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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 management |
Research Field: | Environmental management |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the environmental sciences |
UTAS 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: | 2017-11-01 |
Downloads: | 2 View Download Statistics |
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