Zeng-Zhi Li

A partitional view of concept lattice

Formal concept analysis and rough set theory are two different methods for knowledge representation and knowledge discovery, and both have been successfully applied to various fields. The basis of rough set theory is an equivalence relation on a universe of objects, and that of formal concept analysis is an ordered hierarchical structure — concept lattice. This paper discusses the basic connection between formal concept analysis and rough set theory, and also analyzes the relationship between a concept lattice and the power set of a partition. Finally, it is proved that a concept lattice can be transformed into a partition and vice versa, and transformation algorithms and examples are given.

Study on qos driven web services composition

Providing composed Web Services based on the QoS requirements of clients is still an urgent problem to be solved. In this paper, we try to solve this problem. Firstly, we enhanced the current WSDL to describe the QoS of services, and then gave a way to choose the proper pre-exist services based on their QoS.

A programming model for an embedded media processing architecture

To follow rapid evolution of media processing algorithms, the latest media processing architecture enhances the execution efficiencies of media applications by adding a programmable vision processor and by improving memory hierarchy, while complicates the programming. In this paper, the features of this architecture are analyzed, the reason of inefficiency of media application implemented by general programming model is studied and SPUR programming model is proposed. In SPUR, media data and operations are expressed as media streams and corresponding operations naturally. Moreover, algorithm is divided into high-level part written by SP-C and low-level part written by UR-C. Fine-grained data parallelism are exploited explicitly as well. Experimental results show that SPUR provides programmer a novel, expressive and efficient programming way, and obviously improves readability, robustness, development efficiency and object-code quality of media applications.

Web Service Selection Algorithm Based on Particle Swarm Optimization

A novel multi-objective optimization based particle swarm optimization algorithm is presented to solve the global optimization problem for based services selecting in Web services composition technology. This algorithm takes Web services selection as a multi-objective constrained optimization problem with constraints. It introduces multi-objective PSO intelligent theory to optimize multi parameters simultaneously, and produces a set of constraints to meet the Pareto optimal solution. The experiments show that the algorithm is a feasible and efficient method for Web services selection.

A trust model based on bayesian approach

Trust plays a vital role in web-based systems in which entities act in an autonomous and flexible manner. This paper proposes a novel trust model based on Bayesian approach for web-based systems. The relationships between entities are classified into 4 kinds according to what if there are recommendations and/or direct interactions. For these 4 situations, the estimator of the successful cooperation probability (SCP) between entities is analyzed by using Bayesian approach. Finally, we take the estimator as the basis of an entity trusting in another and obtain the entitys relatively fixed cooperation system which consists of all of its potential partners in the future.

Mobile-agent-based distributed and incremental techniques for association rules

The over-growing size of data being stored in todays information systems, inevitably leads to the distributed database architectures. Moreover, many databases are distributed in nature. It is important to device efficient methods for distributed data mining. It is well known that distributed database has an intrinsic data skew property. So it is desirable to mine the global rules for the global business decisions and the local rules for the local business decision. In this paper a mobile-agent-based distributed knowledge discovery architecture has been proposed for data mining in the distributed, heterogeneous database systems. Based on this architecture a flexible and efficient mobile-agent-based distributed algorithm for association rules (IDMA) is presented that can mine the global and local large itemsets at the same time. Furthermore, when mining the local large itemsets an incremental algorithm (IAA) is employed, which utilizes a heuristic selective scan technique to reduce the number of database scans required and to keep the size of the candidate itemset sets from increasing exponential. The performance of IDMA is studied. The results show that the algorithm IDMA is valid and has superior performance.

A GA-based cache replacement policy

Caching has been recognized as an effective scheme for avoiding service bottleneck and reducing network traffic in World Wide Web. Cache replacement policy plays a key role in Web caching. This paper proposes a method based on genetic algorithm (GA) to provide a novel way for efficient cache replacement. A hybrid algorithm is chosen to work as fitness function. The cache replacement policy considers not only download time but also number of references to an object and object size. Experimental result shows that the GA-based cache replacement policy is effective.

Multi-agent based genetic algorithm for JSSP

A novel multi-agent based on genetic algorithm (GA) is proposed to solve job-shop scheduling problem (JSSP). This algorithm not only can accelerate the creation of initial population and the selection of evaluation population, but also can control the processing of selection, crossover and mutation in an intelligent way. Job-shop benchmarks are used to evaluate the efficiency and performance of the proposed algorithm. The experimental result shows it has better optimal performance.

A Lightweight Method of Web Service Ontology Merging Based on Concept Lattice

Matchmaking is one of the key issues in the field of Web services research community because it is the basis of doing service discovery and composition. Using ontology semantically express the capabilities of services, accurately match, discovery and composition service. In this paper we explore using concept and attribute of Web services to construct the ontology. Also, we present a novel technique for merging ontology between different services using concept lattice. This enables construct and merge small-scale domain ontology convenient.

Effective algorithm of mining frequent itemsets for association rules

The efficiency of mining association rules is an important field of KDD. The algorithm Apriori is a classical algorithm in mining association rules. It is a breadth first search on the lattice space of itemsets. Though it makes use of anti-monotone of itemsets to reduce searching breadth, the algorithmic complexity of time is still the exponential quantity. In this article, the concepts of the generation and the ordinal itemsets tree are introduced. The ordinal itemsets tree is the dynamic description of mining relation of itemsets, and the vegetal ability of the ordinal itemsets tree is described by the generation. Through the study of the association rules, the conclusion that all frequent itemsets are not all vegetal itemsets and all vegetal itemsets are all frequent itemsets is discovered. With this conclusion, the number of the candidate itemsets can be reduced further to improve the efficiency of mining association rules and reduce the searching breadth. According to the generation, the AprioriFREQ algorithm, which is the improvement algorithm of Apriori, is designed in this article. By testing, the efficiency of the AprioriFREQ algorithm is obviously higher than the Aprioris.