scholarly journals A low cost and highly accurate technique for big data spatial-temporal interpolation

2020 ◽  
Vol 153 ◽  
pp. 492-502 ◽  
Author(s):  
M. Esmaeilbeigi ◽  
O. Chatrabgoun ◽  
A. Hosseinian-Far ◽  
R. Montasari ◽  
A. Daneshkhah
Keyword(s):  
Big Data ◽  
Low Cost ◽  
Temporal Interpolation

scholarly journals A study on the development of a light scattering particulate matter sensor and monitoring system

2020 ◽  
Vol 17 (3) ◽  
pp. 867-890
Author(s):  
Jun-Hee Choi ◽  
Hyun-Sug Cho
Keyword(s):  
Big Data ◽  
Particulate Matter ◽  
Light Scattering ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Gravimetric Method ◽  
Correction Method ◽  
Measurement Methods ◽  
Linear Correction

The gravimetric method, which is mainly used among particulate matter (PM) measurement methods, includes the disadvantages that it cannot measure PM in real time and it requires expensive equipment. To overcome these disadvantages, we have developed a light scattering type PM sensor that can be manufactured at low cost and can measure PM in real time. We have built a big data system that can systematically store and analyze the data collected through the developed sensor, as well as an environment where PM states can be monitored mobile in real time using such data. In addition, additional studies were conducted to analyze and correct the collected big data to overcome the problem of low accuracy, which is a disadvantage of the light scattering type PM sensor. We used a linear correction method and proceeded to adopt the most suitable value based on error and accuracy.

Low-cost IoT, Big Data, and Cloud Platform for Developing Countries

2017 ◽  
pp. 285-299 ◽  
Author(s):  
Corentin Dupont ◽  
Tomas Bures ◽  
Mehdi Sheikhalishahi ◽  
Congduc Pham ◽  
Abdur Rahim
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Low Cost ◽  
Cloud Platform

scholarly journals Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis

2018 ◽  
Vol 10 (1) ◽  
pp. 19-37 ◽  
Author(s):  
Alfons Weersink ◽  
Evan Fraser ◽  
David Pannell ◽  
Emily Duncan ◽  
Sarah Rotz
Keyword(s):  
Big Data ◽  
Low Cost ◽  
Animal Health ◽  
Soil Conditions ◽  
Decision Support Tools ◽  
Environmental Footprint ◽  
Digital Revolution ◽  
Agricultural Revolution ◽  
Support Tools ◽  
Crop Development

Agriculture stands on the cusp of a digital revolution, and the same technologies that created the Internet and are transforming medicine are now being applied in our farms and on our fields. Overall, this digital agricultural revolution is being driven by the low cost of collecting data on everything from soil conditions to animal health and crop development along with weather station data and data collected by drones and satellites. The promise of these technologies is more food, produced on less land, with fewer inputs and a smaller environmental footprint. At present, however, barriers to realizing this potential include a lack of ability to aggregate and interpret data in such a way that it results in useful decision support tools for farmers and the need to train farmers in how to use new tools. This article reviews the state of the literature on the promise and barriers to realizing the potential for Big Data to revolutionize agriculture.

scholarly journals Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yan-Ge Tian ◽  
Zheng-Nan Zhang ◽  
Shuang-Qi Tian
Keyword(s):  
Big Data ◽  
Nondestructive Testing ◽  
Near Infrared ◽  
Low Cost ◽  
Chemical Properties ◽  
Quality Analysis ◽  
Research Progress ◽  
Sensor Technology ◽  
Wheat Quality ◽  
Algorithm Optimization

Nondestructive testing with sensor technology is one of the fastest growing and most promising wheat quality information analysis technologies. Nondestructive testing with sensor technology benefits from the latest achievement of many disciplines such as computer, optics, mathematics, chemistry, and chemometrics. It has the advantages of simplicity, speed, low cost, no pollution, and no contact. It is widely used in wheat quality analysis and testing research. This article summarizes nondestructive testing with sensor technology for wheat quality, including the mechanical model, hyperspectral technology, Raman spectroscopy, and near-infrared techniques for wheat mechanical properties, storage properties, and physical and chemical properties (such as moisture, ash, protein, and starch) in the past decade. Based on the current research progress, big data technology needs a lot of research in spectral data mining, modeling algorithm optimization, model robustness, etc. to provide more data support and method reference for the research and application of wheat quality.

scholarly journals Integrated High-Performance Platform for Fast Query Response in Big Data with Hive, Impala, and SparkSQL: A Performance Evaluation

2018 ◽  
Vol 8 (9) ◽  
pp. 1514 ◽  
Author(s):  
Bao Chang ◽  
Hsiu-Fen Tsai ◽  
Yun-Da Lee
Keyword(s):  
Big Data ◽  
Open Source Software ◽  
High Performance ◽  
Low Cost ◽  
Data Retrieval ◽  
Fast Response ◽  
Disk Cache ◽  
Memory Cache ◽  
Medium Enterprise ◽  
Effectiveness And Efficiency

This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an open-source software solution with low cost for small-to-medium enterprise use. In practice, the proposed approach provides an in-memory cache and an in-disk cache to achieve a very fast response to a query if a cache hit occurs. Moreover, this paper develops so-called platform selection that is able to select the appropriate tool dealing with input query with effectiveness and efficiency. As a result, the speed of job execution of proposed approach using platform selection is 2.63 times faster than Hive in the Case 1 experiment, and 4.57 times faster in the Case 2 experiment.

Real-Time Monitoring of Intercity Passenger Flows Based on Big Data

2017 ◽  
Vol 8 (4) ◽  
pp. 120-128 ◽  
Author(s):  
Zheng Li ◽  
Yan Wang ◽  
Qin Chen
Keyword(s):  
Big Data ◽  
Real Time ◽  
Public Transport ◽  
Low Cost ◽  
Decision Support Tool ◽  
Population Mobility ◽  
Population Movements ◽  
Policy Makers ◽  
Support Tool ◽  
Passenger Flows

Managing population mobility is a key to urban growth and sustainable development. This study uses administrative and business data from a number of trustworthy and publicly-available websites for public transport to access passenger flows in a real-time manner. A case study is used to illustrate the application, with intercity passenger flows by public transport mode (rail or air), by rail service type and by time. Moreover, a model is developed for monitoring the implications of population movements, which can be a decision support tool for governments and policy makers to manage population mobility. The big-data approach to accessing public transport passenger movement has the following characteristics: (1) low cost, (2) a population scale, (3) instantaneous data collection/update, and (4) high quality.

Des Translators neue Kleider. Die Translationwirtschaft in Zeiten von Digitalisierung, Datafizierung und Big Data Management

2017 ◽  
Vol 62 (2) ◽  
Author(s):  
Martin Forstner
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Big Data ◽  
Internet Of Things ◽  
Data Management ◽  
Low Cost ◽  
The Internet ◽  
Computer Linguistics ◽  
Translation Services ◽  
The Internet Of Things

AbstractThe Internet of things will influence all professional environments, including translation services. Advances in machine learning, supported by accelerating improvements in computer linguistics, have enabled new systems that can learn from their own experience and will have repercussions on the workflow processes of translators or even put their services at risk in the expected digitalized society. Outsourcing has become a common practice and working in the cloud and in the crowd tend to enable translating on a very low-cost level. Confronted with promising new labels like

scholarly journals Big Data analysis: Intelligent Transportation Development Engine

2014 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Qin Xiao
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Information Age ◽  
Large Data ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transportation Demand ◽  
Advantages And Disadvantages ◽  
Transportation Development

<p>With the development of the times, people have unwittingly entered the information age. The information age has become a large amount of data bursting characteristics of the new era. In this feature people still seek to improve the production and quality of life. For the development of intelligent transportation needs of people's lives and make the real world, but in the construction of intelligent transportation among a large number of information data also adds to its change and difficulty, how to build an intelligent era of big data, security, low-cost, efficient and convenient of intelligent transportation systems become today people study. From the era of big data to intelligent traffic changes brought advantages and disadvantages, the era of big data to bring intelligent traffic problems and challenges, as well as the integration platform for massive data intelligent transportation intelligent transportation demand and large data structures has done a simple elaborate, it can provide some suggestions for areas of research that scientists.</p>

scholarly journals Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

2018 ◽  
Vol 1 ◽  
pp. 1-4
Author(s):  
Lorato Tlhabano
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Low Cost ◽  
High Tech ◽  
Test Case ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Dramatic Rise ◽  
Close Range ◽  
Laws And Policies

Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques’ and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

Sign in / Sign up

Export Citation Format

Share Document