1. Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques Mert Özer, Ilkcan Keles, Ismail Hakki Toroslu, Pinar Karagoz, Hasan Davulcu The Computer Journal 2015

2. CONTENTS Introduction Data & Problem Definition Proposed Methods Evaluation & Experimental Results Conclusion & Discussion

3. INTRODUCTION Location Prediction Sequential Pattern Mining Motivation

4. Mobile phone operator companies are eager to know the location flow of their users to build more reasonable advertisement strategies. to build more reasonable base station installation plans. can be used by city administrators to determine mass people movement patterns around the city.

5. PROBLEM DEFINITIONS Three Sub-Problem Definitions of Broader Location Prediction Problem Next Location and Time Prediction Using Spatio- Temporal Data Next Location Change Prediction Using Spatial Data Next Location Change and Time Prediction Using Spatio-Temporal Data

6. Problem Definition Next Location and Time Prediction Using Spatio- Temporal Data to predict the location and the time of the next action in the next time interval of the user divide a day into time intevals cluster base stations according to their locations into regions

7. Training Data Definition （ Call Detail Data ） Have 11 attributes base station id#1, phone number#1, city plate#1, base station id#2, phone number#2, city plate#2, call time, cdr type, url, duration, call date. The real data is obtained from one of the largest mobile phone operators in Turkey.

8. Training Data The data corresponds to an area roughly 25,000 km 2 with a population around 5 million. Almost 70% of the population is concentrated in a large urban area of approximately 1/3 of the region. The data contains roughly 1 million users' log records for a period of 1 month. The whole area contains 13281 base stations.

9. Method 1 - Next Location and Time Prediction Using Spatio-Temporal Data Preprocessing Extracting Regions Extracting Frequent Patterns Prediction

10. Method 1 - Preprocessing This paper filters unnecessary attributes. Daily call data records of each user are merged into one row in a temporal order. Daily sequences structured as pairs are created.

11. Method 1 - Preprocessing

12. Method 1 – Extracting Regions Under high number of base stations, it is not practical to consider each as the center of movement and predict accordingly. The paper clustered 13281 base stations into 100 regions by using K-Means algorithm. Base station ids in the preprocessed data are replaced with the corresponding region ids in the daily sequences.

13. Method 1 – Extracting Regions Extracted Regions

14. Method 1 – Extracting Frequent Patterns Work with four parameters; preprocessed training data pattern length (the length of the desired frequent pattern) minimum support (the minimum ratio of the pattern to occur in order to be identified as frequent) time interval length (is used to discretize the time of the day, defines the length of each interval)

15. Method 1 – Extracting Frequent Patterns The method is very similar to AprioriAll algorithm. Frequent pattern generation. The paper traverses the data to extract all candidate desired length patterns. The ones that fall below the minimum support threshold are eliminated.

16. Method 1 – Sample Frequent Patterns Three sample frequent patterns with the length 4 are presented below.

17. Test sequence is length of (k-1) and we want to predict k th element. Then this (k-1) length pattern is searched in frequent pattern set. If pattern starting with test sequence have been found, the last element of the matching pattern with the maximum support is generated as prediction. Method 1 - Prediction

19. Method 1 – Prediction – Time Tolerance Difficult to find exact matches between the current user navigation sequence and existing frequent sequences. Base station id and time interval pairs can be moved forward and backward in time with tolerance value. Test instance: Frequent pattern set: {...,,...} Time tolerance value: 15 minutes Prediction: (52,1700)

20. This paper validated the results with real data obtained from one of the largest mobile phone operators in Turkey. Results are very encouraging, and we have obtained very high accuracy results in predicting the next location change and time of users. EVALUATION & RESULTS

21. Evaluation Metrics This paper introduced 2 metrics to evaluate our methods; g-accuracy: g-accuracy = p-accuracy: p-accuracy= The reason for using two different accuracy calculation is due to the fact that maybe there is no matching frequent pattern found for the queried instance.

22. This paper analyzes the effect of length of the frequent patterns and support threshold using the following parameter values. Pattern Length is 6 Minimum Support is 1.00E-6 Cluster Count is 100 Time Interval Length is 15 min Time Tolerance is 75 min Results of Method 1

23. Results – Pattern Length

24. When the pattern length increases, predicting g- accuracy decreases. This is due to the fact that the number of longer frequent patterns is much fewer than the number of shorter frequent patterns.

25. Results – Minimum Support

26. When minimum support threshold value increases, prediction g-accuracy drops. The reason for this result is that as minimum support threshold increases the number of generated frequent pattern decreases.

27. CONCLUSION & DISCUSSION This work shows that determining the potential change of location of mobile phone users through sequential pattern mining techniques is possible with quite high accuracy. This paper elaborated the effect of several factors such as pattern length tolerance and multi prediction limit and further improved the prediction performance.

28. Thank you !