Using Data Analytics To Extract Knowledge From Middle-Of-Life Product Data
Volume 4 - Issue 1, January 2020 Edition
[Download Full Paper]
Fatima-Zahra Abou Eddahab, Imre Horváth
Data analytics, middle-of-life data, analytics tools, application practices, smart analytics toolbox.
Data analytics needs dedicated tools, which are getting complex. In this paper we summarize the results of our literature research done with special attention to existing and potential future tools. The attention is paid mainly to processing big data, rather than to effective semantic processing of middle-of-life data (MoLD). The issue of handling of big MoLD of specific characteristics efficiently has not yet been addressed by the commercialized tools and methods. Another major limitation of the current tools is that they are typically not capable to adapt themselves to designers’ needs and to support knowledge/experience reusability in multiple design tasks. MoLD require quasi-real life handling due to their nature and direct feedback relationships to the operation process and the environment of products. Nowadays products are equipped with smart capabilities. This offers new opportunities for exploiting MoLD. The knowledge aggregated in this study will be used in the development of a toolbox, which (i) integrates various tools under a unified interface, (ii) implements various smart and semantics orientated functions, and (iii) facilitates data transformations in contexts by the practicing designers themselves.
 Z. Bi, and D. Cochran, “Big data analytics with applications,” Journal of Management Analytics, 1(4), pp. 249-265, 2014.
 K. Bodenhoefer, A. Schneider, T. Cock, A. Brooks, G. Sands, L. Allman, and O. Delannoy, “Environmental life cycle information management and acquisition – First experiences and results from field trials,” In Proceedings of Electronics Goes Green, pp. 5-8, 2004
 H.-B. Jun, D. Kiritsis, and P. Xirouchakis, “Research issues on closed-loop PLM,” Computers in Industry, 58(8), pp. 855-868, 2007.
 Falcon Project, “Feedback mechanisms Across the Lifecycle for Customer-driven Optimization of iNnovative product-services”, available at: http://www.falcon-h2020.eu, 2015.
 M. Franke, P. Klein, L. Schröder, and K.-D. Thoben, “Ontological Semantics of Standards and PLM Repositories in the Product Development Phase,” Global Product Development, pp. 473-482, 2011.
 A. Saaksvuori, and A. Immonen, Product lifecycle management (2 Ausgabe), Springer, 2005.
 K. Bongard-Blanchy, and C. Bouchard, “Dimensions of User Experience from the Product Design Perspective,” Journal d'Interaction Personne-Système, 3(1), pp. 1-15, 2014.
 A. Katal, M. Wazid, and R. Goudar, “Big Data: Issues, Challenges, Tools and Good Practices,”. In Proceedings of the 6th International Conference on Contemporary Computing (IC3’13), pp. 404-409, 2013.
 S. Terzi, A. Bouras, D. Dutta, M. Garetti, and D. Kiritsis, “Product Lifecycle Management from its History to its new Role,” International Journal of Product Lifecycle Management, 4(4), pp. 360-389, 2010.
 W.F. van der Vegte, “Taking Advantage of Data generated by Products: Trends, Opportunities and Challenges”, In Proceedings of the ASME Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE, pp. V01BT02A025-V01BT02A025, 2016.
 H. Shu, “Big Data Analytics: Six Techniques,” Geo-spatial Information Science, pp. 1-10, 2016.
 T. H. Davenport, and L. Prusak, Working Knowledge: How Organizations Manage What They Know, Harvard Business Press, Buston, 1998.
 A. Bufardi, D. Kiritsis, and P. Xirouchakis, “Generation of Design Knowledge from Product Life Cycle Data,” Methods and Tools for Effective Knowledge Life-Cycle-Management, pp. 375-389, 2008.
 D. Che, M. Safran, & Z. Peng, “From Big Data to Big Data Mining: Challenges, Issues, and Opportunities,” In Proceedings of the International Conference on Database Systems for Advanced Applications, pp. 1-15, 2013.
 V. Mayer-Schönberger, and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt, 2013.
 F. Xhafa, & L. Barolli, “Semantics, Intelligent Processing and Services for Big Data,” Future Generation Computer Systems, 37, pp. 201–202, 2014.
 D. Kiritsis, “Ubiquitous Product Lifecycle Management Using Product Embedded Information Devices,” In Proceedings of the International Conference in Intelligent Maintenance Systems (IMS’2004), x-x, 2004.
 J. Guttag, “Abstract Data Types and the Development of Data Structures,” Communications of the ACM, 20(6), pp. 396-404, 1977.
 M.L. Brodie, “On the development of data models,” On conceptual modelling, pp. 19-47, 1984.
 C. Ballard, D. Herreman, D. Schau, R. Bell, E. Kim, and A. Valencic, Data Modeling Techniques for Data Warehousing”, IBM Corporation International Technical Support Organization, 1998.
 S.R. Gunn, M. Brown, and K.M. Bossley, “Network Performance Assessment for Neurofuzzy Data Modeling,” Lecture Notes in Computer Science, 1280, pp. 313-323, 1997.
 N. Jia, M. Xie, and X. Chai, “Development and Implementation of a GIS-based Safety Monitoring System for Hydropower Station Construction,” Journal of Computing in Civil Engineering, 26(1), pp. 44-53, 2011.
 N. Jukic, “Modeling strategies and alternatives for data warehousing projects,” Communications of the ACM, 49(4), pp. 83-88, 2006.
 M.F. Worboys, H.M. Hearnshaw, and D.J. Maguire, “Object-oriented Data Modelling for Spatial Databases,” International Journal of Geographical Information System, 4(4), pp. 369-383, 1990.
 M. Molenaar, “A Syntactic Approach for Handling the Semantics of Fuzzy Spatial Objects,” Geographic objects with indeterminate boundaries, 2, pp. 207-224, 1996.
 P. Shoval, and S. Shiran, “Entity-relationship and Object-oriented Data Modeling - An Experimental Comparison of Design Quality,” Data & Knowledge Engineering, 21(3), pp. 297-315, 1997.
 C. Coronel, and S. Morris, Database Systems: Design, Implementation & management, Cengage Learning, Boston, 2016.
 G.V. Post, Database Management Systems: Designing and Building Business Applications, McGraw-Hill, Boston, 1999.
 P. Andreeva, “Data Modelling and Specific Rule Generation Via Data Mining Techniques,” In Proceedings of the International Conference on Computer Systems and Technologies (CompSysTech), pp. IIIA.17-1.-IIIA.17-6, 2006.
 D. J. Henderson, R. J. Carroll, and Q. Li, “Nonparametric Estimation and Testing of Fixed Effects Panel Data Models,” Journal of Econometrics, 144(1), pp. 257-275, 2008.
 S. Madden, “From Databases to Big Data,” IEEE Internet Computing, 16(3), pp. 4-6, 2012.
 D. Dietrich, Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015.
 H.-P. Kriegel, K.M. Borgwardt, P. Kröger, A. Pryakhin, M. Schubert, and A. Zimek, “Future trends in data mining,” Data Mining and Knowledge Discovery, 15(1), pp. 87-97, 2007.
 D. Laney, 3D Data Management: Controlling Data Volume, Velocity, and Variety, Technical Report, http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-andVariety.pdf. 2015
 P. Russom, “Big Data Analytics,” TDWI Best Practices Report, fourth quarter, pp. 1-35, 2011.
 M. M. Fouad, N. E. Oweis, T. Gaber, M. Ahmed, & V. Snasel, “Data Mining and Fusion Techniques for WSNs as a Source of the Big Data,” Procedia Computer Science, 65, pp. 778-786, 2015.
 H. Rahman, S. Begum, and M.U. Ahmed, “Ins and Outs of Big Data: A Review,” In Proceedings of the International Conference on IoT Technologies for HealthCare, pp. 44-51, 2016.
 A.J. Jara, D. Genoud, and Y. Bocchi, “Big Data for Cyber Physical Systems: An Analysis of Challenges, Solutions and Opportunities,” In Proceedings of the 8th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 376-380, 2014.
 M.D. Assunção, R.N. Calheiros, S. Bianchi, M.A. Netto, and R. Buyya, “Big Data Computing and Clouds: Trends and Future Directions,” Journal of Parallel and Distributed Computing, 79, pp. 3-15, 2015.
 P.S. Yu, “On mining big data,” Web-Age Information Management, 7923, XIV, 2013.
 J. Cassina, Extended Product Lifecycle Management, PhD thesis at Politecnico di Milano, 2008.
 M. Madhikermi, A. Buda, B. Dave, and K. Främling, “Key Data Quality Pitfalls for Condition based Maintenance,” In Proceedings of the 2nd International Conference on System Reliability and Safety (ICSRS), pp. 474-480, 2017.
 D. Vladimirova, S. Evans, V. Martinez, and J. Kingston, “Elements of Change in the Transformation towards Product Service Systems,” Functional Thinking for Value Creation, pp. 21-26, 2011.
 A. Ericson, P. Müller, T. Larsson, and R. Stark, “Product-service Systems from Customer Needs to Requirements in Early Development Phases,” In Proceedings of the 1st CIRP Industrial Product Service Systems Conference, pp. 62-67, 2009.
 J. H. Shin, H.B. Jun, C. Catteneo, D. Kiritsis, and P. Xirouchakis, “Degradation Mode and Criticality Analysis based on Product Usage Data,” The International Journal of Advanced Manufacturing Technology, 78(9-12), pp. 1727-1742, 2015.
 J.S Ward, and A. Barker, “Undefined by Sata: A Survey of Big Data Definitions”, available at https://arxiv.org/abs/1309.5821, 2013
 D. Bollier, and C.M. Firestone, The Promise and Peril of Big Data, Aspen Institute, Communications and Society Program, Washington, DC., 2010.
 J. Gantz and D. Reinsel, “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East,” IDC iView: IDC Anal. Future, vol. 2007, pp. 1-16, 2012.
 M. Shah, “Big Data and the Internet of Things,” Big Data Analysis: New Algorithms for a New Society, Springer, pp. 207-237, 2016.
 E.A. Mohammed, B.H Far, and C. Naugler, “Applications of the MapReduce Programming Framework to Clinical Big Data Analysis: Current Landscape and Future Trends,” BioData mining, 7(22), pp. 1-23, 2014.
 S.H. Nguyen, A. Skowron, and P. Synak, “Discovery of Data Patterns with Applications to Decomposition and Classification Problems,” Rough Sets in Knowledge Discovery 2, Springer, pp. 55-97, 1998.
 L. Silverston, and P. Agnew, Universal patterns for data modeling, The Data Model Resource Book, John Wiley & Sons, 2009.
 M. West, Developing high quality data models, Elsevier, 2011.
 M. Goebel, and L. Gruenwald, “A Survey of Data Mining and Knowledge Discovery Software Tools,” ACM SigKDD Explorations Newsletter, 1(1), pp. 20-33, 1999.
 S.-J. Lee, and E.-N Huh, “Shear-based Spatial Transformation to Protect Proximity Attack in Outsourced Database,” In Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 1633-1638, 2013.
 E. Rahm, and H.H. Do, “Data Cleaning: Problems and Current Approaches,” IEEE Data Engineering Bulletin, 23(4), pp. 3-13, 2000.
 L. Berti-Equille, T. Dasu, and D. Srivastava, “Discovery of Complex Glitch Patterns: A Novel Approach to Quantitative Data Cleaning,” In Proceedings of the 27th International Conference on the Data Engineering, pp. 733-744, 2011.
 K. Czarnecki, and S. Helsen, “Classification of Model Transformation Approaches,” In Proceedings of the 2nd OOPSLA Workshop on Generative Techniques in the Context of the Model Driven Architecture, Anaheim, 2003
 S. Sharma, and S. Srinivasan, “Issues Related with Software Conversion Projects,” International Journal of Electrical Electronics & Computer Science Engineering, 1(4), pp. 10-13, 2014.
 E.A. King, “How to Buy Data Mining: A Framework for Avoiding Costly Project Pitfalls in Predictive Analytics,” Information Management, 15(10), pp. 38-44, 2005.
 I.H. Witten, E. Frank, M.A. Hall, and C.J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2016.
 A. Berson, and S.J. Smith, S.J., Data Warehousing, Data Mining, and OLAP. McGraw-Hill, Inc., New York, 1997.
 P. Zikopoulos, and C. Eaton, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Osborne Media, 2011.
 N. Marz, and J. Warren, Big Data: Principles and Best Practices of Scalable Realtime Data Systems, Manning Publications Co., 2015.
 J. Cohen, P. Cohen, S.G. West, and L.S. Aiken, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Psychology Press, Routledge, 2013.
 W. Lin, M.A. Orgun, and G.J. Williams, “An Overview of Temporal Data Mining,” In Proceedings of the 1st Australian Data Mining Workshop, pp. 83-90, 2002.
 T.H. Davenport, J.G. Harris, and R. Morison, Analytics at Work: Smarter Decisions, Better Results, Harvard Business Press, Buston, MA, 2010.
 A. Trnka, “Big Data Analysis,” European Journal of Science and Theology, 10(1), pp. 143-148, 2014.
 C. Barras, S. Meignier, and J.-L Gauvain, “Unsupervised Online Adaptation for Speaker Verification over the Telephone,” In Proceedings of the IEEE Odyssey, ISCA Speaker Recognition Workshop, pp. 157-160, 2004.
 A. Dowgiert, The Impact of Big Data on Traditional Health Information Management and EHR. Doctoral Dissertation, The College of St. Scholastica, 2014.
 E. Fotopoulou, A. Zafeiropoulos, D. Papaspyros, P. Hasapis, G. Tsiolis, T. Bouras, N. Zanetti, “Linked Data Analytics in Interdisciplinary Studies: The Health Impact of Air Pollution in Urban Areas,” IEEE Access, 4, pp. 149-164, 2016.
 S. Zhang, C. Zhang, and Q. Yang, “Data Preparation for Data Mining,” Applied Artificial Intelligence, 17(5-6), pp. 375-381, 2003.
 A.D. Chapman, “Principles and Methods of Data Cleaning,” Report for the Global Biodiversity Information Facility, Copenhagen, pp. 1-72, 2005.
 J. Han, J. Pei, and M. Kamber, Data Mining: Concepts and Techniques, Elsevier, 2011.
 K. Paunović, “Data Collecting,” Data Collecting Encyclopedia of Public Health, Springer, pp. 196-199, 2008.
 R.T. Ng, and J. Han, “CLARANS: A Method for Clustering Objects for Spatial Data Mining,” IEEE Transactions on Knowledge and Data Engineering, 14(5), pp. 1003-1016, 2002.
 A.K. Jain, M.N. Murty, and P.J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, 31(3), pp. 264-323, 1999.
 J. Han, and N. Cercone, “RuleViz: a Model for Visualizing Knowledge Discovery Process,” In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 244-253, 2000.
 A. Wnuk, D. Gozdowski, A. Górny, Z. Wyszyński, and M. Kozak, “Data Visualization in Yield Component Analysis: An Expert Study,” Scientia Agricola, 74(2), pp. 118-126, 2017.
 T. Imamura, S. Yamada, and M. Machida, “Development of a High Performance Eigensolver on the Petascale Next Generation Supercomputer System,” In Proceedings of the Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2010 (SNA+MC2010), pp. 643-650, 2011.
 L. Geng, and H.J. Hamilton, “Interestingness Measures for Data Mining: A Survey,” ACM Computing Surveys (CSUR), https://www.researchgate.net/profile/Howard_Hamilton/publication/220566216_Interestingness_Measures_for_Data_Mining_A_Survey/links/00463517ef3e1d2ce8000000/Interestingness-Measures-for-Data-Mining-A-Survey.pdf. 2006.
 J. Chattratichat, J. Darlington, M. Ghanem, Y. Guo, H. Hüning, M. Köhler, D. Yang, “Large Scale Data Mining: Challenges and Responses,” In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 61-64, 1997.
 S. Ren, and X. Zhao, “A Predictive Maintenance Method for Products based on Big Data Analysis,” In Proceedings of the International Conference on Materials Engineering and Information Technology Applications (MEITA 2015), pp. 385-390, 2015.
 X. Wu, X. Zhu, G.-Q Wu, and W. Ding, “Data Mining with Big Data,” IEEE Transactions on Knowledge and Data Engineering, 26(1), pp. 97-107, 2014.
 W. Klösgen, “Knowledge Discovery in Databases and Data Mining,” Foundations of Intelligent Systems, pp. 623-632, 1996.
 H. Frigui, “Membershipmap: Data Transformation based on Granulation and Fuzzy Membership Aggregation,” IEEE Transactions on Fuzzy Systems, 14(6), pp. 885-896, 2006.
 Z. Blivband, P. Grabov, and O. Nakar, “Expanded Fmea (efmea),” In Proceedings of the Annual Symposium Reliability and Maintainability, pp. 31-36, 2004.
 J. Shafer, R. Agrawal, and M. Mehta, “SPRINT: A Scalable Parallel Classifier for Data Mining,” In Proceedings of the 22nd International Conference on Very Large Databases, pp. 544-555, 1996.
 D. Luo, C. Ding, and H. Huang, “Parallelization with Multiplicative Algorithms for Big Data Mining,” In Proceedings of the IEEE 12th International Conference on Data Mining, pp. 489-498, 2012.
 R. Chen, K. Sivakumar, and H. Kargupta, “Collective Mining of Bayesian Networks from Distributed Heterogeneous Data,” Knowledge and Information Systems,6(2), pp. 164-187, 2004.
 P. Williams, C.R. Margules, and D.W. Hilbert, “Data Requirements and Data Sources for Biodiversity Priority Area Selection,” Journal of Biosciences, 27(4), pp. 327-338, 2002.
 O. Maimon, and L. Rokach, “Decomposition Methodology for Knowledge Discovery and Data Mining,” Data Mining and Knowledge Discovery Handbook, 2, pp. 981-1003, 2005.
 J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A.H. Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, 2011.
 L. Duan, and Y. Xiong, “Big Data Analytics and Business Analytics,” Journal of Management Analytics, 2(1), pp. 1–21, 2015.
 S. Lohr, “The Age of Big Data”, New York Times, http://www.nytimes.com/2012/02/12/sunday-review/bigdatas-impact-in-the-world.html. 2012.
 E. Begoli, and J. Horey, “Design Principles for Effective Knowledge Discovery from Big Data,” In Proceedings of the 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture, pp. 215-218, 2012.
 M. Chen, S. Mao, Y. Zhang, and V.C Leung, “Big data analysis,” Big Data, pp. 51-58, 2014.
 L.A. Kurgan, and P. Musilek, “A Survey of Knowledge Discovery and Data Mining Process Models,” The Knowledge Engineering Review, 21(1), pp. 1-24, 2006.
 B. Padmanabhan, and A. Tuzhilin, “A Brief-driven Method for Discovering Unexpected Patterns,” In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 94-11, 1998.
 S. Munir, J.A. Stankovic, C.-J. M. Liang, and S. Lin, “Reducing Energy Waste for Computers by Human-in-the-loop Control,” IEEE Transactions on Emerging Topics in Computing, 2(4), pp. 448-460, 2014.
 M.J. Kane, J. Emerson, and S. Weston, “Scalable Strategies for Computing with Massive Data,” Journal of Statistical Software, 55(14), pp. 1-19, 2013.
 U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “The KDD Process for Extracting Useful Knowledge from Volumes of Data,” Communications of the ACM, 39(11), pp. 27-34, 1996.
 N. Guarino, Formal Ontology in Information Systems: Proceedings of the 1st International Conference (FOIS'98), IOS Press, Trento, 1998.
 D.T. Tempelaar, B. Rienties, and B. Giesbers, “In Search for the Most Informative Data for Feedback Generation: Learning Analytics in a Data-rich Context,” Computers in Human Behavior, 47, pp. 157-167, 2015.
 Semantics, available at:
 S. Ramsay, “Special Section: Reconceiving Text Analysis: Toward an Algorithmic Criticism,” Literary and Linguistic Computing, 18(2), pp. 167-174, 2003.
 H.-J. Lenz, “A Rigorous Treatment of Microdata, Macrodata, and Metadata,” In Proceedings of the Computational Statistics Conference, pp. 357-362, 1994.
 S. Berry, J. Levinsohn, and A. Pakes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy, 112(1), 68-105, 2004.
 I. Icke, and A. Rosenberg, “Multi-objective Genetic Programming Projection Pursuit for Exploratory Data Modeling,” In Proceedings of the 5th Annual Machine Learning Symposium, available at: https://arxiv.org/pdf/1010.1888.pdf, 2010.
 M. Shehada, and F. Alkhaldi, “Measuring the Efficiency and Effectiveness of the Human Resources Training Function at Orange Jordan,” International Journal of Quantitative and Qualitative Research Method, 3(2), pp. 1-12, 2015.
 E. Parzen, “Nonparametric Statistical Data Modeling,” Journal of the American statistical association, 74(365), pp. 105-121, 1979.
 W.K. Michener, and M.B. Jones, “Ecoinformatics: Supporting Ecology as a Data-intensive Science,” Trends in Ecology & Evolution, 27(2), pp. 85-93, 2012.
 D. Gorissen, K. Crombecq, W. Hendrickx, and T. Dhaene, “Grid Enabled Metamodeling,” In Proceedings of the 7th International Meeting on High Performance Computing for Computational Science, available at http://sumo.intec.ugent.be/sites/sumo/files/sumo/2006_07__VECPAR.pdf, 2006.
 D. Gorissen, K. Crombecq, I. Couckuyt, and T. Dhaene, “Automatic Approximation of Expensive Functions with Active Learning,” Foundations of Computational Intelligence, Learning and Approximation, 1, pp. 35-62, 2009.
 Y. Nemchinova, and H. Sayani, “Using Tools in Teaching University Courses in Information Technology,” In Proceedings of the 7th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06), pp. 361-367, 2006.
 S.E. Lenzi, C.E. Standoli, G. Andreoni, P. Perego, and N.F. Lopomo, “A Software Toolbox to Improve Time-Efficiency and Reliability of an Observational Risk Assessment Method,” In Proceedings of the Congress of the International Ergonomics Association, pp. 689-708, 2018.
 H. Murakoshi, M. Kishi, and K. Ochimizu, “Developing Web-based On-demand Learning System,” In Proceedings of the International Conference on Computers in Education, pp. 1223-1227, 2002.
 J.A. Sain, “ESD/MITRE Software Acquisition Symposium Proceedings; an ESD/Industry Dialogue held in Bedford,” available at http://www.dtic.mil/docs/citations/ADA178785,1986
 J. Fan, F. Han, and H. Liu, “Challenges of Big Data Analysis,” National Science Review, 1(2), pp. 293-314, 2014.
 R. Roy, E. Shehab, A. Tiwari, T. Baines, H. Lightfoot, O. Benedettini, and J. Kay, “The Servitization of Manufacturing: A Review of Literature and Reflection on Future Challenges. Journal of Manufacturing Technology Management, 20(5), pp. 547-567, 2009.
 A. Oussous, F.Z. Benjelloun, A.A. Lahcen, and S. Belfkih, “Big Data Technologies: A Survey,” Journal of King Saud University-Computer and Information Sciences, 30(4), pp. 431-448, 2018.
 A. Taneja, “Enhancing Web Data Mining: The Study of Factor Analysis,” Web Usage Mining Techniques and Applications Across Industries, IGI Global, pp. 116-136, 2017.
 Y. Wang, L. Kung, and T.A. Byrd, “Big Data Analytics: Understanding its Capabilities and Potential Benefits for Healthcare Organizations,” Technological Forecasting and Social Change, 126, pp. 3-13, 2018.
 B.T. Mayne, S.Y. Leemaqz, S. Buckberry, and C.M.R. Lopez, C.T. Roberts, T. Bianco-Miotto, and J. Breen, “msgbsR: An R Package for Analysing Methylation-sensitive Restriction Enzyme Sequencing Data,” Scientific reports, 8(1), pp. 2190-2198, 2018.
 A.S. Tremsin, S. Ganguly, S.M. Meco, G.R. Pardal, T. Shinohara, and W.B. Feller, “Investigation of Dissimilar Metal Welds by Energy-resolved Neutron Imaging,” Journal of applied crystallography, 49(4), pp. 1130-1140, 2016.
 D.R. Roe, and T.E. Cheatham III, Parallelization of CPPTRAJ Enables Large Scale Analysis of Molecular Dynamics Trajectory Data,” Journal of Computational Chemistry, 39(25), pp. 2110-2117, 2018.
 A. Boettcher, W. Brendel, and M. Bethge, “Large Scale Blind Source Separation,” In Proceedings of Bernstein Conference, pp. 118-119, 2016.
 T. Thilagaraj, and N. Sengottaiyan, “A Review of Educational Data Mining in Higher Education System,” In Proceedings of the 2nd International Conference on Research in Intelligent and Computing in Engineering, pp. 349–358, 2017.
 D.H. Spatti, and L.H.B. Liboni, “Computational Tools for Data Processing in Smart Cities,” Smart Cities Technologies, InTech, pp. 41-54, 2016.
 A. Rizwan, A. Zoha, R. Zhang, W. Ahmad, K. Arshad, N.A. Ali, ..., and Q.H. Abbasi, “A Review on the Role of Nano-communication in Future Healthcare Systems: A Big Data Analytics Perspective,” IEEE Access, 6, pp. 41903-41920, 2018.
 Y. Tian, “Accelerating Data Preparation for Big Data Analytics,” Doctoral dissertation, Télécom ParisTech, 2017.
 H. Demuth, M. Beale, and M. Hagan, “Neural Network Toolbox 6,” User’s guide, pp. 37-55, 2008.
 E.C. Foster, and S. Godbole, “Overview of Oracle,” Database Systems, Apress, Berkeley, CA, pp. 435-442, 2016.
 J. Vijayaraj, R. Saravanan, P.V. Paul, and R. Raju, “Hadoop Security Models-a Study,” In Proceedings of the Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1-5, 2016.
 S.K. Sahu, M.M. Jacintha, and A.P. Singh, “Comparative Study of Tools for Big Data Analytics: An Analytical Study,” In Proceedings of the International Conference on Computing, Communication and Automation (ICCCA), pp. 37-41, 2017.
 G. Piatetsky, “R, Python Duel as Top Analytics, Data Science Software”, Kdnuggets 2016 Software Poll results, http://www. kdnuggets. com/2016/06/r-python-topanalytics-data-mining-data-science-software. html. 2016.
 F. Ye, Y. Chen, Q. Huang, and L. Li, “Developing Cloud-Based Tools for Water Resources Data Analysis Using R and Shiny,” In Proceedings of the International Conference on Emerging Internetworking, Data & Web Technologies, pp. 289-297, 2017.
 A. Goyal, I. Khandelwal, R. Anand, A. Srivastava, and P. Swarnalatha, “A Comparative Analysis of the Different Data Mining Tools by Using Supervised Learning Algorithms,” In Proceeding of the International Conference on Soft Computing and Pattern Recognition, pp. 105-112, 2016.
 C. Giraud-Carrier, and O. Povel, “Characterising Data Mining Software,” Intelligent Data Analysis, 7(3), pp. 181-192, 2003.
 S. Peng, G. Wang, and D. Xie, “Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges,” IEEE Network, 31(1), pp. 11-17, 2017.
 I. Taleb, M.A. Serhani, and R. Dssouli, “Big Data Quality: A Survey,” In Proceedings of the 2018 IEEE International Congress on Big Data (BigData Congress), pp. 166-173, 2018.
 A. Gepp, M.K. Linnenluecke, T.J. O’Neill, and T. Smith, “Big Data Techniques in Auditing Research and Practice: Current Trends and Future Opportunities,” Journal of Accounting Literature, 40, pp. 102-115, 2018.
 B. Prakash, and D.M. Hanumanthappa, “Issues and Challenges in the Era of Big Data Mining,” International Journal of Emerging Trends and Technology in Computer Science (IJETICS), 3, pp. 321-325, 2014.
 B. Vijayalakshmi, and M. Srinath, “State-of-the-art Frameworks and Platforms for Processing and Managing Big Data as well as the Efforts Expected on Big Data Mining,” Elysium Journal of Engineering Research & Management, 1(2), pp. 1-9, 2014.
 S. Joshi, and M.K. Nair, “Survey of Classification Based Prediction Techniques in Healthcare,” Indian Journal of Science and Technology, http://www.indjst.org/index.php/indjst/article/viewFile/121111/84191. 2018.
 M. Scarnò, “ ADaMSoft: Un Software Open Source e un’Esperienza nel Calcolo Scientifico,” CASPUR Annual Report, pp. 46-47, 2008.
 H.W. Deng, Y.N. Zhang, B. Ke, and M.T. Li, “Decision Making of Ore Mining Model based on Analytica Software,” The Chinese Journal of Nonferrous Metals, http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZYXZ201702014.htm. 2017.
 U. Cieplik, BV4. 1 Methodology and User-friendly Software for Decomposing Economic Time Series, German Federal Statistical Office, 2006.
 G. Karypis, “CLUTO- a Clustering Toolkit,” Technical Report 02-017, Department of Computer Science, University of Minnesota, http://www.cs.umn.edu/ ̃cluto. 2002.
 A.B. Comsol, COMSOL Multiphysics® v. 5.2. Stockholm, Sweden, 2016.
 I.D., Landau, and V. Landau, “Data Mining et Machine Learning dans les Big Data: Une Tentative de Démystification,” https://hal.archives-ouvertes.fr/hal-01393640/file/DMML_F.pdf. 2016.
 S.V. Chekanov, Numeric Computation and Statistical Data Analysis on the Java Platform, Springer, Basel, 2016.
 E. Coman, M.W. Brewster, S.K. Popuri, A.M. Raim, and M.K. Gobbert, “A Comparative Evaluation of Matlab, Octave, FreeMat, Scilab, R, and IDL on Tara. http://www.webcitation.org/6BbWqerg3. 2012.
 J.W. Eaton, GNU Octave 4.2 Reference Manual, Samurai Media Limited, 2017.
 JASP Team, JASP (Version 0.7. 5.5) [Computer software]. Google Scholar, pp. 765-766, 2016.
 S. Dwivedi, P. Kasliwal, and S. Soni, “Comprehensive Study of Data Analytics Tools (RapidMiner, Weka, R tool, Knime),” In Proceedings of the Symposium on Colossal Data Analysis and Networking (CDAN), pp. 1-8, 2016.
 D.J. Higham, and N.J. Higham, MATLAB Guide, Siam, 2016.
 T. Hothorn, and M.T. Hothorn, “The Maxstat Package”, http://btr0x2.rz.uni-bayreuth.de/math/statlib/R/CRAN/doc/packages/maxstat.pdf. 2007.
 W. Winston, “Microsoft Excel Data Analysis and Business Modeling,” Microsoft Press, 2016.
 W. Miller, OpenStat Reference Manual, Springer, Science & Business Media, 2012.
 P. Tamayo, C. Berger, M. Campos, J. Yarmus, B. Milenova, A. Mozes, S. Thomas, “Oracle Data Mining,” Data Mining and Knowledge Discovery Handbook, pp. 1315-1329, 2005.
 G. Shmueli, P.C. Bruce, I. Yahav, N.R. Patel, and K.C. Lichtendahl Jr, Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, John Wiley & Sons, 2017.
 M. Hofmann, and R. Klinkenberg, “ Getting Used to RapidMiner,” RapidMiner, Chapman and Hall/CRC, pp. 65-76, 2016.
 R. Cody, Learning SAS by Example: A Programmer's Guide, SAS Institute, 2018.
 S.L. Campbell, J.P. Chancelier, and R. Nikoukhah, Modeling and Simulation in SCILAB, Springer, New York, 2006.
 S. Sonnenburg, S. Henschel, C. Widmer, J. Behr, A. Zien, F.D. Bona, V. Franc, “The SHOGUN Machine Learning Toolbox,” Journal of Machine Learning Research, 11, pp. 1799-1802. 2010.
 D. George, and P. Mallery, IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference, Routledge, 2016.
 L. StataCorp, Stata Data Analysis and Statistical Software, Special Edition Release, 2007.
 R.R. Bouckaert, E. Frank, M. Hall, R. Kirkby, P. Reutemann, A. Seewald, and D. Scuse, WEKA Manual for Version 3-9-1, University of Waikato, Hamilton, New Zealand, 2016.
 R. Brennan, “Challenges for Value-driven Semantic Data Quality Management,” In Proceedings of the 19th International Conference on Enterprise Information Systems, 1, pp. 85-392, 2017.
 A.A. Rahimah, and T.R. Naik, “Data Mining for Big Data,” International Journal of Advanced Technology and Innovation research, 7(5), pp. 697-701, 2015.
 N. Bikakis, “Big Data Visualization Tools”, arXiv preprint arXiv:1801.08336. 2018.
 M. Chen, S. Mao, Y. Zhang, and V.C. Leung, “Big Data Applications,” In Big Data, Springer, pp. 59-79, 2014.
 I. Kopanas, N.M. Avouris, and S. Daskalaki, “The Role of Domain Knowledge in a Large Scale Data Mining Project,” In Proceedings of the 2nd Hellenic Conference on Artificial Intelligence: Methods and Applications of Artificial Intelligence, pp. 288-299, 2002.
 M. Brodie, M. Greaves, and J. Hendler, “Databases and AI: The Twain Just Met,” In Proceedings of the 2011 STI Semantic Summit, pp: 6-8, 2011.
 C. Bizer, P. Boncz, M.L. Brodie, and O. Erling, “The Meaningful Use of Big Data: Four Perspectives - Four Challenges,” ACM Sigmod Record, 40(4), pp. 56-60, 2012.
 K. Michael, and K.W. Miller, “Big Data: New Opportunities and New Challenges [Guest Editors' Introduction],” Computer, 46(6), pp. 22-24., 2013.
 H.-P. Schöner, “Automotive Mechatronics,” Control Engineering Practice, 12(11), pp. 1343-1351, 2004.
 C. Zheng, M. Bricogne, J. Le Duigou, and B. Eynard, “Survey on Mechatronic Engineering: A Focus on Design Methods and Product Models,” Advanced Engineering Informatics, 28(3), pp. 241-257, 2014.
 X. Yang, P. Moore, and S.K. Chong, “Intelligent Products: From Lifecycle Data Acquisition to Enabling Product-related Services,” Computers in Industry, 60(3), pp. 184-194, 2009.
 P. Breuer, J. Moulton, and R.M. Turtle, Applying Advanced Analytics in Consumer Companies, McKinsey & Company, New York, 2013.
 J. Li, F. Tao, Y. Cheng, and L. Zhao, “Big Data in Product Lifecycle Management,” The International Journal of Advanced Manufacturing Technology, 81(1-4), pp. 667-684, 2015.
 M.J. Shaw, C. Subramaniam, G.W. Tan, and M.E. Welge, “Knowledge Management and Data Mining for Marketing,” Decision Support Systems, 31(1), pp. 127-137, 2001.
 G. Duncan, “Privacy by Design,” Science, 317(5842), pp. 1178-1179, 2007.
 E.E. Schadt, “The Changing Privacy Landscape in the Era of Big Data,” Molecular Systems Biology, 8(1), pp. 612-614, 2012.
 C. Liu, and K.P. Arnett, “Exploring the Factors Associated with Web Site Success in the Context of Electronic Commerce,” Information & Management, 38(1), pp. 23-33, 2000.
 T. Preis, H.S. Moat, and H.E. Stanley, “Quantifying Trading Behavior in Financial Markets using Google Trends,” Scientific Reports, 3, pp. 1684-1689, 2013.
 E. Fuhrer, E. System for Enhanced Financial Trading Support, Google Patents, Washington, DC, 2000.
 R. Bryant, R.H. Katz, and E.D. Lazowska, “Big Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society,” The Computing Community Consortium Project, pp. 1-15. 2008
 R. Steinbrook, “Personally Controlled Online Health Data – The Next Big Thing in Medical Care?,” The New England Journal of Medicine, 358(16), pp. 1653-1656, 2008.
 P. Groves, B. Kayyali, D. Knott, and S. Van Kuiken, “The Big Data Revolution in Healthcare,” McKinsey Q, http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care, 2013.
 W.H. Organization, Who Operational Package for Assessing, Monitoring and Evaluating Country Pharmaceutical Situations, Guide for Coordinators and Data Collectors, 2007.
 G.M. Weiss, “Data Mining in Telecommunications,” Data Mining and Knowledge Discovery Handbook, pp. 1189-1201, 2005.
 I. Kļevecka, and J. Lelis, “Pre-processing of Input Data of Neural Networks: The Case of Forecasting Telecommunication Network Traffic,” Telektronikk, 3, pp. 168-178, 2008.
 A. Szalay, “Extreme Data-intensive Scientific Computing,” Computing in Science & Engineering, 13(6), pp. 34-41, 2001.
 G. Brumfiel, “Down the Petabyte Highway,” Nature News, 469(20), pp. 282-283, 2011.
 F.-Y Wang, K.M. Carley, D. Zeng, and W. Mao, “Social Computing: From Social Informatics to Social Intelligence,” IEEE Intelligent Systems, 22(2), pp. 79-83, 2007.
 A. Lesk, Introduction to Bioinformatics, Oxford University Press, 3rd edition, 2013.
 J. McDermott, R. Samudrala, R. Bumgarner, K. Montgomery, and R. Ireton, Computational Systems Biology, Springer, 2009.
 C.P. Chen, and C.Y. Zhang, “Data-intensive Applications, Challenges, Techniques and Technologies: A Survey on Big Data,” Information Sciences, 275, pp. 314-347, 2014.
 W. Fan, and A. Bifet, “Mining Big Data: Current Status, and Forecast to The Future,” ACM SigKDD Explorations Newsletter, 14(2), pp. 1-5, 2013.
 J. Leskovec, A. Rajaraman, and J.D. Ullman, Mining of Massive Datasets, Cambridge University Press, Cambridge, 2014.
 M. Zheng, X. Ming, G. Li, and Shi Y., “The Framework of Business Model Innovation for Smart Product-service Ecosystem,” In Proceedings of the NordDesign Conference, 2, pp. 400-409, 2016.
 L. Atzori, A. Iera, and G Morabito, “The Internet of Things: A Survey,” Computer Networks, 54(15), pp. 2787-2805, 2010.
 T.S. Dillon, H. Zhuge, C. Wu, J. Singh, and E. Chang, “Web‐of‐things Framework for Cyber-Physical Systems,” Concurrency and Computation: Practice and Experience, 23(9), pp. 905-923, 2011.
 J. Farringdon, A.J. Moore, N. Tilbury, J. Church, and P.D. Biemond, “Wearable Sensor Badge and Sensor Jacket for Context Awareness,” In Proceedings of the 3rd International Symposium on Wearable Computers, San Francisco, pp. 107-113, 1999.
 P. Valckenaers, B. Saint Germain, P. Verstraete, J. Van Belle, K. Hadeli, H. Van Brussel, “Intelligent Products: Agere versus Essere,” Computers in Industry, 60(3), pp. 217–228, 2009.
 I. Horváth, Z. Rusák, and Y. Li, “Order beyond Chaos: Introducing the Notion of Generation to Characterize the Continuously Evolving Implementations of Cyber-Physical Systems,” In Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 1-14, 2017.
 G.G. Meyer, K. Främling, and J. Holmström, “Intelligent Products: A survey,” Computers in Industry, 60(3), pp. 137-148, 2009.
 K. Främling, J. HolmströM, J. Loukkola, J. Nyman, and A. Kaustell, “Sustainable PLM through Intelligent Products,” Engineering Applications of Artificial Intelligence, 26(2), pp. 789-799, 2013.
 S. Earley, “Analytics, Machine Learning, and the Internet of Things,” IT Professional, 17(1), pp. 10-13, 2015.
 S. Szykman, S.J. Fenves, W. Keirouz, and S.B. Shooter, “A Foundation for Interoperability in Next-generation Product Development Systems,” Computer-Aided Design, 33(7), pp. 545-559, 2001.
 L. Aroyo, and D. Dicheva, “The New Challenges for E-Learning: The Educational Semantic Web,” Educational Technology & Society, 7(4), pp. 59-69, 2004.
 J. Bézivin, H. Bruneliere, F. Jouault, and I. Kurtev, “Model Engineering Support for Tool Interoperability,” In Proceedings of the Workshop in Software Model Engineering - A MODELS 2005 Satellite Event, pp. 1-16, 2005.
 S. Pandey, and S. Nepal, “Cloud Computing and Scientific Applications – Big Data, Scalable Analytics, and Beyond,” Future Generation Computer Systems, 29, pp. 1774–1776, 2013.
 H. Haugen, and B. Ask, “Real Integration of ICT into Subject Content and Methodology Requires more than Technology, Infrastructure and Standard Software,” In Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 107-117, 2010.
 E.G. Nabati, K.D. Thoben, and M. Daudi, “Stakeholders in the Middle of Life of Complex Products: Understanding the Role and Information Needs,” International Journal of Product Lifecycle Management, 10(3), pp. 231-257, 2017.
 C.W. Chow, J. Liu, J. Li, N. Swain, K. Reid, and C.P. Saint, “Development of Smart Data Analytics Tools to Support Wastewater Treatment Plant Operation,” Chemometrics and Intelligent Laboratory Systems, 177, pp. 140-150, 2018.