Guigang Zhang

Parallel Association Rule Mining for Medical Applications

For real-time applications that consist of massive number of rules, partitioning of the rules to support parallel processing is important. This paper proposes a suite of algorithms called GAPCM for parallel processing of massive number of rules. By considering even distribution, minimal waiting time and minimal inter-processor communication, we propose three algorithms for subnet allocation, and apply these algorithms to association rule mining.

Semantic Programming of Web-Enabled Database Applications

This paper presents a declarative programming language SOBL for data-intensive Web applications. A SOBL program separates the application data from the composition and navigation of UI data. Static Web pages are automatically generated from UI requirements, and an executable behavior specification is derived from the behavior requirements in SOBL automatically. SOBL is employed to represent the behavior requirements that involve a series of actions in a specific order, which are expressed by the compositions of certain types of control structures and triggers. It assists non-technical people to describe the scenarios of systems without the knowledge of technical details.

An Efficient Massive Data Processing Model in the Cloud -- A Preliminary Report

Nowadays, the data-intensive applications and IoT applications have gained a very big development in the cloud environment. All these applications need to process massive data. How to process these massive data effectively is becoming very important in the cloud environment. In this paper, we designed an efficient massive data processing model in the cloud. This model can be used to process all kinds of structured data resources, semi-structured data resources and non-structured data resources together. We introduced some key components in this model and give some key algorithms in our model.

A New Electronic Commerce Architecture in the Cloud

In this paper, the authors propose a new electronic commerce architecture in the cloud that satisfies the requirements of the cloud. This architecture includes five technologies, which are the massive EC data storage technology in the cloud, the massive EC data processing technology in the cloud, the EC security management technology in the cloud, OLAP technology for EC in the cloud, and active EC technology in the cloud. Finally, a detailed discussion of future trends for EC in the cloud environment is presented in this paper.

DataCloud: An Efficient Massive Data Mining and Analysis Framework on Large Clusters

With the development of cloud computing technologies, big data processing is becoming more and more important. How to mine and analyze massive data is facing a very big challenge. In this paper, we proposed an efficient massive data mining and analysis framework Data Cloud on large clusters. The most important part of Data Cloud is the Rabbit. It is a kind of massive data mining and analysis processing plan framework on the large clusters like the Pig and Hive. We make a detail analysis about the Rabbit plan.

A Semantic++ Social Search Engine Framework in the Cloud

With the development of internet communities, lots of social-computing intensive applications have been developed, such as the Google+, Twitter and Facebook so on. In this paper, we describe a semantic++ social search engine framework in the cloud. Our framework is composed by 8 parts. It includes: (1)semantic social search interface, (2)semantic parser, (3)semantic social rank, (4) semantic index base, (5) social network relationships base, (6) Seman Social computing module, (7) massive data processing and (8) HDFS (files).

Research on Semantic++ Computing Based on Big Data Environment

With the development of cloud computing and IoT, more and more data-intensive applications have come into being. It is very import to process the big data for these data-intensive applications. In this paper, we do some analysis about the basic concepts of big data and semantic++ computing. It includes the definition of big data, semantic computing and semantic++ computing. In this paper, we make a detail analysis about the semantic++ understanding based on big data. It mainly includes semantic++ storage of big data resources, semantic++ information acquirement of big data resources, semantic++ resources management, semantic++ processing of big data, semantic++ service of big data, semantic++ security and privacy of big data, semantic++ interface and applications based on big data. Two kinds of semantic++ applications are proposed. In this paper, a semantic++ search and recommendation system framework is analyzed. Finally, the simulation experiment shows that the semantic++ search and recommendation method is more efficient than the traditional method.

Semantic++ Digital Library Service Framework in the Cloud Environment

With the development of internet, more and more new technologies occur. Digital library is migrating toward the directions of semantic computing and cloud environment, too. In this paper, we proposed a semantic++ digital library service framework based on the cloud environment. The semantic++ computing has higher semantic than the traditional semantic computing. In this framework, all massive digital library data are stored into the cloud environment. Digital library users can get their information through a semantic++ interface, that is to say, they can get “what they wanted” from the semantic++ digital library more easily than before. Semantic++ digital library maybe become the development trend of digital library in the future.

A Cloud Framework for Electronic Commerce Applications

With the development of cloud computing technologies, more and more electronic commerce applications have been transplanted into the cloud environment. How to deploy a cloud framework for electronic commerce applications is becoming more and more important. In this chapter, we constructed a cloud framework for electronic commerce and discussed some key technologies in this framework such as semantic cloud file system and the cloud security for electronic commerce so on.

Implementation of Space Optimized Bisecting K-Means (BKM) Based on Hadoop

This article is composed in the background of the study of scientific field of coauthors phenomenon factual basis. By the study of massive amounts of relational data, it provides us with major significances theoretically and practically on retrieving and obtaining professionally academic information and getting knowing of academic development trend of miscellaneous fields. In process of studying this type of project, the problem of cluttering for coauthors that are in the data is involved. However, it is hard to meet the need of implementing the analysis of massive amounts of data cluttering by the existing cluttering software and algorithms, for this reason, finding an approach to deal with this kind of question is toughly important. To solve this question, this article presents an optimized Bisecting K-Means (BKM) clustering algorithm based on Hadoop and states the fashion of how to optimize the algorithm and the key point of implementing in details after analyzing the status quo related to this study. Estimating the complexity of the algorithm by experiments indicates the current problems and the direction for the future study.