15609476
An Online Personalized Recommendation Model Based on Bayesian Networks
It is one of an important method of using Bayesian networks in electronic commercial recommended system. But the models of Bayesian networks for describing recommended system have a problem that it could not learn online. The paper puts forward an online personalized recommended model based on Bayesian networks. The paper uses a partial ordering to represent previous structure and find posterior distributions of every node on the orders to realize online structure learning. It also uses a correctional function to revise log likelihood for online parameter learning. The experiment shows that the model can be learned online for personalized recommended system.
46086944
A Research on Dissipative Structure of the Information Resource Organization of E-Government
Government information resource is the foundation of e-government, and government information resource organization pattern is the key factor that affects sustainable development of e-government. At present, a lack of the systematic plan in government information resource organization exists universally. The primary reason is that systematization and formalization of research on government information resource organization pattern is insufficient and lacks support to organize and plan the government information resource. This article starts with the basic assumption of the government information hyper-cycle system consisting of e-government and its object system, depicts the dissipative structure of government information resource organization through introducing superior-rank operator and lower-rank operator in algebra system, and explores actively the self-organized, extensible and universal government information resource organization pattern based on hyper-cycle (PBHC) systematic plan.
14622249
Knowledge Inventory Management Using Actuarial Method
Knowledge inventory management is nowadays central issue as knowledge management becomes vital for organizations. Knowledge inventory management involves acquiring, retaining, deploying, idling, and abandoning technologies. Managers must make investment decisions about which technologies should be involved into inventory for future usage. Options-pricing model (OPM) has been widely accepted to make investment decisions. An alternative technology is regarded as an option to switch technologies to deal with future contingencies. This paper studies another kind of knowledge inventory problem which OPM is sort of inefficient to deal with. An organization might be forced to invest in some technology in the future because of some specific incident occurs, thus option is replaced by obligation. The characteristics of this kind of problem are portrayed and we show why OPM is not applicable. We treat the specific incident as an insurance accident to organizations and initial investment in some technology as insurance fee which can cover the loss when accident occurs. We use actuarial model to analyze the problem and give the evaluation methods in different conditions.
35490802
Mining Condensed and Lossless Association Rules by Pruning Redundancy
There are excessive and disordered rules generated by traditional approaches of association rule mining, many of which are redundant, so that they are difficult for users to understand and make use of. Han et al pointed out the bottleneck of association rules mining is not on whether we can derive the complete set of rules under certain constraints efficiently but on whether we can derive a compact but high quality set of rules. To solve this problem, a new method was represented, which is based on statistics and probability to get a condensed rules set by removing redundant rules. Our set of rules is more concise, more intelligible and easier to manage and use than others. Especially, the condensed set is lossless so that its switch to original rules-set can be realized. Itpsilas important because the switch keeps the information complete. Experiments on some datasets show that the number of rules in rules-set has been reduced greatly.
694781
A Decentralized Resource Discovery Based on Keywords Combinations and Node Clusters in Knowledge Grid
The organization and discovery of grid resources are foundational and key subjects in grid research. Many research works in this field have presented the solutions to the problem, but few of them are focused on knowledge resources. This paper aims to explore how to support user's knowledge requests submitted in form of multi-keywords. We present a decentralized resource discovery method based on keyword combinations and node clusters. In the method, hot keyword combinations are formed based on user's knowledge requests. Then, grid nodes can be clustered according to these keyword combinations and user's knowledge requests will be transmitted to those clusters that have high correlations with the requests.
3041039
The Impacts of Enterprise Resource Planning Systems on Firm Performance: An Empirical Analysis of Chinese Chemical Firms
Chinese firms heavily invest in enterprise resource planning systems (ERP) in recent years and expect that ERP can help them gain superior financial performance. Given the high costs and high risk of ERP investments, to quantify the financial benefits of ERP implementation is an important research issue. This paper empirically examines the impacts of ERP implementation on firm performance using the financial data from 50 Chinese chemical firms that implemented ERP. The firms’ abnormal performance during the two-year implementation period and three-year post-implementation period was analyzed based on the guideline of Barber et al. on event studies. The results find no significant performance improvement during the implementation period and the three-year post-implementation period and a decline in performance in the first two years after implementation. However, a slight performance improvement in the third year after implementation may indicate that the financial benefits of ERP may show after a long-term ERP use. These results provide insight for Chinese firms that want to invest in ERP.
3090205
Challenges for End-to-End Quality of Service in Heterogeneous Networks
To guarantee the end-to-end QoS (E2E Quality of Service) in heterogeneous network is an important issue to provide pervasive and trusted service. Currently, the different networks are optimized for different services, but it becomes urgent to provide pervasive service across different networks. This paper gives an overview on how to design a dynamic and integrated QoS architecture under heterogeneous networks. This paper firstly analyzes the actuality of the E2E quality of service in heterogeneous networks, and then discusses the requirements of the E2E QoS of two types of heterogeneous networks, finally points out the five key issues to solve the practical problem in the current network.
36541131
High Performance Support of Lustre over Customized HSNI for HPC
Parallel I/O needs keep pace with the demand of high performance computing applications. I/O bottleneck still is one of the key problems which restrict the development of high performance computer. Object-based storage combines the advantages of SAN and NAS, adopts an object-based interface, and has been a new orientation of massive storage. The trend as such makes it very promising to build a scalable I/O system for highperformance computing through implementation of object-based storage system based on high-performance interconnects. Lustre is a high performance distributed object parallel file system, is being widely studied and applied. Exploiting performance of Lustre depends on supports of high performance interconnects. In this paper, firstly we designed and implemented a customized high performance interconnect chip, HSNI (High performance Storage Network Interface), and introduced the characteristics of HSNI. Then we analyzed the communication features which support high performance of Lustre. Experimental results show that HSNI can greatly exert the performance of Lustre and has a very good scalability, so its very suitable for building the high performance object-based storage systems for high performance computing.
6983198
Investigation of Research towards Efficient Social Peer-to-Peer Networks
Peer-to-peer (P2P) networks attract attentions worldwide with their great success in file sharing networks (e.g. Napster, Gnutella, BitTorrent, and Kazaa). In the last decade, numerous studies have been devoted to the problem of resource discovery in P2P networks. However, efficient resource discovery remains a key challenge for large-scale P2P networks. An investigation of existing research on resource discovery towards efficient social P2P networks will be given in this paper.
6333240
Web video topic discovery and tracking via bipartite graph reinforcement model
Automatic topic discovery and tracking on web-shared videos can greatly benefit both web service providers and end users. Most of current solutions of topic detection and tracking were done on news and cannot be directly applied on web videos, because the semantic information of web videos is much less than that of news videos. In this paper, we propose a bipartite graph model to address this issue. The bipartite graph represents the correlation between web videos and their keywords, and automatic topic discovery is achieved through two steps - coarse topic filtering and fine topic re-ranking. First, a weight-updating co-clustering algorithm is employed to filter out topic candidates at a coarse level. Then the videos on each topic are re-ranked by analyzing the link structures of the corresponding bipartite graph. After the topics are discovered, the interesting ones can also be tracked over a period of time using the same bipartite graph model. The key is to propagate the relevant scores and keywords from the videos of interests to other relevant ones through the bipartite graph links. Experimental results on real web videos from YouKu, a YouTube counterpart in China, demonstrate the effectiveness of the proposed methods. We report very promising results.
16471179
Small World Architecture for Peer-to-Peer Networks
Small-world phenomenon has been observed in existing peer-to-peer (P2P) networks, such as Gnutella and Freenet. Due to the similarity of P2P networks to social networks, the previous small-world model proposed by Duncan Watts can be adopted in the design of P2P networks: each node is connected to some neighbouring nodes, and a group of nodes keep a small number of long links to randomly chosen distant nodes. Unfortunately, current unstructured search algorithms have difficulty distinguishing these random long-range shortcuts. This paper presents small world architecture for P2P networks (SWAN) with a semi-structured P2P search algorithm that is used to create and find long-range shortcuts toward remote peer groups. In SWAN, not every peer node needs to be connected to remote groups, but every peer node can easily find which peer nodes have external connections to a specific peer group