An SPN-Based Integrated Model for Web Prefetching and Caching

Shi, Lei; Han, Yinghua; Ding, Xiaoguang; Wei, Lin; Gu, Zhimin

doi:10.1007/s11390-006-0482-1

Cited by 16 publications

(7 citation statements)

References 12 publications

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“…Experimental procedure is as follows: First read the initial transition probability matrix [6] The experiment discusses two performance parameters: precision and recall [10]. The former is used for measuring accuracy of prediction and the latter is used for measuring applicability of prediction model.…”

Section: Experimental Results and Analysesmentioning

confidence: 99%

On the Compression of Markov Prediction Model

Shi

Yao

Wei

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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Markov prediction model is the basis of Web prefetching and personalized recommendation. The existence of a large amount of Web objects results in a vast increase in the number of states which represent the users visited transfer behavior, which also causes the problem of huge spatial complexity in prediction model. In view of the transition probability matrix in Markov prediction model, this paper proposes a measurement method based on row similarity and column similarity. First, the similarity matrix is calculated. Then the row similarity and column similarity are used to obtain similar pages simultaneously which can be compressed together. Thus the number of states can be reduced. The experimental results show that the model can not only have good overall performance and compression effect but also keeps relative higher prediction accuracy and recall.

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Section: Experimental Results and Analysesmentioning

confidence: 99%

On the Compression of Markov Prediction Model

Shi

Yao

Wei

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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“…Caching is an important optimization measure to enhance the efficiency of query processing, which uses the results of previous queries to answer the current query. Thus we can use some caching techniques (e.g., [23,24]) in our ranking mechanism. Secondly, because local scoring or global aggregation is performed within a source or a cluster, we can utilize the multithreading concurrent mechanism to run these operations in parallel.…”

Section: Global Score Computingmentioning

confidence: 99%

Combining Local Scoring and Global Aggregation to Rank Entities for Deep Web Queries

Kou

Shen

et al. 2009

J. Comput. Sci. Technol.

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With the rapid growth of Web databases, it is necessary to extract and integrate large-scale data available in Deep Web automatically. But current Web search engines conduct page-level ranking, which are becoming inadequate for entity-oriented vertical search. In this paper, we present an entity-level ranking mechanism called LG-ERM for Deep Web queries based on local scoring and global aggregation. Unlike traditional approaches, LG-ERM considers more rank influencing factors including the uncertainty of entity extraction, the style information of the entities and the importance of the Web sources, as well as the entity relationship. By combining local scoring and global aggregation in ranking, the query result can be more accurate and effective to meet users' needs. The experiments demonstrate the feasibility and effectiveness of the key techniques of LG-ERM.

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“…It provides decision support by using big data analysis and mining technology to integrate and analyze the resources of medical science. [1] Data management of patients and hospital information is convenient, and implementation of medicine resources sharing of information is effective. [2] Through this platform, it provides an effective way to balance and optimize the medical resources of medical mediums, so as to facilitate the medical treatment of people and promote the development of medical association.…”

Section: Introductionmentioning

confidence: 99%

Application of Medical Big Data in Clinic

Wang¹,

Wang²

2018

Proceedings of the 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018)

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Main business application of big data is used to analyze the specific cases through health data model. Combined with the actual demand and development trend, the main business range of medical applications of large data includes intelligent auxiliary diagnosis, image data analysis, image intelligent diagnosis, rational drug use, remote monitoring, precise medical treatment, cost analysis, the effect of performance management, hospital fee and medical quality analysis. By medical data aggregation and circulation of platform, the big hospital and small and medium-sized hospitals through mutual referral, can share graded and medical treatment.

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An SPN-Based Integrated Model for Web Prefetching and Caching

Cited by 16 publications

References 12 publications

On the Compression of Markov Prediction Model

On the Compression of Markov Prediction Model

Combining Local Scoring and Global Aggregation to Rank Entities for Deep Web Queries

Application of Medical Big Data in Clinic

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