Energy Efficient Distributed Indexing Scheme for Full-Text Search on Multi Channel Broadcast

2013 ◽  
Author(s):  
V. Goel ◽  
A. K. Ahlawat ◽  
A. K. Gupta
Keyword(s):  
Full Text ◽  
Energy Efficient ◽  
Text Search ◽  
Full Text Search ◽  
Indexing Scheme ◽  
Distributed Indexing

GPU Computation for Online Realtime Multi-Pattern Matching

2013 ◽  
Vol 284-287 ◽  
pp. 3428-3432 ◽  
Author(s):  
Yu Hsiu Huang ◽  
Richard Chun Hung Lin ◽  
Ying Chih Lin ◽  
Cheng Yi Lin
Keyword(s):  
Parallel Computation ◽  
Pattern Matching ◽  
Full Text ◽  
Network Performance ◽  
Text Search ◽  
Full Text Search ◽  
Network Intrusion ◽  
Speed Up ◽  
Set Up ◽  
Gpu Implementation

Most applications of traditional full-text search, e.g., webpage search, are offline which exploit text search engine to preview the texts and set up related index. However, applications of online realtime full-text search, e.g., network Intrusion detection and prevention systems (IDPS) are too hard to implementation by using commodity hardware. They are expensive and inflexible for more and more occurrences of new virus patterns and the text cannot be previewed and the search must be complete realtime online. Additionally, IDPS needs multi-pattern matching, and then malicious packets can be removed immediately from normal ones without degrading the network performance. Considering the problem of realtime multi-pattern matching, we implement two sequential algorithms, Wu-Manber and Aho-Corasick, respectively over GPU parallel computation platform. Both pattern matching algorithms are quite suitable for the cases with a large amount of patterns. In addition, they are also easier extendable over GPU parallel computation platform to satisfy realtime requirement. Our experimental results show that the throughput of GPU implementation is about five to seven times faster than CPU. Therefore, pattern matching over GPU offers an attractive solution of IDPS to speed up malicious packets detection among the normal traffic by considering the lower cost, easy expansion and better performance.

scholarly journals Application of Full Text Search Engine Based on Lucene

2012 ◽  
Vol 02 (04) ◽  
pp. 106-109 ◽  
Author(s):  
Rujia Gao ◽  
Danying Li ◽  
Wanlong Li ◽  
Yaze Dong
Keyword(s):  
Search Engine ◽  
Full Text ◽  
Text Search ◽  
Full Text Search

Recommender Systems in Digital Libraries Using Artificial Intelligence and Machine Learning

2021 ◽  
pp. 162-178
Author(s):  
Namik Delilovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Libraries ◽  
Full Text ◽  
Text Search ◽  
Full Text Search ◽  
Artificial Intelligence Techniques ◽  
Search Results ◽  
Advanced Search ◽  
Search Field

Searching for contents in present digital libraries is still very primitive; most websites provide a search field where users can enter information such as book title, author name, or terms they expect to be found in the book. Some platforms provide advanced search options, which allow the users to narrow the search results by specific parameters such as year, author name, publisher, and similar. Currently, when users find a book which might be of interest to them, this search process ends; only a full-text search or references at the end of the book may provide some additional pointers. In this chapter, the author is going to give an example of how a user could permanently get recommendations for additional contents even while reading the article, using present machine learning and artificial intelligence techniques.

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