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Execution Time Analysis of Electrical Network Tracing in Relational and Graph Databases
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In today’s society, we handle a lot of connected data. Examples are companies like Facebook and Amazon, that handle connected data in different ways. Geographic Information Systems and Network Information Systems handle connected data in the form of networks or graphs that can represent anything from an electrical network to a product network. When it comes to connected data, the most commonly used database technology is relational databases. However, with a lot of new databases emerging, there may be better alternatives for connected data that can provide higher performance. In this study we look at the Oracle relational database and the Neo4j graph database and study how both databases traverse an electrical network. The findings indicate that the Neo4j graph database outperforms the Oracle relational database regarding execution time of search queries.

Abstract [sv]

I dagens samhälle hanterar vi mycket kopplad data. Exempel är företag som Facebook och Amazon, som hanterar kopplad data på olika sätt. Geografiska informationssystem och nätverksinformationssystem hanterar kopplad data i form av nätverk eller grafer som kan representera allt från elnät till ett produktnätverk. När det gäller kopplad data är den mest använda tekniken relationsdatabaser. Men med många nya databaser som kommer fram kan det nu finnas bättre alternativ för kopplad data som kan ge högre prestanda. I denna undersökning tittar vi på relationsdatabasen Oracle och grafdatabasen Neo4j och undersöker hur båda databaserna traverserar ett elnät. De presenterade resultaten visar att grafdatabasen Neo4j utför graftraversering snabbare än relationsdatabas Oracle, där fokus ligger på körningstid.

Place, publisher, year, edition, pages
2019. , p. 50
Series
TRITA-EECS-EX ; 2019:71
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-249639OAI: oai:DiVA.org:kth-249639DiVA, id: diva2:1304968
Supervisors
Examiners
Available from: 2019-05-15 Created: 2019-04-15 Last updated: 2019-05-15Bibliographically approved

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fulltext(1016 kB)25 downloads
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e30b33e5fbe0ebe005a04d848011421d34d70ce487861cbb5f2ff11f51df33a6d7a8bf7cfc5db22dc6d330b55affe5d448857b3e26de8a43af84b4ec1d225971
Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • nn-NB
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  • Other locale
More languages
Output format
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