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Improve and optimize search engine: To provide better and relevant content for the customer
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationssystem och –teknologi.
2019 (engelsk)Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
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.

 

sted, utgiver, år, opplag, sider
2019. , s. 61
Emneord [en]
Open Source Search Engine, Apache Lucene Core, Elasticsearch, Sphinx, Xapian, Solr, SiteVision
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-36805Lokal ID: DT-V19-A2-002OAI: oai:DiVA.org:miun-36805DiVA, id: diva2:1341431
Fag / kurs
Computer Engineering DT1
Utdanningsprogram
Master of Science in Engineering - Computer Engineering TDTEA 300 higher education credits
Veileder
Examiner
Tilgjengelig fra: 2019-08-08 Laget: 2019-08-08 Sist oppdatert: 2019-08-08bibliografisk kontrollert

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