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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 2019 International Conference on Management of Data
Number of pages4
PublisherAssociation for Computing Machinery
Publication date2019
Pages1933-1936
ISBN (Electronic)978-1-4503-5643-5
DOIs
Publication statusPublished - 2019
EventACM SIGMOD International Conference on Management of Data - Amsterdam, Netherlands
Duration: 30 Jun 20195 Jul 2019
https://sigmod2019.org/

Conference

ConferenceACM SIGMOD International Conference on Management of Data
CountryNetherlands
CityAmsterdam
Period30/06/201905/07/2019
Internet address
SeriesProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN0730-8078

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