Transportation Service Quality Improvement through Closed Sequential Pattern Mining Approach

1. How Alibaba Turned an Obscure, Made-Up Chinese Holiday in to a 14.3 Billion Dollors Shopping Extravaganza that’s Bigger than Black Friday. http://www.businessinsider.com/how-alibaba-made-143-billion-on-singles-day-2015-11Search in Google Scholar

2. Wagner, S. M., R. Sutter. A Qualitative Investigation of Innovation between Third-Party Logistics Providers and Customers. - International Journal of Production Economics, Vol. 140, 2012, No 2, pp. 944-958.10.1016/j.ijpe.2012.07.018Search in Google Scholar

3. Ramanathan, R., U. Ramanathan, L. W. L. Ko. Adoption of RFID Technologies in UK Logistics: Moderating Roles of Size, Barcode Experience and Government Support. - Expert Systems with Applications, Vol. 41, 2014, No 1, pp. 230-236.10.1016/j.eswa.2013.07.024Search in Google Scholar

4. Prasanna, K. R., M. Hemalatha. RFID GPSand GSM Based Logistics Vehicle Load Balancing and Tracking Mechanism. - Procedia Engineering, Vol. 30, 2012, pp. 726-729.10.1016/j.proeng.2012.01.920Search in Google Scholar

5. Mejjaouli, S., R. F. Babiceanu. RFID-Wireless Sensor Networks Integration: Decision Models and Optimization of Logistics Systems Operations. - Journal of Manufacturing Systems, Vol. 35, 2015, pp. 234-245.10.1016/j.jmsy.2015.02.005Search in Google Scholar

6. Zhong, R. Y., G. Q. Huang, S. Lan, Q. Y. Dai, X. Chen, T. Zhanget al. A Big Data Approach for Logistics Trajectory Discovery from RFID-Enabled Production Data. - International Journal of Production Economics, Vol. 165, 2015, pp. 260-272.10.1016/j.ijpe.2015.02.014Search in Google Scholar

7. Ting, S. L., Y. K. Tse, G. T. S. Ho, S. H. Chung, G. Pang. Mining Logistics Data to Assure the Quality ina Sustainable Food Supply Chain: A Case in the Red Wine Industry. - International Journal of Production Economics, Vol. 152, 2014, pp. 200-209.10.1016/j.ijpe.2013.12.010Search in Google Scholar

8. Shim, B., K. Choi, Y. Suh. CRM Strategies fora Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns. - Expert Systems with Applications, Vol. 39, 2012, No 9, pp. 7736-7742.10.1016/j.eswa.2012.01.080Search in Google Scholar

9. Han, H. K., H. S. Kim, S. Y. Sohn. Sequential Association Rules for Forecasting Failure Patterns of Aircrafts in Korean Airforce. - Expert Systems with Applications, Vol. 36, 2009, No 2, pp. 1129-1133.10.1016/j.eswa.2007.10.012Search in Google Scholar

10. Wright, A. P., A. T. Wright, A. B. Mc Coy, D. F. Sittig. The Use of Sequential Pattern Mining to Predict Next Prescribed Medications. - Journal of Biomedical Informatics, Vol. 53, 2015, pp. 73-80.10.1016/j.jbi.2014.09.003Search in Google Scholar

11. Shaw, A. A., N. P. Gopalan. Finding Frequent Trajectories by Clustering and Sequential Pattern Mining. - Journal of Traffic and Transportation Engineering (English Edition), Vol. 1, 2014, No 6, pp. 393-403.10.1016/S2095-7564(15)30289-0Search in Google Scholar

12. Tsai, C.-Y., B.-H. Lai. A Location-Item-Time Sequential Pattern Mining Algorithm for Route Recommendation. - Knowledge-Based Systems, Vol. 73, 2015, pp. 97-110.10.1016/j.knosys.2014.09.012Search in Google Scholar

13. Xifeng, Y., H. Jiawei, A. Ramin. CloSpan: Mining Closed Sequential Patterns in Large Datasets. - In: In SDM, 2003, pp. 166-177.Search in Google Scholar

14. Gomariz, M. C., R. Marín, B. Goethals. CLASP: An Efficient Algorithm for Mining Frequent Closed Sequences. - Advances in Knowledge Discovery and Data Mining, 2013, pp. 50-61. 10.1007/978-3-642-37453-1_5Search in Google Scholar