The storage and mining technologies of time series data is getting increasingly important with the arrival and development of the big data era. Time series data from stock market, macroeconomic system, images of satellite, medical and industrial objects can cause failures when processed with relational database traditionally, especially in the cases of indexing and querying of time series data.On the basis of time series data analyses, a method to calculate index value of time series data based on reference point is adopted in this article, then efficient data indexing and querying is achieved after the establishment of index structure by List Sort Tree and R Tree algorithms. After that, the design of interface functions is completed in C# by the kernel of SQLite, and script programming is also integrated into the database to allow secondary developments by scripting language. At last, the integration tests of the database is finished in LabVIEW by the thought of object oriented programming.The indexing algorithms tests and system integration show that the excellent versatility of the time series database design by the kernel of open source relational database SQLite. The two algorithms have its own merits, List Sort Tree algorithm has a better performance in querying while R Tree algorithm can solve dynamic index well. The whole system with openness and versatility has a clear independent structure because of hierarchical design, so the users can build their own time series database according to individual needs. |