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Realtidssammanställning av stora mängder data från tidsseriedatabaser
KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
2017 (Swedish)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
Realtime compilation of large datasets from time series databases (English)
Abstract [sv]

Stora mängder tidsseriedata genereras och hanteras i tekniska försörjningssystem och processindustrier i syfte att möjliggöra övervakning av systemen. När tidserierna ska hämtas och sammanställas för dataanalys utgör tidsåtgången ett problem. Examensarbetet hade som syfte att ta reda på hur utvinning av tidsseriedata borde utföras för att ge bästa möjliga svarstid för systemen.

För att göra hämtningen och sammanställningen så effektiv som möjligt testades och utvärderades olika tekniker och metoder. De områden som tekniker och metoder jämfördes inom var sammanställning av data inom och utanför databasen, cachning, användandet av minnesdatabaser jämfört med andra databaser, dataformat, dataöverföring, och förberäkning av data.

Resultatet var att den bästa lösningen bestod av att sammanställa data parallellt utanför databasen, att använda en egen inbyggd minnesdatabas, att använda Google Protobuf som dataformat, samt att förberäkna data.

Abstract [en]

Large amounts of time series data are generated and managed within management systems and industries with the purpose to enable monitoring of the systems. When the time series is to be acquired and compiled for data analysis, the expenditure of time is a problem. This thesis was purposed to determine how the extraction of time series data should be performed to give the systems the best response time possible.

To make the extraction and compilation as effective as possible, different techniques and methods were tested and evaluated. The areas that techniques and methods were compared for were compilation of data inside and outside the database, caching, usage of in-memory databases compared to other databases, dataformats, data transfer, and precalculation of data.

The results showed that the best solution was to compile data in parallel outside the database, to use a custom built-in in-memory database, to use Google Protobuf as data format, and finally to use precalculated data.

Place, publisher, year, edition, pages
2017. , 67 p.
Series
TRITA-STH, 2017:35
Keyword [en]
time series, time series database, cache, data format, data transfer, performance, in-memory database
Keyword [sv]
tidsserier, tidsseriedatabas, cache, dataformat, dataöverföring, prestanda, minnesdatabaser
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208932OAI: oai:DiVA.org:kth-208932DiVA: diva2:1108975
External cooperation
Gemit
Subject / course
Computer Technology, Program- and System Development
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2017-06-16 Created: 2017-06-13 Last updated: 2017-06-16Bibliographically approved

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