TSCache

2021 ◽  
Vol 14 (13) ◽  
pp. 3253-3266
Author(s):  
Jian Liu ◽  
Kefei Wang ◽  
Feng Chen
Keyword(s):  
Time Series ◽  
High Speed ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Efficient System ◽  
Indexing Structure ◽  
System Solution ◽  
Challenges And Opportunities ◽  
Cache System

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

Analysis of Privacy Preservation Techniques in IoT

2019 ◽  
Vol 10 (3) ◽  
pp. 27-33
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

Analysis of Privacy Preservation Techniques in IoT

2021 ◽  
pp. 1343-1351
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

Low Cost Modular Telemetry for Coastal Time-Series Data

2002 ◽  
Author(s):  
Daniel E. Frye ◽  
W. R. Geyer ◽  
Bradford Butman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data

scholarly journals Automatic identification of differences in behavioral co-occurrence between groups

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yiming Tian ◽  
Takuya Maekawa ◽  
Joseph Korpela ◽  
Daichi Amagata ◽  
Takahiro Hara ◽  
...  
Keyword(s):  
Time Series ◽  
High Speed ◽  
Time Series Data ◽  
Natural Habitat ◽  
Sensor Data ◽  
Series Data ◽  
Physiological Data ◽  
Diving Behavior ◽  
Depth Sensors ◽  
Naive Method

Abstract Background Recent advances in sensing technologies have enabled us to attach small loggers to animals in their natural habitat. It allows measurement of the animals’ behavior, along with associated environmental and physiological data and to unravel the adaptive significance of the behavior. However, because animal-borne loggers can now record multi-dimensional (here defined as multimodal) time series information from a variety of sensors, it is becoming increasingly difficult to identify biologically important patterns hidden in the high-dimensional long-term data. In particular, it is important to identify co-occurrences of several behavioral modes recorded by different sensors in order to understand an internal hidden state of an animal because the observed behavioral modes are reflected by the hidden state. This study proposed a method for automatically detecting co-occurrence of behavioral modes that differs between two groups (e.g., males vs. females) from multimodal time-series sensor data. The proposed method first extracted behavioral modes from time-series data (e.g., resting and cruising modes in GPS trajectories or relaxed and stressed modes in heart rates) and then identified two different behavioral modes that were frequently co-occur (e.g., co-occurrence of the cruising mode and relaxed mode). Finally, behavioral modes that differ between the two groups in terms of the frequency of co-occurrence were identified. Results We demonstrated the effectiveness of our method using animal-locomotion data collected from male and female Streaked Shearwaters by showing co-occurrences of locomotion modes and diving behavior recorded by GPS and water-depth sensors. For example, we found that the behavioral mode of high-speed locomotion and that of multiple dives into the sea were highly correlated in male seabirds. In addition, compared to the naive method, the proposed method reduced the computation costs by about 99.9%. Conclusion Because our method can automatically mine meaningful behavioral modes from multimodal time-series data, it can be potentially applied to analyzing co-occurrences of locomotion modes and behavioral modes from various environmental and physiological data.

Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search

2019 ◽  
Vol 34 (05) ◽  
pp. 2050010 ◽  
Author(s):  
Peng Zhan ◽  
Changchang Sun ◽  
Yupeng Hu ◽  
Wei Luo ◽  
Jiecai Zheng ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Similarity Search ◽  
High Speed ◽  
High Performance ◽  
Time Series Data ◽  
Rapid Development ◽  
Feature Representation ◽  
Series Data ◽  
Data Points

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

Automatic Linear Disturbance Footprint Extraction Based on Dense Time-Series Landsat Imagery

2014 ◽  
Author(s):  
Zhaohua Chen ◽  
Bill Jefferies ◽  
Paul Adlakha ◽  
Bahram Salehi ◽  
Des Power
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Oil And Gas ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Series Data ◽  
Linear Features ◽  
Seismic Lines

Linear disturbances from the construction of pipelines, roads and seismic lines for oil and gas extraction and mining have caused landscape changes in Western Canada; however these linear features are not well recorded. Inventory maps of pipelines, seismic lines and temporary access routes created by resource exploration are essential to understanding the processes causing ecological changes in order to coordinate resource development, emergency response and wildlife management. Mapping these linear disturbances traditionally relies on manual digitizing from very high resolution remote sensing data, which usually limits results to small operational area. Extending mapping to large areas is challenging due to complexity of image processing and high logistical costs. With increased availability of low cost satellite data, more frequent and regular observations are available and offer potential solutions for extracting information on linear disturbances. This paper proposes a novel approach to incorporate spectral, geometric and temporal information for detecting linear features based on time series data analysis of regularly acquired, and low cost satellite data. This approach involves two steps: multi-scale directional line detection and line updating based on time series analysis. This automatic method can effectively extract very narrow linear features, including seismic lines, roads and pipelines. The proposed method has been tested over three sites in Alberta, Canada by detecting linear disturbances occurring over the period of 1984–2013 using Landsat imagery. It is expected that extracted linear features would be used to facilitate preparation of baseline maps and up-to-date information needed for environmental assessment, especially in extended remote areas.

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