Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series

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Abstrakt

Due to the big amounts of sensor data produced, it is infeasible to store all of the data points collected and practitioners currently hide outliers by storing simple aggregates instead. As a remedy, we demonstrate ModelarDB, a model-based Time Series Management System (TSMS) for time series with dimensions and possibly gaps. In this demonstration, participants can ingest data sets from multiple domains and experience how ModelarDB provides fast ingestion and a high compression ratio by adaptively compressing time series using a set of models to accommodate changes in the structure of each time series over time. Models approximate time series within a user-defined error bound (possibly zero). Participants can also experience how the compression ratio can be improved by ingesting correlated time series in groups created by ModelarDB from user-hints. Participants provide these using primitives for describing correlation. Last, participants can execute SQL queries on the ingested data sets and see how the system optimizes queries directly on models.
OriginalsprogEngelsk
TitelProceedings of the 2019 International Conference on Management of Data
Antal sider4
ForlagAssociation for Computing Machinery
Publikationsdato2019
Sider1933-1936
ISBN (Elektronisk)978-1-4503-5643-5
DOI
StatusUdgivet - 2019
BegivenhedACM SIGMOD International Conference on Management of Data - Amsterdam, Holland
Varighed: 30 jun. 20195 jul. 2019
https://sigmod2019.org/

Konference

KonferenceACM SIGMOD International Conference on Management of Data
LandHolland
ByAmsterdam
Periode30/06/201905/07/2019
Internetadresse
NavnProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN0730-8078

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