Autonomous Sensor Data Cleaning in Stream Mining Setting

1. Al Quhtani, M. (2017), “Data Mining Usage in Corporate Information Security: Intrusion Detection Applications”, Business Systems Research, Vol. 8, No. 1, pp. 51-59.10.1515/bsrj-2017-0005Search in Google Scholar

2. Belfo, F., Trigo, A., Estébanez, R. P. (2015), “Impact of ICT Innovative Momentum on Real-Time Accounting”, Business Systems Research, Vol. 6, No. 2, pp. 1-17.10.1515/bsrj-2015-0007Search in Google Scholar

3. Breiman, L. (2001), “Random Forests”, Machine Learning, Vol. 45, No. 1, pp. 5-32.10.1023/A:1010933404324Search in Google Scholar

4. Chen, C., Liu, L. (1993), “Joint Estimation of Model Parameters and Outlier Effects in Time Series”, Journal of the American Statistical Association, Vol. 88, No. 421, pp. 284-297.Search in Google Scholar

5. Chu, X., Ilyas, I. F., Krishnana, S., Wang, J. (2016), “Data cleaning: Overview and emerging challenges”, in Özcan, F., Koutrika, G. (Eds.), Proceedings of the 2016 International Conference on Management of Data, ACM, San Francisco, pp. 2201-2206.10.1145/2882903.2912574Search in Google Scholar

6. Dempster, A. P., Laird, N. M., Rubin, N. M. (1977), “Maximum likelihood from incomplete data via the EM algorithm”, Journal of Royal Statistical Society Series B, Vol. 39, No. 1, pp. 1-38.Search in Google Scholar

7. Fan, W., Bifet, A. (2013), “Mining big data: Current status, and forecast to the future”, ACM siGKDD Explorations Newsletter, Vol. 14, No. 2, pp. 1-5.10.1145/2481244.2481246Search in Google Scholar

8. Kalman, R. E. (1960), “A new Approach to linear filtering and prediction problem”, Journal of basic engineering, Vol. 82, No. 1, pp. 34-45.10.1115/1.3662552Search in Google Scholar

9. Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N.H., Weaver, C., Lee, B., Brodbeck, D., Buono, P. (2011), “Research directions in data wrangling: Visualizations and transformations for usable and credible data”, Information Visualization Journal, Vol. 10, No. 4, pp. 271-288.10.1177/1473871611415994Search in Google Scholar

10. Kenda, K. (2017), Artificial data-set for testing time-series additive outlier detection methods, available at: https://www.researchgate.net/publication/317721142_Artificial_dataset_for_testing_time-series_additive_outlier_detecion_methods (18 February 2018).Search in Google Scholar

11. Kenda, K., Mladenić, D. (2017), “Autonomous on-line outlier detection framework for streaming sensor data”, in Zadnik Strin, L., Kljajić Borštnar, M., Žerovnik, J., Drobne, S. (Eds.), Proceedings of the 14th International Symposium on Operational Research, Bled, pp. 103-108.Search in Google Scholar

12. Kenda, K., Škrbec, J., Škrjanc, M. (2013). “Usage of Kalman Filter for Data Cleaning of Sensor Data”, in Gams, M. (Ed.), Proceedings of the 16th International Multiconference Information Society - IS 2013, Ljubljana, pp. 172-175.Search in Google Scholar

13. Krempl, G., Žliobaite, I., Brzezinski, D., Hüllenmeier, E., Last., M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopolou, M. (2014), “Open challenges for data stream mining research”, ACM siGKDD Explorations Newsletter, Vol. 16, No. 1, pp. 1-10.10.1145/2674026.2674028Search in Google Scholar

14. Krishnan, S., Wang. J., Wu, E., Franklin, M. J., Goldberg, K. (2016), “ActiveClean: interactive data cleaning for statistical modeling”, in Chaudhuri, S., Haritsa, J. (Eds.), Proceedings of the VLDB Endowment, Vol. 9, No. 12, pp. 948-959.10.14778/2994509.2994514Search in Google Scholar

15. Marczak, M., Proietti, T., Grassi, S. (2018), “A data-cleaning augmented Kalman filter for robust estimation of state space models”, Econometrics and Statistics, Vol. 5, pp. 107-123.10.1016/j.ecosta.2017.02.002Search in Google Scholar

16. Press, G. (2016), “Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says”, available at: https://www.forbes.com/sites/suntrustprivatewealth/2017/12/21/wealth-transfer-are-yousure-your-beneficiaries-are-prepared/ (31 January 2018).Search in Google Scholar

17. Shearer, C. (2000), “The CRISP-DM model: the new blueprint for data mining”, Journal of data warehousing, Vol. 5, No. 4, pp. 13-22.Search in Google Scholar

18. Xu, S. (2015), “Data Cleaning and Knowledge Discovery in Process Data”, PhD thesis, University of Texas, Austin.Search in Google Scholar

19. Yahoo! Webscope (2015), “S5 - A Labeled Anomaly Detection Dataset, version 1.0”, available at: http://research.yahoo.com/Academic_Relations (28 February 2018).Search in Google Scholar

20. Zekić-Sušac, M., Has, A. (2015), “Data Mining as Support to Knowledge Management in Marketing”, Business Systems Research Journal, Vol. 6, No. 2, pp. 18-30.10.1515/bsrj-2015-0008Search in Google Scholar

• Dashboard for the Management and Acceptance of Customer Orders
• Using EPP Boxes in a Dark Store: A New Approach to Simplify Food Retail E-Commerce Deliveries
• Circular Economy and Consumer’s Engagement: An Exploratory Study on Higher Education
• Internal Logistics Process Improvement using PDCA: A Case Study in the Automotive Sector
• Migration Flows through the Lens of Human Resource Ageing
• Boosting Regional Socioeconomic Development through Logistics Activities: A Conceptual Model
• Possible Impact of Risk Management Strategies with Farm Model on a Mixed Farm Type
• Conflict and Corporate Social Responsibility in Duopoly
• Editorial for the Special Issue: “Novel Solutions and Novel Approaches in Operational Research”: co-published with the Slovenian Society INFORMATIKA – Section for Operational Research (SSI-SOR)
• The Risk and Return of Traditional and Alternative Investments Under the Impact of COVID-19
• A Machine Learning Approach to Forecast International Trade: The Case of Croatia