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Improve and optimize search engine: To provide better and relevant content for the customer
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This report has conducted a research of comparing a few open source search engines. The research contains two evaluation processes, the first evaluation will evaluate each open source search engine found on today’s market. Points will be given between one to five points depending on how well the open source search engine meets the requirements. The open source search engine with the highest score will then be chosen for implementation. The first evaluation resulted in Elasticsearch being the selected open source search engine and will continue to the implementation phase. The second evaluation will be measuring the system performance and the relevance of the SERP (Search Engine Results Pages). This phase will evaluate the system performance by taking time measurements on how long it takes for the search engines to deliver the SERP. The relevance of the search results will be judge by a group of CSN employers. The group will be giving point be-tween one to five points depending on the relevance of the SERP. It will eval-uate Elasticsearch with the search engine CSN are using today on their web-site (www.csn.se). This phase resulted in Elasticsearch being the better in performance measurements but not in the relevance of the SERP. This was discussed and came to the conclusion that most points were lost because of the first search result Elasticsearch delivered. If this search result was re-moved Elasticsearch could deliver as good results as the old search engine. The survey came to the conclusion that Elasticsearch is recommended for CSN if certain problem areas could be corrected before implementation into their systems.

 

Place, publisher, year, edition, pages
2019. , p. 61
Keywords [en]
Open Source Search Engine, Apache Lucene Core, Elasticsearch, Sphinx, Xapian, Solr, SiteVision
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:miun:diva-36805Local ID: DT-V19-A2-002OAI: oai:DiVA.org:miun-36805DiVA, id: diva2:1341431
Subject / course
Computer Engineering DT1
Educational program
Master of Science in Engineering - Computer Engineering TDTEA 300 higher education credits
Supervisors
Examiners
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved

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fulltext(1544 kB)15 downloads
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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