Apache IoTDB

Wang, Chen; Huang, Xiangdong; Qiao, Jialin; Jiang, Tingting; Rui, Lei; Zhang, Jinrui; Kang, Rong; Feinauer, Julian; McGrail, Kevin A.; Wang, Peng; Luo, Diaohan; Yuan, Jun; Wang, Jianmin; Sun, Jun

doi:10.14778/3415478.3415504

Cited by 52 publications

(10 citation statements)

References 2 publications

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“…It provides many query and analysis functions, including single value query, range query, multidimensional query, and aggregation analysis. It has been demonstrated (Wang et al, 2020) that Apache IoTDB has good performance in benchmark tests. Apache IoTDB is selected for comparison to better evaluate the comprehensive performance of RHTSDB.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, Byteseries proposes the compressed inverted index algorithm, which can ensure efficient ingestion and multidimensional query performance. Aiming at high throughput, low latency, and advanced timing analysis performance indicators of time-series data, Apache IoTDB (Wang et al, 2020) adopts the LSM Tree mechanism and provides high-performance data reading/ writing and rich query capabilities in the cloud. Apache IoTDB customizes an efficient directory organization structure for IoT scenarios.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

Ma,

Zhou,

2024

Journal of Database Management

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In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

Ma,

Zhou,

2024

Journal of Database Management

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“…There are multiple systems for indexing time series data on cloud servers (Wang et al, 2020;Yang et al, 2019). These systems cannot be adapted to the low-memory and CPU processing present on embedded sensor devices.…”

Section: Introductionmentioning

confidence: 99%

Using Learned Indexes to Improve Time Series Indexing Performance on Embedded Sensor Devices

Ding¹,

Carvalho²,

Lawrence³

2023

Preprint

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Efficiently querying data on embedded sensor and IoT devices is challenging given the very limited memory and CPU resources. With the increasing volumes of collected data, it is critical to process, filter, and manipulate data on the edge devices where it is collected to improve efficiency and reduce network transmissions. Existing embedded index structures do not adapt to the data distribution and characteristics. This paper demonstrates how applying learned indexes that develop space efficient summaries of the data can dramatically improve the query performance and predictability. Learned indexes based on linear approximations can reduce the query I/O by 50 to 90% and improve query throughput by a factor of 2 to 5, while only requiring a few kilobytes of RAM. Experimental results on a variety of time series data sets demonstrate the advantages of learned indexes that considerably improve over the state-of-the-art index algorithms.

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“…The massive volume and complexity bring greater challenges to data analysis. To address this, various technologies are employed to manage [ 11 ], store [ 12 ], process, and analyze [ 13 ] time series data aiming to extract useful information from the data.…”

Section: Introductionmentioning

confidence: 99%

Survey of Time Series Data Generation in IoT

Hu,

Sun,

et al. 2023

Sensors

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Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time series data are being generated. Time series data play an important role in scientific and technological research for conducting experiments and studies to obtain solid and convincing results. However, due to privacy restrictions, limited access to time series data is always an obstacle. Moreover, the limited available open source data are often not suitable because of a small quantity and insufficient dimensionality and complexity. Therefore, time series data generation has become an imperative and promising solution. In this paper, we provide an overview of classical and state-of-the-art time series data generation methods in IoT. We classify the time series data generation methods into four major categories: rule-based methods, simulation-model-based methods, traditional machine-learning-based methods, and deep-learning-based methods. For each category, we first illustrate its characteristics and then describe the principles and mechanisms of the methods. Finally, we summarize the challenges and future directions of time series data generation in IoT. The systematic classification and evaluation will be a valuable reference for researchers in the time series data generation field.

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Apache IoTDB

Cited by 52 publications

References 2 publications

An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

Using Learned Indexes to Improve Time Series Indexing Performance on Embedded Sensor Devices

Survey of Time Series Data Generation in IoT

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