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

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

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

Fingerprint

Time series
Demonstrations
Sensors

Citer dette

Jensen, S. K., Pedersen, T. B., & Thomsen, C. (2019). Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series. I Proceedings of the 2019 International Conference on Management of Data (s. 1933-1936). Association for Computing Machinery. Proceedings of the ACM SIGMOD International Conference on Management of Data https://doi.org/10.1145/3299869.3320216
Jensen, Søren Kejser ; Pedersen, Torben Bach ; Thomsen, Christian. / Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series. Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery, 2019. s. 1933-1936 (Proceedings of the ACM SIGMOD International Conference on Management of Data).
@inproceedings{2c8fce3b9cf241a4960a925c27ced52d,
title = "Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series",
abstract = "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.",
author = "Jensen, {S{\o}ren Kejser} and Pedersen, {Torben Bach} and Christian Thomsen",
year = "2019",
doi = "10.1145/3299869.3320216",
language = "English",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
pages = "1933--1936",
booktitle = "Proceedings of the 2019 International Conference on Management of Data",
publisher = "Association for Computing Machinery",
address = "United States",

}

Jensen, SK, Pedersen, TB & Thomsen, C 2019, Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series. i Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery, Proceedings of the ACM SIGMOD International Conference on Management of Data, s. 1933-1936, ACM SIGMOD International Conference on Management of Data, Amsterdam, Holland, 30/06/2019. https://doi.org/10.1145/3299869.3320216

Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series. / Jensen, Søren Kejser; Pedersen, Torben Bach; Thomsen, Christian.

Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery, 2019. s. 1933-1936 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series

AU - Jensen, Søren Kejser

AU - Pedersen, Torben Bach

AU - Thomsen, Christian

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1145/3299869.3320216

DO - 10.1145/3299869.3320216

M3 - Article in proceeding

T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data

SP - 1933

EP - 1936

BT - Proceedings of the 2019 International Conference on Management of Data

PB - Association for Computing Machinery

ER -

Jensen SK, Pedersen TB, Thomsen C. Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series. I Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery. 2019. s. 1933-1936. (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/3299869.3320216