Time-Series data has become more and more important for Industry 4.0, IoT and data-driven companies. Since the data volume is rising, NoSQL databases like Apache Accumulo, Cassandra and HBase are providing extensions to work with time-series data:

• Timely (Accumulo)
• Open TSDB (HBase)
• KairosDB (Cassandra)

Unfortunately they are either immature or didn’t provide exact numbers for aggregations (min, max, sum, avg, std deviation, percentile) of large data sets.

This theses aims to define a performant schema for exact aggregations by either using Apache Accumulo with server-side iterators or Apache Flink as distributed calculation framework.

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