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A Comparative Study of Databases for Storing Sensor Data
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]

More than 800 Zettabytes of data is predicted to be generated per year by the Internet of Things by 2021. Storing this data necessitates highly scalable databases. Many different data storage solutions exist that specialize in specific use cases, and designing a system to accept arbitrary sensor data while remaining scalable presents a challenge.The problem was approached through a comparative study of six common databases, inspecting documented features and evaluations, followed by the construction of a prototype system. Elasticsearch was found to be the best suited data storage system for the specific use case presented in this report, and a flexible prototype system was designed. No single database was determined to be best suited for sensor data in general, but with more specific requirements and knowledge of future use, a decision could be made.

Abstract [sv]

Över 800 Zettabytes av data är förutspått att genereras av Sakernas Internet vid år 2021. Lagring av denna data gör det nödvändigt med synnerligen skalbara databaser. Det finns många olika datalagringslösningar som specialiserar sig på specifika användningsområden, och att designa ett system som ska kunna ta emot godtycklig sensordata och samtidigt vara skalbar är en utmaning. Problemet angreps genom en jämförande studie av sex populära databaser som jämfördes utifrån dokumenterad funktionalitet och fristående utvärderingar. Detta följdes av utvecklingen ut av ett prototypsystem. Elasticsearch bedömdes vara bäst lämpad för det specifika användningsområde som presenteras i denna rapport, och ett flexibelt prototypsystem utvecklades. Inte en enda databas bedömdes vara bäst lämpad för att hantera sensordata i allmänhet, men med mer specifika krav och vetskap om framtida användning kan en databas väljas ut.

Place, publisher, year, edition, pages
2019. , p. 70
Series
TRITA-EECS-EX ; 2019:174
Keywords [en]
IoT, NoSQL
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-253772OAI: oai:DiVA.org:kth-253772DiVA, id: diva2:1325707
Supervisors
Examiners
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-17Bibliographically approved

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Citation style
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