Big Data Meet Green Challenges: Big Data Toward Green Applications

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.

[1]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[2]  Qiang Sun,et al.  Research on State Environmental Data Center Construction , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[3]  Ram Rajagopal,et al.  Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.

[4]  Yu Fang,et al.  Effectively Modeling Environmental Pollution by Using the GIS Database , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[5]  Christian Icking,et al.  Air Pollution and Fog Detection through Vehicular Sensors , 2014, 2014 8th Asia Modelling Symposium.

[6]  Christoph Waldmann,et al.  European multidisciplinary seafloor and water-column observatory (EMSO): Power and Internet to European waters , 2014, 2014 Oceans - St. John's.

[7]  James H. Faghmous,et al.  A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science , 2014, Big Data.

[8]  G. Andria,et al.  Modeling study in remote sensing of air pollution measured data , 2000, Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference [Cat. No. 00CH37066].

[9]  Zhang Weiguo,et al.  Knowledge-based environmental information system for sustainable development of wetland area , 2010, The 2nd International Conference on Software Engineering and Data Mining.

[10]  Katherine G. Herbert,et al.  Scientific Data Infrastructure for Sustainability Science Mobile Applications , 2014, 2014 IEEE International Congress on Big Data.

[11]  P. Pedrazzoli,et al.  Towards sustainability assessment: Reference data model for integrated product, process, supply chain design , 2012, 2012 18th International ICE Conference on Engineering, Technology and Innovation.

[12]  Zhang Juan,et al.  Economic growth and industrial pollution: Evidence from the urban panel data in China , 2011, 2011 International Conference on E-Business and E-Government (ICEE).

[13]  Song Guo,et al.  Big Data Meet Green Challenges: Greening Big Data , 2016, IEEE Systems Journal.

[14]  Manish Marwah,et al.  Knowledge discovery and data mining for enhanced sustainability of physical ecosystems , 2009, 2009 IEEE International Symposium on Sustainable Systems and Technology.

[15]  Antonio Garzon,et al.  Using Space-Based Downstream Services for Urban Management in Smart Cities , 2014, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing.

[16]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[17]  Miguel A. Labrador,et al.  P-Sense: A participatory sensing system for air pollution monitoring and control , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[18]  Osamu Saeki,et al.  Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data , 2006 .

[19]  T. Karl,et al.  A Revised U.S. Climate Extremes Index , 2008 .

[20]  Wei Chen,et al.  Research on water environment automatic monitoring evaluation system for ecological compensation , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[21]  Jin Dong,et al.  Big data technologies in support of real time capturing and understanding of electric vehicle customers dynamics , 2014, 2014 IEEE 5th International Conference on Software Engineering and Service Science.

[22]  Xiaoli Li,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 Classification of Energy Consumption in Buildings with Outlier Detection , 2022 .

[23]  Hussnain Ahmed.  Applying Big Data analytics for energy efficiency. , 2014 .

[24]  Shuhong Si,et al.  The pollution degree of heavy metals to city soil environment , 2012, 2012 International Symposium on Geomatics for Integrated Water Resource Management.

[25]  Ana Aguiar,et al.  Mining geographic data for fuel consumption estimation , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[26]  M. Boznar,et al.  Perceptron neural network-based model predicts air pollution , 1997, Proceedings Intelligent Information Systems. IIS'97.

[27]  J. Audet.  New tools for the ocean pollution data and information network , 1985, OCEANS '85 - Ocean Engineering and the Environment.

[28]  Abbas Alimohammadi,et al.  Analysis of the Spatio-Temporal Patterns of Water Pollution and Source Contribution Using the MODIS Sensor Products and Multivariate Statistical Techniques , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Ning Ling Wang,et al.  Big Data Analytics-Based Energy-Consumption Feature Selection of Large Thermal Power Units , 2013 .

[30]  Young-geun Kim,et al.  The change of electromagnetic environments in the Smart Grid , 2013, 2013 2nd International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST).

[31]  Ashiq Anjum,et al.  Cloud Based Big Data Analytics for Smart Future Cities , 2013, UCC.

[32]  S. Wojciech,et al.  The integrated computer system for modelling of air pollution based on the digital data , 2009, 2009 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[33]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[34]  Jibonananda Sanyal,et al.  Simulation and big data challenges in tuning building energy models , 2013, 2013 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES).

[35]  Decheng Fan,et al.  Analysis on the Status of the Characteristics and Distribution of Industrial Water Pollution in China , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[36]  Abdolhossein Sarrafzadeh,et al.  SVM aggregation modelling for spatio-temporal air pollution analysis , 2014, 17th IEEE International Multi Topic Conference 2014.

[37]  Paul Rowley,et al.  From smart homes to smart communities: Advanced data acquisition and analysis for improved sustainability and decision making , 2013, International Conference on Information Society (i-Society 2013).

[38]  Jukka Huhtamäki,et al.  Understanding Business Ecosystem Dynamics: A Data-Driven Approach , 2015, TMIS.

[39]  Haluk Demirkan,et al.  The Next Wave of Sustainable IT , 2011, IT Professional.

[40]  E. Pritchard,et al.  An integrated approach to the study of pollution transport and waves in the coastal zone - Part III: The integrated program and satellite requirements , 1974 .

[41]  Yi Wang,et al.  Identifying Potential Pollution Sources in River Basin via Water Quality Reasoning Based Expert System , 2013, 2013 Fourth International Conference on Digital Manufacturing & Automation.

[42]  Yu Tian,et al.  Impact of Big Data on China Smart River Basin , 2014, 2014 7th International Conference on Intelligent Computation Technology and Automation.

[43]  Duncan Waga,et al.  Environmental Conditions’ Big Data Management and Cloud Computing Analytics for Sustainable Agriculture , 2013 .

[44]  Illhoi Yoo,et al.  Data Mining in Healthcare and Biomedicine: A Survey of the Literature , 2012, Journal of Medical Systems.

[45]  W. Bressette,et al.  An integrated approach to the study of pollution transport and waves in the costal zone- Part I: Data acquisition - Remote and in SITU sensing , 1974 .

[46]  A. Vega-Corona,et al.  Data fusion and neural network combination method for air pollution level monitoring , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[47]  Kenneth A. Loparo,et al.  Big data analytics in power distribution systems , 2015, 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[48]  Junwei Cao,et al.  Energy Internet -- Towards Smart Grid 2.0 , 2013, 2013 Fourth International Conference on Networking and Distributed Computing.

[49]  M. Mayilvaganan,et al.  A cloud-based architecture for Big-Data analytics in smart grid: A proposal , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[50]  Soon-Thiam Khu,et al.  Status and challenges of water pollution problems in China: learning from the European experience , 2014, Environmental Earth Sciences.

[51]  Hsiao-Hua K. Burke.  Detection of Regional Air Pollution Episodes Utilizing Satellite Digital Data in the Visual Range , 1982, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Pavlos S. Kanaroglou,et al.  An investigation of air pollution in southern Ontario, Canada, with MODIS and MISR Aerosol Data , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[53]  Cisco Visual Networking Index: Forecast and Methodology 2016-2021.(2017) http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual- networking-index-vni/complete-white-paper-c11-481360.html. High Efficiency Video Coding (HEVC) Algorithms and Architectures https://jvet.hhi.fraunhofer. , 2017 .

[54]  Eddie Soulier,et al.  “Sustainable assemblage for energy (SAE)” inside intelligent urban areas: How massive heterogeneous data could help to reduce energy footprints and promote sustainable practices and an ecological transition , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[55]  Anna Fensel,et al.  OpenFridge: A platform for data economy for energy efficiency data , 2013, 2013 IEEE International Conference on Big Data.

[56]  Andrew J. Connolly,et al.  Statistics, Data Mining, and Machine Learning in Astronomy , 2014 .

[57]  Byung-Won On,et al.  A big data management system for energy consumption prediction models , 2014, Ninth International Conference on Digital Information Management (ICDIM 2014).

[58]  Shouzhi Xu,et al.  The comprehensive evaluation method of the heavy metals pollution in the topsoil of the city , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[59]  M. Filice,et al.  Intelligent Data Analysis in Environmental Sampling , 2005, 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[60]  A. Benmecheta,et al.  Monitoring of Oil Pollution by GIS and Remote-Sensing case Of West Algeria Harbours , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[61]  Luciano Tarricone,et al.  A Linked Data Approach to Electromagnetic Pollution Monitoring , 2013, 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing.

[62]  Sun-Kyoung Park,et al.  Sustainable Development Plan for Korea through Expansion of Green IT: Policy Issues for the Effective Utilization of Big Data , 2015 .

[63]  Tim Farnham,et al.  ICT INFRASTRUCTURE FOR EFFICIENT WATER RESOURCES MANAGEMENT: TOWARDS HARMONIZATION OF A FRAGMENTED FIELD , 2015 .

[64]  Ling Luo,et al.  Pollution of renewable energies to the grid: Measured data and statistical analysis via large-scale urban power quality monitoring system , 2013, 2013 IEEE Global High Tech Congress on Electronics.

[65]  Mervin F. Fingas,et al.  Review of oil spill remote sensing , 1997 .

[66]  Xin Miao,et al.  The opportunity and challenge of Big Data's application in distribution grids , 2014, 2014 China International Conference on Electricity Distribution (CICED).

[67]  Caitlin D Cottrill,et al.  Leveraging Big Data for the Development of Transport Sustainability Indicators , 2015 .

[68]  Daniele Miorandi,et al.  SUPERHUB: a user-centric perspective on sustainable urban mobility , 2012, Sense Transport '12.

[69]  Chih-Hung Wu,et al.  Collecting and Mining Big Data for Electric Vehicle Systems Using Battery Modeling Data , 2015, 2015 12th International Conference on Information Technology - New Generations.

[70]  M. Fingas,et al.  Review of oil spill remote sensing. , 2014, Marine pollution bulletin.

[71]  M. Jelinek,et al.  Innovation as the strategic driver of sustainability: big data knowledge for profit and survival , 2013, IEEE Engineering Management Review.

[72]  Shengqi Yang,et al.  Hierarchical air quality monitoring system design , 2014, 2014 International Symposium on Integrated Circuits (ISIC).

[73]  Xiaowei Zhao,et al.  Spatial externalities, environmental pollution and total factor productivity growth in China — Evidence from a spatial panel data set of provincial-level , 2011, Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services.

[74]  Victor E. DeBrunner,et al.  Multi-resolution and multi-sensor data fusion for remote sensing in detecting air pollution , 2002, Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation.

[75]  Schahram Dustdar,et al.  M2M Platform-as-a-Service for sustainability governance , 2012, 2012 Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA).

[76]  D. A. Dickinson,et al.  Assessing integrated circuit manufacturing for environmental performance and sustainability: a full scale IC business application , 2003, IEEE International Symposium on Electronics and the Environment, 2003..

[77]  Yonghui Song,et al.  The analyses of environmental pollution accidents from 1992 to 2008 in China and the management proposals , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[78]  Zhou Guobing,et al.  The Research of Urban Air Pollution Weather Characteristics under the Special Terrain , 2010, 2010 International Conference on Digital Manufacturing & Automation.

[79]  Brian Bojarski,et al.  Crowd-sourcing urban air-quality in developing countries through open source technologies , 2013, 2013 IEEE Global Humanitarian Technology Conference (GHTC).

[80]  Peng Wenfu,et al.  Dynamical Detection on Urban Sprawl Based on EO Data , 2009, 2009 International Forum on Information Technology and Applications.

[81]  José M. F. Moura,et al.  Big Data + Big Cities: Graph Signals of Urban Air Pollution [Exploratory SP] , 2014, IEEE Signal Processing Magazine.

[82]  Stephen Chi-fai Chan,et al.  Web agents for spatial mining on air pollution meteorology , 2001, Proceedings of the Sixth International Conference on Computer Supported Cooperative Work in Design (IEEE Cat. No.01EX472).

[83]  Siyao Lv,et al.  Assessment of heavy metal pollution in green area soil along Jilin city street , 2012, World Automation Congress 2012.

[84]  Ian Gorton,et al.  Large-Scale Data Challenges in Future Power Grids , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[85]  C. Wells,et al.  Stochastic regional air pollution modeling , 1971, CDC 1971.

[86]  Ivan Rodero,et al.  Cloud-Based Data Analytics Framework for Autonomic Smart Grid Management , 2014, 2014 International Conference on Cloud and Autonomic Computing.

[87]  R. E. Turner,et al.  An integrated approach to the study of polLution transport and waves in the coastal zone - Part II: Modeling , 1974 .

[88]  Yanchi Liu,et al.  Diagnosing New York city's noises with ubiquitous data , 2014, UbiComp.

[89]  Kasper Hornbæk,et al.  Big data from the built environment , 2011, LARGE '11.

[90]  Ebrahim Sahafizadeh,et al.  Prediction of Air Pollution of Boushehr City Using Data Mining , 2009, 2009 Second International Conference on Environmental and Computer Science.