Many research institutes produce a huge amount of data. It was said by someone that “We are drowning in data, but starving of information”. This is particularly true for scientific data. The needs and the advantages of being able to search data from different experiments are increasing in order to look for differences and similarities among them and thus doing Meta studies. A Meta-study is the method that takes data from different independent studies and integrate them using statistical analysis. If data is well described and data access is flexible then it is possible to establish unexpected relationships among data. It also helps in the re-using of data from studies that have already been conducted which saves time, money and resources. In this thesis, we explore at the ways to store data from experiments and to make finding cross-experiments more efficient. The main aim of this thesis work is to propose a database solution for storing time series data generated by different simulators and to investigate the feasibility of using it with ICAT. ICAT is a metadata system used for searching and browsing of scientific data. This thesis has been completed in two steps. The first step is aimed at proposing an efficient database solution for storing time series data. The second step is aimed at investigating the feasibility of using ICAT and proposed database solution together. We found out that it is feasible to use ICAT as a metadata system for scientific studies. Since it is free and open source, it can be linked to any system and customized according to the needs.

All cars support reading diagnostic data from their control units via the On-Board Diagnostics II protocol. For companies with large vehicle fleets it may be valuable to analyze this diagnostic data, but large vehicle fleets produce large amounts of data. In this thesis we investigated whether the time series database TimescaleDB is suitable for storing such data. In order to investigate this we tested and evaluated its insertion speed, query execution time and compression ratio. The results show that TimescaleDB is able to insert over 200 000 rows of data per second. They also show that the compression algorithm can speed up query execution by up to 134.5 times and reach a compression ratio of 9.1. Considering these results we conclude that TimescaleDB is a suitable choice for storing diagnostic data, but not necessarily the most suitable.

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. / 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.