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Identifying & Evaluating Methods for Improved Database Performance
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis concern the evaluation of methods for improving the efficiency of searching in databases. More and more of our lives and human interactions occur through computerized systems which in turn requires efficient processing of data. People simply expect minimal delay. Likewise, the modern computerized world requires a high demand of security measures, which nevertheless should be as minimally intrusive in use as possible. Today there are many different ways for people to authenticate identity or privileges. One of these ways, that have become increasingly paid attention to, is called Active Authentication which means that individuals are authenticated with the help of different biometric sensors. This type of technology however places great demands on today’s databases, computing power and efficient search of data.As long as the data is limited and not too complex, this can easily be managed by a simple brute force search. When the complexity and size of the database increase, more refined methods need to be used. Even though there are many different methods that can be used, different databases and data types demand different solutions.This study has through the use of techniques for requirements analysis and efficiency evaluation focused on finding the most efficient and least intrusive method for improving search in a database. The results show that in this case indexing should be seen as the least intrusive as long as there are more static datasets available. While more complex methods and frameworks need to be used when the data is of a more dynamic and complex character.

Place, publisher, year, edition, pages
2015. , 36 p.
Keyword [en]
Technology
Keyword [sv]
Teknik, Database, performance, Big data, data modeling, pattern recognition, keystroke analysis, behaviometrics, Hadoop, Mapreduce, Fuzzy logic, indexing, index, NoSQL, SQL, evalutation
Identifiers
URN: urn:nbn:se:ltu:diva-49109Local ID: 67f9aa3f-257c-486e-a997-e6ff58508be4OAI: oai:DiVA.org:ltu-49109DiVA: diva2:1022454
External cooperation
Subject / course
Student thesis, at least 15 credits
Educational program
Systems Sciences, bacheor's level
Supervisors
Note
Validerat; 20150819 (global_studentproject_submitter)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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fulltext(931 kB)40 downloads
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Bitar, HadiHenriksson, Mattias

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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