Journal of Computer Research and Development 

About: Journal of Computer Research and Development is an academic journal. The journal publishes majorly in the area(s): Wireless sensor network & Cluster analysis. Over the lifetime, 3172 publications have been published receiving 13633 citations.

Overview of Wireless Sensor Networks

Abstract: The invention of ubiquitous computing and increase of portable devices have raised the importance of mobile and wireless networking. Wireless networking is an emerging technology that makes the users to access data and services electronically, irrespective of their geographic location. Wireless network is a data communication system, which uses electromagnetic waves to transmit and receive information via air as medium from one place to another place. Basically wireless networks are classified into two types: infrastructure-based networks and infrastructure-less (ad-hoc) networks. Infrastructure-based networks have fixed BSs called access points which are connected by wires. The mobile nodes communicate with the BS via wireless link when it is inside the communication range of it. When the mobile node moves out of the communication range of a BS, it makes the connection with the other base station for communication. Cellular phone system, wireless local area networks (WLAN), paging systems are some of the example of

Big Data Management: Concepts，Techniques and Challenges

Abstract: Data type and amount in human society is growing in amazing speed which caused by emerging new service such as cloud computing, internet of things and social network, the era of Big Data has come. Data has been fundamental resource from simple dealing object, and how to manage and utilize big data better has attracted much attention. Evolution or revolution on database research for big data is a problem. This paper discusses the concept of big data, and surveys its state of the art. The framework of big data is described and key techniques are studied. Finally some new challenges in the future are summarized.

Knowledge Graph Construction Techniques

Abstract: Googles knowledge graph technology has drawn a lot of research attentions in recent years. However,due to the limited public disclosure of technical details,people find it difficult to understand the connotation and value of this technology.In this paper,we introduce the key techniques involved in the construction of knowledge graph in a bottom-up way,starting from a clearly defined concept and a technical architecture of the knowledge graph.Firstly,we describe in detail the definition and connotation of the knowledge graph,and then we propose the technical framework for knowledge graph construction,in which the construction process is divided into three levels according to the abstract level of the input knowledge materials,including the information extraction layer,the knowledge integration layer,and the knowledge processing layer,respectively.Secondly,the research status of the key technologies for each level are surveyed comprehensively and also investigated criticaly for the purposes of gradualy revealing the mysteries of the knowledge graph technology,the state-of-the-art progress,and its relationship with related disciplines.Finaly,five major research chalenges in this area are summarized,and the corresponding key research issues are highlighted.

A heuristic algorithm for reduction of knowledge

Abstract: Reduction of knowledge is one of the important topics in the research on rough set theory. It has been proven that computing the optimal (minimal) reduction of decision table is a NP hard problem. In the paper here, first, the significance of attributes in decision table is defined from the viewpoint of information; then, a heuristic algorithm based on mutual information for reduction of knowledge is proposed, and the complexity of this algorithm is analyzed; Finally, the experimental results show that this algorithm can find the minimal reduction for most decision tables.

Extension of rough set under incomplete information systems

Abstract: The classical rough set theory developed by professor Pawlak is based on complete information systems. It classifies objects using upper-approximation and lower-approximation defined on an indiscernibility relation that is a kind of equivalent relation. In order to process incomplete information systems, the classical rough set theory needs to be extended, especially, the indiscernibility relation needs to be extended to some inequivalent relation. There are several extensions for the indiscernibility relation now, such as tolerance relation, non-symmetric similarity relation, and valued tolerance relation. Unfortunately, these extensions have their own limitation. Presented in this paper is a new extension of rough set based on limited tolerance relation. The performances of these extended rough set models are also compared.