined in consistent set-valued information systems based on similarity grade of attribute value sets. The problem of attribute reduction and the judgment in consistent set-valued decision information system are studied, from which a new approach to attribute reductions is provided in consistent set-valued decision information system. The assignment reduction is introduced in inconsistent set-valued decision information system in this paper, and it is shown that the inconsistent system can be turned into a generalized consistent approximation space. Furthermore, the influence of similarity level on attribute reduction is discussed.

사후확률에 기반한 근사 규칙의 생성

In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.

Optimal decision of multi-inconsistent information systems based on information fusion

This paper discusses the fusion of multi-inconsistent decision information systems. First, basic definitions and properties of rough sets, belief and plausibility functions are reviewed. Then, conditional mass function, conditional belief function and conditional plausibility function based on the decision set are defined. We then study the optimal decision of a test set and the reduction of an inconsistent decision information system based on the conditional mass function. Meanwhile, conditional mass function, conditional belief function and conditional plausibility function based on the conditional attribute set are also discussed and we define an uncertainty degree of an inconsistent information system based on the quasi-probability measure. Further, we study fusion method of inconsistent decision information systems using conditional mass functions based on a decision set. Finally, we define a conditional uncertainty measure and give a method to obtain the optimal decision and the confidence level.

Allocation Reductions in Inconsistent Decision Tables Based on Dominance Relations

In recent years, many methods have been proposed in order to deal with inconsistent information systems based on indiscernibility relations in rough set theory. However, a little attention has been paid to inconsistent ordered decision tables. In this paper, the concept of allocation reductions is proposed in inconsistent ordered decision tables. And an approach to computing this kind of reductions is then presented by introducing the discernibility matrix and discernibility function. Moreover, the relationship is investigated between the allocation reduction and the ≥-upper (≤-lower) approximate distribution reduction in inconsistent ordered decision tables with a single decision attribute. A fictitious numerical example is employed to substantiate the conceptual argument throughout the paper.

베이지언 정보엔트로피에 의한 불완전 의사결정 시스템의 불확실성 향상

Abstract Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system. Keywords : Rough Set, Indiscernibility Relation, Conditional Information Entropy, Uncertainty, Bayesian Theory * 종신회원, 청운대학교 컴퓨터학과 ** 정회원, 중부대학교 컴퓨터학과 (교신저자)접수일자: 2014년 11월 11일, 수정일자: 2014년 12월 9일게재확정일자: 2014년 12월 12일 Received: 11 November, 2014 / Revised: 9 December, 2014Accepted: 12 December, 2014

Automatic determination about precision parameter value based on inclusion degree with variable precision rough set model

The rough set theory provides a powerful approach for attributes reduction and data analysis. The variable precision rough set (VPRS) model, an extension of the original rough set approach, tolerates misclassifications of the training data to some degree, which promotes the applications of rough set theory in inconsistent information systems. However, in most existing algorithms of feature reduction based on VPRS, the precision parameter (β) is introduced as prior knowledge, which restricts their applications because it is not clear how to set the β value. By studying β-consistency in the measurement of a decision table and the threshold value of the β-consistent decision table, this paper presents an algorithm for automatic determination of the precision parameter value from a decision table based on VPRS. At the same time, the precision parameter value from our proposed method is compared with the thresholds from the decision-theoretic rough set (DTRS). The influences of the precision parameter are also discussed on attribute reduction, which shows the necessity of the estimated precision parameter from a decision table. The simulation results including VPRS and other classification methods in real data further indicate that different precision parameter values make a great difference on rules and setting a precise parameter near the threshold value of the β-consistent decision table can precisely reflect the decision distribution of the decision table.

Knowledge Reduction of Set-Valued Decision Information Systems Based on Tolerance Relation

Knowledge reduction is one of the main problems in the study of rough set theory. This paper deals with knowledge reduction of set-valued decision information systems based on tolerance relation. The concepts of distribution reduct based on set-valued decision information systems is introduced. The judgement theorems and discernibility matrices associated with the tolerance relation were examined, from which we can obtain the specific operation methods to inconsistent set-valued decision information systems.

Rule Acquisition Based on Inconsistent Decision-Making Information System

Rule acquisition is a hot topic in the field of data mining. And the inconsistent information systems are widespread nowadays. However, rules acquisition methods are always the difficulty of rough set theory application in inconsistent decision information systems; So the paper proposes a new rule acquisition method. Firstly, we use maximum distribution reduction method for knowledge reduction in single decision-making inconsistent information system and then we use decision-making resolution matrix and decision-making matrix function to get the decision rules. Finally, we mine the rules from inconsistent decision-making information systems.

KNOWLEDGE REDUCTION IN LATTICE-VALUED INFORMATION SYSTEMS WITH INTERVAL-VALUED INTUITIONISTIC FUZZY DECISION

In this paper, associated with dominance relation, lattice theory and intuitionistic fuzzy sets theory, the lattice-valued information systems with interval-valued intuitionistic fuzzy decision are proposed and some of its properties are investigated carefully. And, an approach to knowledge reduction based on discernibility matrix in consistent lattice-valued information systems with interval-valued intuitionistic fuzzy decision is constructed and an illustrative example is applied to show its validity. Moreover, extended from the idea of knowledge reduction in consistent information systems, four types of reductions and approaches to obtaining the knowledge reductions of the inconsistent lattice-valued information systems with interval-valued intuitionistic fuzzy decision are formulated via the use of discernibility matrix. Furthermore, examples are considered to show that the approaches are useful and effective. One can obtain that the research is meaningful both in theory and in application for the issue of knowledge reduction in complex information systems.