DocumentCode :
1666634
Title :
Enterprise Search with Development for Network Management System
Author :
Mingxue Wang ; Grindrod, Robin ; O´Meara, Jimmy ; Zuzuarregui, Mikel ; Martinez, Eloy ; Fallon, Enda
Author_Institution :
Network Manage. Lab., Ericsson, Athlone, Ireland
fYear :
2015
Firstpage :
430
Lastpage :
437
Abstract :
Browsing and searching network information for observation, analysis and troubleshooting is an inherent part of using the features and functions of any Network Management System. Enterprise search has capabilities for handling various data types and sources and big data scalability, and is becoming an emerging technology for such network management functions development. In this paper, we give an overview on work done in our research and prototype team regarding an advanced search project. We provide a brief report on search fundamental knowledge and study of Solr search platform stack. It answers common questions from management and development teams regarding adopting search technology for production development, and gives a Solr reference card for developers. We also introduce two advanced search features, user experience based search recommendation and anomaly detection enhanced search ranking from our research work. Two features are developed to make network searches more efficient as it can help user quickly locate the most valuable search results, but the concept can be adopted for search applications in other domains.
Keywords :
data handling; feature extraction; information networks; information retrieval; recommender systems; search engines; Solr search platform stack; anomaly detection; data handling; enterprise search; network information browsing; network information searching; network management system; search feature; search ranking; user experience based search recommendation; Big data; Indexing; Navigation; Search engines; Software; Anomaly detection; Big data analytics; Enterprise search; Network management; Search ranking; Search recommendation; Solr;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
Type :
conf
DOI :
10.1109/BigDataCongress.2015.70
Filename :
7207254
Link To Document :
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