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Recommending Environmental Big Data Using Semantically Guided Machine Learning

Citation

Dutta, R and Morshed, A and Aryal, J, Recommending Environmental Big Data Using Semantically Guided Machine Learning, Large Scale and Big Data: Processing and Management, CRC Press, S Sakr, MM Gaber (ed), Florida, USA, pp. 463-494. ISBN 978-1-4665-8150-0 (2014) [Research Book Chapter]

Copyright Statement

Copyright 2014 Taylor & Francis Group, LLC

Official URL: http://www.crcpress.com/product/isbn/9781466581500

Abstract

In information technology, Big Data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data-processing applications. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared with separate smaller sets with the same total amount of data. Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genetics, complex physics simulations, and environmental research. Wireless technology-based automated data gathering from the large environmental sensor networks have increased the quantity of sensor data available for analysis and sensor informatics. Next-generation environmental monitoring, natural resource management, and agricultural decision support systems are becoming heavily dependent on very large scale multiple sensor network deployments, massive-scale accumulation, harmonization, web-based Big Data integration and interpretation of Big Data. With large amount of the data availability, the complexity of data has also increased hence regular maintenance of large-scale sensor are becoming a difficult challenge. Uncertainty factors in the environmental monitoring processes are more evident than before due to current technological transparency achieved by most recent advanced communication technologies. The other challenges include capture, storage, search, sharing, analysis, and visualization. Data availability from a particular environmental sensor web is often very limited and data quality is subsequently very poor. This practical limitation could be due to difficult geographical location of the sensor node or sensor station, extreme environmental conditions, communication network failure, and lastly technical failure of the sensor node. Data uncertainty from a sensor network makes the network unreliable and inefficient. This inefficiency leads to failure of natural resource management systems such as agricultural water resource management, weather forecast, crop management including irrigation scheduling and natural resource-based crop business model systems. The ultimate challenge in environmental forecasting and decision support systems, is to overcome the data uncertainty and make the derived output more accurate. It is evident that there is a need to capture and integrate environmental knowledge from various independent sources including sensor networks, individual sensory system, large-scale environmental simulation models, and historical environmental data for each of the independent sources). It is not good enough to produce efficient decision support system using a single data source. So there is an urgent requirement for on demand complementary knowledge integration where different sources of environmental sensor data could be used to complement each other automatically.

Item Details

Item Type:Research Book Chapter
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Environmental Management
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Environmental Sciences
Author:Aryal, J (Dr Jagannath Aryal)
ID Code:86619
Year Published:2014
Deposited By:Geography and Environmental Studies
Deposited On:2013-10-03
Last Modified:2015-04-23
Downloads:2 View Download Statistics

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