TS-Benchmark: A Benchmark for Time Series Databases

Hao, Yuanzhe; Qin, Xiongpai; Chen, Yueguo; Sun, Xiaoyan; Yu, Tao; Zhang, Xiao; Du, Xiaoyong

doi:10.1109/icde51399.2021.00057

Cited by 26 publications

(11 citation statements)

References 21 publications

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“…For storing discrete data points InfluxDB , an open source time series database is used [ 50 ]. The InfluxDB stands out from other popular time series databases such as Prometheus, Druid, or OpenTSDB due to its query response time [ 51 ]. Compared to Prometheus, InfluxDB offers an SQL-like query language, the possibility to manage user rights and in-memory capabilities [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the InfluxDB features a better compression ratio than Druid and OpenTSDB. These advantages make InfluxDB suitable for storing time series data within the infrastructure [ 51 ]. InfluxDB is able to sign incoming data with a timestamp and classify it into corresponding buckets which can be assigned to sensors in even more detail with the help of further criteria from the DT.…”

Section: Methodsmentioning

confidence: 99%

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Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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“…YCSB-TS [28] adopts the structure and the workloads of YCSB and adds basic time functions and thus inherits unoptimized workloads to benchmark time-series databases. ts-benchmark [12] is a time-series benchmark developed by Yuanzhe Hao et al It uses a GAN model to generate synthetic time-series data to ingest data and supports diverse workloads for data loading, injection, and loading in addition to monitoring usage of system resources. ts-benchmark, however, does not take into consideration aggregation and down-sampling queries which are very important for data visualization and analysis.…”

Section: Related Workmentioning

confidence: 99%

“…range queries, out-of-range queries, and more complex queries like aggregation and down-sampling queries; • Scalability Performance: the ability to understand the performance of a TSDB as its size grows larger; • System Monitoring: the capability to monitor the usage of system resources. Existing TSDB benchmarks only support a limited set of queries or do not reflect on the scalability performance of a TSDB [12,19,27,28]. Our benchmark builds on previous efforts by providing queries from real-life scenarios, specifically scientific experiments, and by giving insights into the scalability performance of TSDBs.…”

Section: Introductionmentioning

confidence: 99%

SciTS: A Benchmark for Time-Series Database in Scientific Experiments and Industrial Internet of Things

Mostafa,

Wehbi,

Chilingaryan

et al. 2022

Preprint

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Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for timeseries data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL. CCS CONCEPTS• Information systems → DBMS engine architectures; Online analytical processing engines; Record and buffer management; Database design and models; Relational database model; • Applied computing → Astronomy; Physics.

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“…The TS-Benchmark is another TSDB benchmark centered on the requirements of managing huge time series data. It uses DCGAN based model to generate the high-quality synthesized data after being trained with real time series data [8]. For the benchmark, they used data from wind turbines.…”

Section: Time Series Benchmarksmentioning

confidence: 99%

Untitled

2022

IJDMS

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The growth of big-data sectors such as the Internet of Things (IoT) generates enormous volumes of data. As IoT devices generate a vast volume of time-series data, the Time Series Database (TSDB) popularity has grown alongside the rise of IoT. Time series databases are developed to manage and analyze huge amounts of time series data. However, it is not easy to choose the best one from them. The most popular benchmarks compare the performance of different databases to each other but use random or synthetic data that applies to only one domain. As a result, these benchmarks may not always accurately represent real-world performance. It is required to comprehensively compare the performance of time series databases with real datasets. The experiment shows significant performance differences for data injection time and query execution time when comparing real and synthetic datasets. The results are reported and analyzed.

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TS-Benchmark: A Benchmark for Time Series Databases

Cited by 26 publications

References 21 publications

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

SciTS: A Benchmark for Time-Series Database in Scientific Experiments and Industrial Internet of Things

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