Big Data: the current wave front of the tsunami

• Autorzy: Caldarola E. G. , Sacco M. , Terkaj W.
• Języki publikacji: EN

Abstrakty

EN

In recent years, a real tsunami has flooded many human activities. Genomics, Astronomy, Particle Physics and Social Sciences are just a few examples of fields which have been intensively invaded by a massive amount of data coming from simulation, experiments or exploration. This huge pile of data requires a new way to deal with, a real paradigmatic shift respect to the past as for theories, technologies or approaches in data management. This work outlines the current wave front of Big Data, starting from a possible characterization of this new paradigm to its most compelling applications and tools, with an exploratory research of Big Data challenges in manufacturing engineering.

Słowa kluczowe

EN

big data; big science; advanced manufacturing

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 10, no 4
• Strony: 7--16
• Opis fizyczny: Bibliogr. 31 poz., fig.

Twórcy

autor

Caldarola E. G.

• Big Data: the current wave front of the tsunami Caldarola E. G., Sacco M., Terkaj W. Keywords: big data, big science, advanced manufacturing Abstract: In recent years, a real tsunami has flooded many human activities. Genomics, Astronomy, Particle Physics and Social Sciences are just a few examples of fields which have been intensively invaded by a massive amount of data coming from simulation, experiments or exploration. This huge pile of data requires a new way to deal with, a real paradigmatic shift respect to the past as for theories, technologies or approaches in data management. This work outlines the current wave front of Big Data, starting from a possible characterization of this new paradigm to its most compelling applications and tools, with an exploratory research of Big Data challenges in manufacturing engineering. References [1] Hilbert M., Lopez P.: The World’s Technological Capacity to Store, Communicate, and Compute Information. Science 332, 60 (2011). [2] Castells M.: End of Millennium, The Information Age: Economy, Society and Culture. vol. III, Wiley-Blackwell, Malden, MA, 2000. [3] Franks B.: Taming the Big Data Tidal Wave. John Wiley & Sons, Inc., 2012. [4] Weinberg B.D. Davis L., Berger P.D.: Perspectives on Big Data. Journal of Marketing Analytics Vol. 1, 4, pp. 187-201. [5] McKinsey Global Institute, Big Data: The Next Frontier for Innovation, Competition, and Productivity, May 2011. [6] Merv A.: “Big Data,” Teradata Magazine Online, Q1, 2011. [7] Kerry Butters, Big Data Will Die Within Two Years, OneStopoClick Article, 15 May 2014. [8] Jacobs A.: The pathologies of big data. Communication of the ACM, August 2009, Vol. 52, N. 8. [9] Sagiroglu S., Sinanc D.: Big Data: A Review. International Conference on Collaboration Technologies and Systems (CTS), 2013. [10] Manyika J., Chui M., Brown B. et al.: Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 2011. [11] Mohanty S., Jagadeesh M., Srivatsa H.: Big Data Imperatives. Apress. [12] Jagadish H.V. Et al.: Big Data and its Technical Challenge. COMMUNICATIONS OF THE ACM, July 2014, Vol. 57, n. 7. [13] Gaber M. M., Zaslavsky A., Krishnaswamy S.: Mining Data Streams: A Review, ACM SIGMOD Record, Vol. 34, No. 2, June 2005, pp. 18-26. [14] Desouza K. C., Smith K. L.: Big Data for Social Innovation, Stanford Social Innovation Review, 2014. [15] Rijmenam M. Van: Think Bigger. Developing a successful big data strategy for your business, American Management Association, 2014. [16] Hey T., Tansley S., Tolle K.: The Fourth Paradigm. Data-Intensive Scientific Discovery. Microsoft Research, 2009. [17] Smolan R., Erwitt J.: The Human Face Of Big Data. Against All Odds Productions, 2012. [18] Pak A., Paroubek P.: Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREC, 2010. [19] Friedman T.L.: The World is Flat – A Brief History of the Twenty-first Century. Farrar, Straus, & Giroux, 2005. [20] Lee E.A.: Cyber Physical Systems: Design Challenges. Electrical Engineering and Computer Sciences University of California at Berkeley, 2008. [21] Kádár B., Terkaj W., Sacco M.: Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems. CIRP Annals Manufacturing Technology, 62(1), 2013, pp. 443-446. [22] Ghielmini G. et al.: Virtual Factory Manager for semantic data handling. CIRP Journal of Manufacturing Science and Technology, 2013, 6(4), pp. 281-291. [23] Palajová S., Figa Š., Gregor M.: Simulation of manufacturing and logistics systems for the 21th century, Applied Computer Science, V. 8, N. 1, 2012. [24] Chen D., Kjellberg T., Euler A. von: Software Tools for the Digital Factory – An Evaluation and Discussion. Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology Advances in Intelligent and Soft Computing Volume 66, 2010, pp. 803-812, V. 4, N. 2, 2008. [25] Kozłowski E., Gola A., Świć A.: Model of Production Control in Just-in-Time Delivery System Conditions. Advances in Manufacturing Science and Technology, Vol. 38, No. 1, 2014, pp. 77-88. [26] Modoni Ge, Sacco M., Terkaj W.: A survey of RDF store solutions. Engineering, Technology and Innovation (ICE), International ICE Conference 2014. [27] Gola A., Świć A.: Design of storage subsystem of flexible manufacturing system using the computer simulation method, Actual Problems of Economics, No. 4 (142) 2013, pp. 312-318. [28] Terkaj W., Urgo M.: Ontology-based modeling of production systems for design and performance evaluation. Proceedings of 12th IEEE INDIN, 2014, pp 748-753. [29] Pedrielli G., Sacco M., Terkaj W., Tolio T.: An HLA-based distributed simulation for networked manufacturing systems analysis. Journal of Simulation, 2012, 6(4): 237-252. [30] Dean J., Ghemawat S.: MapReduce: simplified data processing on large clusters, Communications of the ACM, 2008. [31] Amazon Web Services: Amazon Elastic Compute Cloud. User Guide for Microsoft Windows. API Version 2014-09-01, 2014.
• Dipartimento di Ingegneria Elettrica e Tecnologie Dell'Informazione – Università di Napoli “Federico II”, Via Claudio, 21, Napoli, Italy

autor

Sacco M.

• Institute of Industrial Technologies and Automation – National Research Council, Via E. Bassini, 15, Milano, Italy

autor

Terkaj W.

• Institute of Industrial Technologies and Automation – National Research Council, Via E. Bassini, 15, Milano, Italy

Bibliografia

• [1] Hilbert M., Lopez P.: The World’s Technological Capacity to Store, Communicate, and Compute Information. Science 332, 60 (2011).
• [2] Castells M.: End of Millennium, The Information Age: Economy, Society and Culture. vol. III, Wiley-Blackwell, Malden, MA, 2000.
• [3] Franks B.: Taming the Big Data Tidal Wave. John Wiley & Sons, Inc., 2012.
• [4] Weinberg B.D. Davis L., Berger P.D.: Perspectives on Big Data. Journal of Marketing Analytics Vol. 1, 4, pp. 187-201.
• [5] McKinsey Global Institute, Big Data: The Next Frontier for Innovation, Competition, and Productivity, May 2011.
• [6] Merv A.: “Big Data,” Teradata Magazine Online, Q1, 2011.
• [7] Kerry Butters, Big Data Will Die Within Two Years, OneStopoClick Article, 15 May 2014.
• [8] Jacobs A.: The pathologies of big data. Communication of the ACM, August 2009, Vol. 52, N. 8.
• [9] Sagiroglu S., Sinanc D.: Big Data: A Review. International Conference on Collaboration Technologies and Systems (CTS), 2013.
• [10] Manyika J., Chui M., Brown B. et al.: Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 2011.
• [11] Mohanty S., Jagadeesh M., Srivatsa H.: Big Data Imperatives. Apress.
• [12] Jagadish H.V. Et al.: Big Data and its Technical Challenge. COMMUNICATIONS OF THE ACM, July 2014, Vol. 57, n. 7.
• [13] Gaber M. M., Zaslavsky A., Krishnaswamy S.: Mining Data Streams: A Review, ACM SIGMOD Record, Vol. 34, No. 2, June 2005, pp. 18-26.
• [14] Desouza K. C., Smith K. L.: Big Data for Social Innovation, Stanford Social Innovation Review, 2014.
• [15] Rijmenam M. Van: Think Bigger. Developing a successful big data strategy for your business, American Management Association, 2014.
• [16] Hey T., Tansley S., Tolle K.: The Fourth Paradigm. Data-Intensive Scientific Discovery. Microsoft Research, 2009.
• [17] Smolan R., Erwitt J.: The Human Face Of Big Data. Against All Odds Productions, 2012.
• [18] Pak A., Paroubek P.: Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREC, 2010.
• [19] Friedman T.L.: The World is Flat – A Brief History of the Twenty-first Century. Farrar, Straus, & Giroux, 2005.
• [20] Lee E.A.: Cyber Physical Systems: Design Challenges. Electrical Engineering and Computer Sciences University of California at Berkeley, 2008.
• [21] Kádár B., Terkaj W., Sacco M.: Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems. CIRP Annals Manufacturing Technology, 62(1), 2013, pp. 443-446.
• [22] Ghielmini G. et al.: Virtual Factory Manager for semantic data handling. CIRP Journal of Manufacturing Science and Technology, 2013, 6(4), pp. 281-291.
• [23] Palajová S., Figa Š., Gregor M.: Simulation of manufacturing and logistics systems for the 21th century, Applied Computer Science, V. 8, N. 1, 2012.
• [24] Chen D., Kjellberg T., Euler A. von: Software Tools for the Digital Factory – An Evaluation and Discussion. Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology Advances in Intelligent and Soft Computing Volume 66, 2010, pp. 803-812, V. 4, N. 2, 2008.
• [25] Kozłowski E., Gola A., Świć A.: Model of Production Control in Just-in-Time Delivery System Conditions. Advances in Manufacturing Science and Technology, Vol. 38, No. 1, 2014, pp. 77-88.
• [26] Modoni Ge, Sacco M., Terkaj W.: A survey of RDF store solutions. Engineering, Technology and Innovation (ICE), International ICE Conference 2014.
• [27] Gola A., Świć A.: Design of storage subsystem of flexible manufacturing system using the computer simulation method, Actual Problems of Economics, No. 4 (142) 2013, pp. 312-318.
• [28] Terkaj W., Urgo M.: Ontology-based modeling of production systems for design and performance evaluation. Proceedings of 12th IEEE INDIN, 2014, pp 748-753.
• [29] Pedrielli G., Sacco M., Terkaj W., Tolio T.: An HLA-based distributed simulation for networked manufacturing systems analysis. Journal of Simulation, 2012, 6(4): 237-252.
• [30] Dean J., Ghemawat S.: MapReduce: simplified data processing on large clusters, Communications of the ACM, 2008.
• [31] Amazon Web Services: Amazon Elastic Compute Cloud. User Guide for Microsoft Windows. API Version 2014-09-01, 2014.