Treffer: Data Model for Rich Time Series Data and Chameleon Query Language.

Nowadays time-stamped data is being generated by a variety of applications and stored in a variety of database systems. Depending on the data structure and the importance of the time aspect those database systems may be either general-purpose or time-series oriented. Most popular time-series DBMS use only simple data types for non-time values, whereas generalpurpose databases usually lack specialized query methods designed for time-series aspects. Each of those solutions has some drawbacks as far as data handling is concerned. Extending SQL with built-in time series focused components enables the combination of relational queries with document and time-series-oriented queries. At the same time, the popularity of SQL among data analysts and data scientists would give them the benefit of an easy start. This paper presents such an extension and introduces ChQL, a query language designed to work with both document data and multivariate time series. This language is designed to imitate the syntax of the Python language, so that it could be easily integrated as a library into object-oriented languages such as Python, Javascript, or PHP. The main architectural concept behind the data model for both languages is to use a mixed data model based on both document and time-series storage. This paper is focused on the languages time-series related features and their architectural consequences. [ABSTRACT FROM AUTHOR]

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