Paper Info
Title
Scalable Model-Based Management Of Correlated Dimensional Time Series In Modelardb(+)
Abstract
To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used. However, as they produce huge amounts of data, only simple aggregates are stored. This removes outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing time series with dimensions that exploits correlation in and among time series. Specifically, we propose compressing groups of correlated time series using an extensible set of model types within a user-defined error bound (possibly zero). We name this new category of model-based compression methods for time series Multi-Model Group Compression (MMGC). We present the first MMGC method GOLEMM and extend model types to compress time series groups. We propose primitives for users to effectively define groups for differently sized data sets, and based on these, an automated grouping method using only the time series dimensions. We propose algorithms for executing simple and multi-dimensional aggregate queries on models. Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB (ModelarDB(+)). Our evaluation shows that compared to widely used formats, ModelarDB(+) provides up to 13.7x faster ingestion due to high compression, 113x better compression due to the adaptivity of GOLEMM, 573x faster aggregates by using models, and close to linear scalability. It is also extensible and supports online query processing.
Year
DOI
Venue
2021
10.1109/ICDE51399.2021.00123
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021)
DocType
ISSN
Citations 
Conference
1084-4627
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Søren Kejser Jensen1202.20
Torben Bach Pedersen22102181.24
Christian Thomsen39512.10