Open Access
Issue
ITM Web Conf.
Volume 41, 2022
International Conference on Exploring Service Science (IESS 2.2)
Article Number 05002
Number of page(s) 15
Section Digital Innovation through Smart Services
DOI https://doi.org/10.1051/itmconf/20224105002
Published online 08 February 2022
  1. T. Le Dinh, N. A. K. Dam, Smart data as a service. Proceedings of the International conference on exploring service science (IESS) 2.1, (2021) [Google Scholar]
  2. B. Dykes, Actionable insights: The missing link between data and business value. Forbes (2016) [Google Scholar]
  3. A. Medina-Borja, Editorial Column—Smart Things as Service Providers: A Call for Convergence of Disciplines to Build a Research Agenda for the Service Systems of the Future. Service Science 7(1), ii-v (2015) [CrossRef] [Google Scholar]
  4. N. A. K. Dam, T. Le Dinh, W. Menvielle, Towards a Conceptual Framework for Customer Intelligence in the Era of Big Data. International Journal of Intelligent Information Technologies (IJIIT) 17(4), 1–17 (2021) [CrossRef] [Google Scholar]
  5. J. Shim, R. Taylor, Purchase-Based Analytics and Big Data for Actionable Insights. IT Professional 21(5), 48–56 (2019) [Google Scholar]
  6. V. S. Sharma, R. Mehra, S. Podder, A. P. Burden, A journey towards providing intelligence and actionable insights to development teams in software delivery. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), (IEEE, 2019) [Google Scholar]
  7. I. Triguero, D. García‐Gil, J. Maillo, J. Luengo, S. García, F. Herrera, Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9(2), e1289 (2019) [CrossRef] [Google Scholar]
  8. A. Wieneke, C. Lehrer, Generating and exploiting customer insights from social media data. Electronic Markets 26(3), 245–268 (2016) [CrossRef] [Google Scholar]
  9. J. Hester, T. R. Miller, J. Gregory, R. Kirchain, Actionable insights with less data: guiding early building design decisions with streamlined probabilistic life cycle assessment. The International Journal of Life Cycle Assessment 23(10), 1903–1915 (2018) [CrossRef] [Google Scholar]
  10. N. Siggelkow, C. Terwiesch, The Age of Continuous Connection. Harvard Business Review (2019) [Google Scholar]
  11. A. Rawson, E. Duncan, C. Jones, The Truth About Customer Experience. Harvard Business Review (2013) [Google Scholar]
  12. A. Stein, B. Ramaseshan, Towards the identification of customer experience touch point elements. Journal of Retailing and Consumer Services 30, 8–19 (2016) [CrossRef] [Google Scholar]
  13. K. N. Lemon, P. C. Verhoef, Understanding customer experience throughout the customer journey. Journal of marketing 80(6), 69–96 (2016) [CrossRef] [Google Scholar]
  14. F. Colbert, D. C. Dantas, Customer Relationships in Arts Marketing: A Review of Key Dimensions in Delivery by Artistic and Cultural Organizations. International Journal of Arts Management 21(2) (2019) [Google Scholar]
  15. A. McAfee, E. Brynjolfsson, T. H. Davenport, D. Patil, D. Barton, Big data: the management revolution. Harvard business review 90(10), 60–68 (2012) [Google Scholar]
  16. U. Sivarajah, M. M. Kamal, Z. Irani, V. Weerakkody, Critical analysis of Big Data challenges and analytical methods. Journal of Business Research 70, 263–286 (2017) [CrossRef] [Google Scholar]
  17. P. Zerbino, D. Aloini, R. Dulmin, V. Mininno, Big Data-enabled customer relationship management: A holistic approach. Information Processing & Management 54(5), 818–846 (2018) [CrossRef] [Google Scholar]
  18. E. W. T. Ngai, L. Xiu, D. C. K. Chau, Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications 36(2), 2592–2602 (2009) [CrossRef] [Google Scholar]
  19. Y. Yan, C. Huang, Q. Wang, B. Hu, Data mining of customer choice behavior in internet of things within relationship network. International Journal of Information Management 50, 566–574 (2020) [CrossRef] [Google Scholar]
  20. S. Gupta, A. Leszkiewicz, V. Kumar, T. Bijmolt, D. Potapov, Digital analytics: Modeling for insights and new methods. Journal of Interactive Marketing 51, 26–43 (2020) [Google Scholar]
  21. K. A. Whitler, Stop Focusing On Big Data And Start Focusing On Smart Data. Forbes (2019) [Google Scholar]
  22. T. Le Dinh, T. T. T. Pham, Information-driven framework for collaborative business service modelling. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 3(1), 1–18 (2012) [Google Scholar]
  23. M. Murphy, J. Barton, From a Sea of Data to Actionable Insights. (2014) [Google Scholar]
  24. A. Levenson, A. Fink, Human capital analytics: too much data and analysis, not enough models and business insights. Journal of Organizational Effectiveness: People and Performance (2017) [Google Scholar]
  25. K. Xie, Y. Wu, J. Xiao, Q. Hu, Value co-creation between firms and customers: The role of big data-based cooperative assets. Information Management Science 53(8), 1034–1048 (2016) [CrossRef] [Google Scholar]
  26. A. R. Hevner, S. T. March, J. Park, S. Ram, Design science in information systems research. MIS Quarterly 28(1), 75–105 (2004) [CrossRef] [Google Scholar]
  27. A. Hevner, S. Chatterjee: Design science research in information systems. In: Design research in information systems. pp. 9–22. Springer, (2010) [Google Scholar]
  28. T. Le Dinh, T.-C. Phan, T. Bui, M. C. Vu, Towards a Service-Oriented Architecture for Knowledge Management in Big Data Era. International Journal of Intelligent Information Technologies 14(4), 24–38 (2018) [CrossRef] [Google Scholar]
  29. T. L. Dinh, N. A. K. Dam, Towards Smart Customer Knowledge Management Systems. International Conference on Computational Collective Intelligence, (Springer, 2021) [Google Scholar]
  30. K. Li, V. Deolalikar, N. Pradhan, Big data gathering and mining pipelines for CRM using open-source. 2015 IEEE International Conference on Big Data (Big Data), (IEEE, 2015) [Google Scholar]
  31. G. Ekren, A. Erkollar: The potential and capabilities of NoSQL databases for ERP systems. In: Advanced MIS and Digital Transformation for Increased Creativity and Innovation in Business. pp. 147–168. IGI Global, (2020) [CrossRef] [Google Scholar]
  32. M. Kiran, P. Murphy, I. Monga, J. Dugan, S. S. Baveja, Lambda architecture for cost-effective batch and speed big data processing. 2015 IEEE International Conference on Big Data (Big Data), (IEEE, 2015) [Google Scholar]
  33. J. O. Chan, Big data customer knowledge management. Communications of the IIMA 14(3), 5 (2014) [Google Scholar]
  34. J. Abawajy, Comprehensive analysis of big data variety landscape. International journal of parallel, emergent and distributed systems 30(1), 5–14 (2015) [CrossRef] [Google Scholar]
  35. M. Alnoukari, A framework for big data integration within the strategic management process based on a balanced scorecard methodology. Journal of Intelligence Studies in Business 1(1) (2021) [CrossRef] [Google Scholar]
  36. G. Pal, G. Li, K. Atkinson, Big data real time ingestion and machine learning. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), (IEEE, 2018) [Google Scholar]
  37. P. Singh, B. Chaitra, Comprehensive Review of Stream Processing Tools. (2020) [Google Scholar]
  38. C. French, Data processing and information technology. Cengage Learning EMEA, (1996) [Google Scholar]
  39. S. Pippal, S. P. Singh, D. S. Kushwaha, Data Trasfer From MySQL To Hadoop: Implementers’ Perspective. Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, (2014) [Google Scholar]
  40. N. A. K. Dam, T. Le Dinh, W. Menvielle, Marketing Intelligence From Data Mining Perspective – A Literature Review. International Journal of Innovation, Management and Technology (2019) [Google Scholar]
  41. M. Holmlund, Y. Van Vaerenbergh, R. Ciuchita, A. Ravald, P. Sarantopoulos, F. V. Ordenes, M. Zaki, Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research (2020) [Google Scholar]
  42. M. Anshari, M. N. Almunawar, S. A. Lim, A. Al-Mudimigh, Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics (94), 101 (2019) [Google Scholar]
  43. G. Fotaki, M. Spruit, S. Brinkkemper, D. Meijer, Exploring big data opportunities for online customer segmentation. International Journal of Business Intelligence Research (IJBIR) 5(3), 58–75 (2014) [CrossRef] [Google Scholar]
  44. A. Hogan, E. Blomqvist, M. Cochez, C. d’Amato, G. d. Melo, C. Gutierrez, S. Kirrane, J. E. L. Gayo, R. Navigli, S. Neumaier, Knowledge graphs. Synthesis Lectures on Data, Semantics, and Knowledge 12(2), 1–257 (2021) [CrossRef] [Google Scholar]
  45. T. Le Dinh, T. T. Pham Thi, C. Pham-Nguyen, L. N. H. Nam, A knowledge-based model for context-aware smart service systems. Journal of Information and Telecommunication, 1–22 (2021) [CrossRef] [Google Scholar]
  46. Y.-H. Chen, C.-T. Su, A Kano-CKM model for customer knowledge discovery. Total Quality Management & Business Excellence 17(5), 589–608 (2006) [CrossRef] [Google Scholar]
  47. N. A. K. Dam, T. Le Dinh, W. Menvielle, Key Aspects of Customer Intelligence in the Era of Massive Data. (Springer International Publishing, 2021) [Google Scholar]
  48. M. J. Shaw, C. Subramaniam, G. W. Tan, M. E. Welge, Knowledge management and data mining for marketing. Decision support systems 31(1), 127–137 (2001) [CrossRef] [Google Scholar]
  49. S. K. Taghizadeh, S. A. Rahman, M. M. Hossain, Knowledge from customer, for customer or about customer: which triggers innovation capability the most? Journal of Knowledge Management (2018) [Google Scholar]
  50. J. Lu, L. Cairns, L. Smith, Data science in the business environment: customer analytics case studies in SMEs. Journal of Modelling in Management (2020) [Google Scholar]
  51. N. Taherparvar, R. Esmaeilpour, M. Dostar, Customer knowledge management, innovation capability and business performance: a case study of the banking industry. Journal of knowledge management (2014) [Google Scholar]
  52. M. K. Daradkeh, Determinants of visual analytics adoption in organizations: Knowledge discovery through content analysis of online evaluation reviews. Information Technology & People (2019) [Google Scholar]
  53. A. Asllani, F. Luthans, What knowledge managers really do: an empirical and comparative analysis. Journal of knowledge management (2003) [Google Scholar]
  54. A.-S. Oertzen, G. Odekerken-Schröder, S. A. Brax, B. Mager, Co-creating services—conceptual clarification, forms and outcomes. Journal of Service Management 29(4), 641–679 (2018) [CrossRef] [Google Scholar]
  55. J. Trischler, S. J. Pervan, D. R. Scott, Exploring the “black box” of customer co-creation processes. Journal of Services Marketing 31(3), 265–280 (2017) [CrossRef] [Google Scholar]
  56. N. A. K. Dam, T. Le Dinh, W. Menvielle, Customer Co-creation through the Lens of Service-dominant Logic: A Literature Review. AMCIS 2020 Proceedings. 29., (2020) [Google Scholar]
  57. J. Forcier, P. Bissex, W. J. Chun, Python web development with Django. Addison-Wesley Professional, (2008) [Google Scholar]
  58. M. Ahmed, M. M. Uddin, M. S. Azad, S. Haseeb, MySQL performance analysis on a limited resource server: Fedora vs. Ubuntu Linux. Proceedings of the 2010 Spring Simulation Multiconference, (2010) [Google Scholar]
  59. E. Bressert, SciPy and NumPy: an overview for developers. (2012) [Google Scholar]
  60. W. McKinney, pandas: a foundational Python library for data analysis and statistics. Python for high performance and scientific computing 14(9), 1–9 (2011) [Google Scholar]
  61. J. Hunt: PyMySQL module. In: Advanced Guide to Python 3 Programming. pp. 291–301. Springer, (2019) [CrossRef] [Google Scholar]
  62. G. Hackeling, Mastering Machine Learning with scikit-learn. Packt Publishing Ltd, (2017) [Google Scholar]
  63. A. Berti, S. J. van Zelst, W. van der Aalst, Process mining for python (PM4Py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169 (2019) [Google Scholar]
  64. S. L. France, S. Ghose, Marketing analytics: Methods, practice, implementation, and links to other fields. Expert Systems with Applications 119, 456–475 (2018) [Google Scholar]
  65. S. Fan, R. Y. K. Lau, J. L. Zhao, Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix. Big Data Research 2(1), 28–32 (2015) [CrossRef] [Google Scholar]
  66. A. Amado, P. Cortez, P. Rita, S. Moro, Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics 24(1), 1–7 (2018) [CrossRef] [Google Scholar]
  67. W. M. van der Aalst, Process discovery: Capturing the invisible. IEEE Computational Intelligence Magazine 5(1), 28–41 (2010) [CrossRef] [Google Scholar]
  68. J. Rowley, The wisdom hierarchy: representations of the DIKW hierarchy. Journal of information Science 33(2), 163–180 (2007) [CrossRef] [Google Scholar]
  69. M. Alavi, D. E. Leidner, Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 107–136 (2001) [CrossRef] [Google Scholar]
  70. P. Vassiliadis, A. Simitsis, S. Skiadopoulos, Conceptual modeling for ETL processes. Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP, (2002) [Google Scholar]
  71. U. Shafique, H. Qaiser, A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research 12(1), 217–222 (2014) [Google Scholar]
  72. M. Drăgoicea, L. Walletzký, L. Carrubbo, N. G. Badr, A. M. Toli, F. Romanovská, M. Ge, Service Design for Resilience: A Multi-Contextual Modeling Perspective. IEEE Access 8, 185526–185543 (2020) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.