Low Cost Modular Telemetry for Coastal Time-Series Data

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%.

Download Full-text

Analysis of Privacy Preservation Techniques in IoT

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2019070104 ◽

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.

Download Full-text

Analysis of Privacy Preservation Techniques in IoT

Research Anthology on Privatizing and Securing Data ◽

10.4018/978-1-7998-8954-0.ch063 ◽

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.

Download Full-text

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

Volume 1: Design and Construction; Environment; Pipeline Automation and Measurement ◽

10.1115/ipc2014-33172 ◽

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.

Download Full-text

Time Series Data Storage System Based on Big Data Technologies: Architecture and Implementation

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2016-0279 ◽

2017 ◽

Vol 18 (3) ◽

Author(s):

Kehe Wu ◽

Yayun Zhu ◽

Zuge Chen

Keyword(s):

Time Series ◽

Data Storage ◽

Credit Card ◽

Large Scale ◽

Time Series Data ◽

Distributed Storage ◽

Low Cost ◽

Series Data ◽

Proof Of Concept ◽

Smart Meters

AbstractA steady usage growth of various smart meters/sensors types comes along with the development of Smart Grid. Due to that smart meters/sensors usually produce data continuously, the state-of-the-art storage architecture will no longer be sustainable under such a dramatic data increase circumstance. It is crucial that these data shall be stored for both short and long term analysis to seek valuable information. This paper proposed a scalable and distributed storage architecture for time series data, and further substantiated the proposed storage framework on a proof-of-concept testbed using a cluster of low-cost and credit-card-sized single-board computers. We evaluated the proposed distributed storage framework and proof-of-concept testbed with a comprehensive set of performance measures. Results showed that low-cost hardware and open source technologies could be utilized for large-scale time series data storage.

Download Full-text

Tank irrigation in India: a time series analysis

Water Policy ◽

10.2166/wp.2006.063 ◽

2007 ◽

Vol 9 (2) ◽

pp. 193-216 ◽

Cited By ~ 12

Author(s):

A. Narayanamoorthy

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Growth Pattern ◽

Field Survey ◽

Time Series Data ◽

Low Cost ◽

National Level ◽

Series Data ◽

Level Data ◽

Policy Measures

Tanks are one of the important traditional sources of irrigation in India, irrigating nearly three million hectares even today. Though considered to be a low cost source with few environmental problems, the performance of tank irrigation has been poor and has deteriorated over the years. Several studies have analysed the performance of tank irrigation using field survey data, but not many studies seem to have analysed the trends and determinants of tank irrigation covering various states as well as national level data in recent times. In this study, therefore, using time series data from 1950/51 to 1999/2000, an attempt is made (a) to study the growth pattern of tank irrigation across different periods both at the national as well as across states level, (b) to study the nexus between rainfall and area under tank irrigation at a specific state, which has relatively larger area under tank irrigation, (c) to find out the losers and gainers of tank irrigation among different size of farmers, and (d) to suggest policy measures to rejuvenate tank irrigation in India.

Download Full-text

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

Download Full-text

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Download Full-text