TREAT - Two wRongs makE A righT: efficient distributed storage and queries of loT datasets with erasure coding and compression

Erasure coding in distributed multi-cloud data storage increases availability, durability and security, but it also makes data analytics inefficient since the whole dataset must be reconstructed to answer a query, even if the result set is a small fraction of the complete file. Data compression has a similar trade-off as it can reduce storage costs while requiring the entire compressed data to be collected and decompressed in order to access even a few bytes. We propose TREAT, a novel method that combines erasure coding and compression to achieve efficient queries of time series datasets while keeping the benefits of both underlying techniques. Our evaluation of five real-life datasets shows that it can answer range queries up to 25 times faster with 100 times less data transfer than reconstructing the whole dataset.