Research on Web mining-based intelligent search engine

Author(s):  
Yan Li ◽  
Xin-Zhong Chen ◽  
Bing-Ru Yang
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa

The user of dynamic social network does not require irrelevant and vast amount of information during a search. A need of an intelligent search is required to get the reduced, filtered and relevant information that is achieved using an intelligent information retrieval and web mining. In this paper, identification and description of facts related to needs of an intelligent search in dynamic social network has been done by the authors after the deep and thorough study conducted on several journal and conference papers that are scattered on different electronic databases globally. The usage of intelligent agent for effective information retrieval from the social network site is a very emerging area and it will help the users to find the relevant and concerned information quickly and efficiently. The findings of the authors will help researchers and scholars who are already working in this area to get the relevant information in the direction of future research.


Author(s):  
Ji-Rong Wen

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.


2020 ◽  
Vol 21 (2) ◽  
pp. 189-202
Author(s):  
Neelima Gullipalli ◽  
Sireesha Rodda

Like other mining, web mining is also necessary to increase the power of web search engine to identify the intended web page and web document. While processing with large datasets, there arises several issues associated with space availability, similarity relationships between different webpage’s and running time. Hence, this paper intends to develop an enhanced web mining model based on two contributions. At first, the hierarchical tree is framed, which produces different categories of the searching queries (different web pages). Next, to hierarchical tree model, enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique model is developed by modifying the traditional DBSCAN. This technique results in proper session identification from raw data. Moreover, this technique offers the optimal level of clusters necessitated for hierarchical clustering. After hierarchical clustering, the rule mining is adopted. The traditional rule mining technique is generally based on the frequency; however, this paper intends to enhance the traditional rule mining based on utility factor as the second contribution. Hence the proposed model for web rule mining is termed as Enhanced DBSCAN-based Hierarchical Tree (EDBHT). It benefits in providing the search results depending on high level information (e.g., location), so that the ability of search engine in providing the interesting association rules can be improved. Next, to the implementation, the performance of proposed EDBHT is found to be enhanced when compared over several traditional models.


2021 ◽  
Author(s):  
Michal Huptych ◽  
Jiri Potucek ◽  
Lenka Lhotská

The paper describes some aspects of precision medicine and shows the importance of pharmacokinetics and pharmacodynamics for the therapeutic drug monitoring and model-informed precision dosing. A key element in the design of the pharmacokinetics and pharmacodynamics (PKPD) models is relevant literature search that represents an essential step in the procurement and validation of a new drug. Available search engine resources do not offer specific functionalities that are required for efficient and relevant search in reliable literature sources. We present a prototype of such an intelligent search engine and show its results on real project data.


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