Name
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 \sys , a model-based \actsms for time series with dimensions and possibly gaps. In this demonstration, participants can ingest data sets from multiple domains and experience how \sys 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 \sys 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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299869.3320216 | Proceedings of the 2019 International Conference on Management of Data |
Keywords | Field | DocType |
correlation, distribution, model-based compression, model-based data management, model-based query processing, olap, sensor, time series | SQL,Data point,Data mining,Data set,Model based management,Computer science,Outlier,Compression ratio,Online analytical processing | Conference |
ISSN | ISBN | Citations |
0730-8078 | 978-1-4503-5643-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Søren Kejser Jensen | 1 | 20 | 2.20 |
| Torben Bach Pedersen | 2 | 2102 | 181.24 |
| Christian Thomsen | 3 | 95 | 12.10 |