Zhongzhi Shi

THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, rough entropy and knowledge granulation in rough set theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in rough set theory.

Information entropy, rough entropy and knowledge granulation in incomplete information systems

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances that are characterized by vagueness and uncertainty. Rough set theory uses a table called an information system, and knowledge is defined as classifications of an information system. In this paper, we introduce the concepts of information entropy, rough entropy, knowledge granulation and granularity measure in incomplete information systems, their important properties are given, and the relationships among these concepts are established. The relationship between the information entropy E(A) and the knowledge granulation GK(A) of knowledge A can be expressed as E(A)+GK(A) = 1, the relationship between the granularity measure G(A) and the rough entropy E r(A) of knowledge A can be expressed as G(A)+E r(A) = log2|U|. The conclusions in Liang and Shi (2004) are special instances in this paper. Furthermore, two inequalities − log2 GK(A) ≤ G(A) and E r(A) ≤ log2(|U|(1 − E(A))) about the measures GK, G, E and E r are obtained. These results will be very helpful for understanding the essence of uncertainty measurement, the significance of an attribute, constructing the heuristic function in a heuristic reduct algorithm and measuring the quality of a decision rule in incomplete information systems.

The Semantic Web – ASWC 2006

Invited Talks.- The Semantic Web: A Network of Understanding.- Transformation from OWL Description to Resource Space Model.- Next Generation Semantic Web Applications.- Annotation.- Hierarchical Topic Term Extraction for Semantic Annotation in Chinese Bulletin Board System.- Automatic Annotation Using Citation Links and Co-citation Measure: Application to the Water Information System.- Semantic Annotation Using Horizontal and Vertical Contexts.- Semantic Wiki as a Lightweight Knowledge Management System.- Ontology Alignment.- Partition-Based Block Matching of Large Class Hierarchies.- Towards Quick Understanding and Analysis of Large-Scale Ontologies.- Matching Large Scale Ontology Effectively.- Finding Important Vocabulary Within Ontology.- Document and Recommendation.- Ontology-Based Similarity Between Text Documents on Manifold.- A Formalism of XML Restructuring Operations.- FTT Algorithm of Web Pageviews for Personalized Recommendation.- Social Network and RSS.- D-FOAF: Distributed Identity Management with Access Rights Delegation.- Community Focused Social Network Extraction.- Behavioral Analysis Based on Relations in Weblogs.- UniRSS: A New RSS Framework Supporting Dynamic Plug-In of RSS Extension Modules.- Ontology Integration and Interoperability 1.- Ontology-Based RBAC Specification for Interoperation in Distributed Environment.- Business Process Collaboration Using Semantic Interoperability: Review and Framework.- An Ontology Architecture for Integration of Ontologies.- Automatic Alignment of Ontology Eliminating the Probable Misalignments.- Ontology Integration and Interoperability 2.- Semantic Integration of Enterprise Information: Challenges and Basic Principles.- Application Integration Using Conceptual Spaces (CSpaces).- A New Evaluation Method for Ontology Alignment Measures.- Representing and Reasoning with Application Profiles Based on OWL and OWL/XDD.- Reasoning.- OWL-Full Reasoning from an Object Oriented Perspective.- Visualizing Defeasible Logic Rules for the Semantic Web.- A Reasoning Algorithm for pD*.- Triple Space Computing: Adding Semantics to Space-Based Computing.- Application 1.- Full-Automatic High-Level Concept Extraction from Images Using Ontologies and Semantic Inference Rules.- Dental Decision Making on Missing Tooth Represented in an Ontology and Rules.- Ontology Driven Visualisation of Maps with SVG - Technical Aspects.- Applying CommonKADS and Semantic Web Technologies to Ontology-Based E-Government Knowledge Systems.- A Semantics-Based Protocol for Business Process Transactions.- Information Search.- Fuzzy View-Based Semantic Search.- A Semantic Search Conceptual Model and Application in Security Access Control.- Document Filtering for Domain Ontology Based on Concept Preferences.- Database.- Qualitative Spatial Relation Database for Semantic Web.- Automatic Creation and Simplified Querying of Semantic Web Content: An Approach Based on Information-Extraction Ontologies.- HStar - A Semantic Repository for Large Scale OWL Documents.- Minerva: A Scalable OWL Ontology Storage and Inference System.- Semantic Web Services 1.- Exploring the Flexible Workflow Technology to Automate Service Composition.- Mediation Enabled Semantic Web Services Usage.- Toward Automatic Discovery and Invocation of Information-Providing Web Services.- Automatic Composition of Semantic Web Services - A Theorem Proof Approach.- Semantic Web Services 2.- A Semantic Rewriting Approach to Automatic Information Providing Web Service Composition.- Web Services Analysis: Making Use of Web Service Composition and Annotation.- WWW: WSMO, WSML, and WSMX in a Nutshell.- Automatic Generation of Service Ontology from UML Diagrams for Semantic Web Services.- A Composition Oriented and Graph-Based Service Search Method.- Ontology and Tool.- DODDLE-OWL: A Domain Ontology Construction Tool with OWL.- Knowledge Elicitation Plug-In for Protege: Card Sorting and Laddering.- Towards a Topical Ontology of Fraud.- Application 2.- Product Data Interoperability Based on Layered Reference Ontology.- Design of Semantically Interoperable Adverse Event Reporting Framework.- Protein Data Sources Management Using Semantics.- Semantic Web Modeling for Virtual Organization: A Case Study in Logistics.- A PSO-Based Web Document Query Optimization Algorithm.- Ontology and Theory.- Modular Ontologies - A Formal Investigation of Semantics and Expressivity.- A Pi-Calculus Based Ontology Change Management.- A Comprehensive Study of Inappropriate Hierarchy in WordNet.- Autonomous Ontology: Operations and Semantics OR Local Semantics with Semantic Binding on Foreign Entity.- Peer-to-Peer.- SemreX: A Semantic Peer-to-Peer System for Literature Documents Retrieval.- Personal Information Modeling in Semantic Web.- A Semantic Reputation Mechanism in P2P Semantic Web.- Client and Server Anonymity Preserving in P2P Networks.- Industrial Track 1.- A Map Ontology Driven Approach to Natural Language Traffic Information Processing and Services.- A Knowledge- and Workflow-Based System for Supporting Order Fulfillment Process in the Build-to-Order Supply Chains.- A Distributed IR Model Based on Semantic Web.- Experimental Study of Semantic Contents Mining on Intra-university Enterprise Contents Management System for Knowledge Sharing.- Industrial Track 2.- Semantic Autocompletion.- Ubiquitous Metadata Scouter - Ontology Brings Blogs Outside.- Networked Interactive Photo Annotation and Reminiscence Content Delivery.- Task-Oriented Mobile Service Recommendation Enhanced by a Situational Reasoning Engine.

A fast approach to attribute reduction in incomplete decision systems with tolerance relation-based rough sets

Efficient attribute reduction in large, incomplete decision systems is a challenging problem; existing approaches have time complexities no less than O(|C|^2|U|^2). This paper derives some important properties of incomplete information systems, then constructs a positive region-based algorithm to solve the attribute reduction problem with a time complexity no more than O(|C|^2|U|log|U|). Furthermore, our approach does not change the size of the original incomplete system. Numerical experiments show that the proposed approach is indeed efficient, and therefore of practical value to many real-world problems. The proposed algorithm can be applied to both consistent and inconsistent incomplete decision systems.

Parallel extreme learning machine for regression based on MapReduce

Regression is one of the most basic problems in data mining. For regression problem, extreme learning machine (ELM) can get better generalization performance at a much faster learning speed. However, the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task. Through analyzing the mechanism of ELM algorithm, an efficient parallel ELM for regression is designed and implemented based on MapReduce framework, which is a simple but powerful parallel programming technique currently. The experimental results demonstrate that the proposed parallel ELM for regression can efficiently handle very large datasets on commodity hardware with a good performance on different evaluation criterions, including speedup, scaleup and sizeup.

Extreme support vector machine classifier

Instead of previous SVM algorithms that utilize a kernel to evaluate the dot products of data points in a feature space, here points are explicitly mapped into a feature space by a Single hidden Layer Feedforward Network (SLFN) with its input weights randomly generated. In theory this formulation, which can be interpreted as a special form of Regularization Network (RN), tends to provide better generalization performance than the algorithm for SLFNs--Extreme Learning Machine (ELM) and leads to a extremely simple and fast nonlinear SVM algorithm that requires only the inversion of a potentially small matrix with the order independent of the size of the training dataset. The experimental results show that the proposed Extreme SVM can produce better generalization performance than ELM almost all of the time and can run much faster than other nonlinear SVM algorithms with comparable accuracy.

FORMANAGER: an office forms management system

The form has become an important abstraction for data management in an office application environment. Structured office forms present data to users in an easily understood and easily manipulated manner. In this paper we classify forms systems in terms of three dimensions: data structuring, user interfaces, and programming interfaces. Current forms systems are analyzed under these dimensions. We have designed a comprehensive forms management system, FORMANAGER, that includes facilities for form specification, form processing, and form control. The system transforms data from a relational database into a hierarchical data structure which defines the form. The design and algorithms for implementation of the system are described, and future extensions to enhance the capabilities of forms systems are proposed.

Parallel Implementation of Apriori Algorithm Based on MapReduce

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.

Flood decision support system on agent grid: method and implementation

In this paper, we introduce the concept and architecture of agent grid. Agent grid is an intelligent platform that enables the independent operating entities (agents) to interact with one another to form dynamic services on the Grid. Under this view, we built an agent grid platform named AGrIP that includes four layers and several useful toolkits. With the platform support, we implemented the flood decision support system which combines the wireless sensor network for data acquisition and software agent technology for legacy system integration. Additionally, we developed a toolkit for programmers to visually develop software agents which makes the development process easier. Besides, the MWAC model proposed is for sensor network to save power which can transit the information for long distance. This system is now applied as a module in the city emergency interact project.

MSMiner-a developing platform for OLAP

Since the early 1970s, decision support systems (DSS) have evolved significantly. In this paper, the design and implementation of MSMiner, a developing platform for DSS, is introduced. The system is constructed on a data warehouse and integrated with a number of data mining algorithms. It is well suited for on-line analytical processing (OLAP). The characteristics of MSMiner include the ability to support multiple data sources and data mining strategies, additional organizational flexibility in regard to data and mining strategies, and the powerful expansibility of data mining tasks.