1. CENTRE Cellular Network’s Positioning Data Generator Fosca GiannottiKDD-Lab Andrea MazzoniKKD-Lab Puntoni SimoneKDD-Lab Chiara RensoKDD-Lab

2. Why to generate data? Trouble in finding  Due to ITC Companies reticence  …and for legal and privacy reasons Need to have ad-hoc datasets  To improve algorithm development  To have a tools for validation and testing phases

3. CENTRE: CEllular Network Trajectory Reconstruction Environment : A positioning data (LOG) generation Environment aimed to Mobile technology Developed as tool of GeoPKDD projects

4. GSM technology

5. GeoPKDD: Geographic Privacy-Aware Knowledge Discovery & Delivery

6. The Idea To generate positional mobile data (LOG) by the simulation of the event deriving from:  Trajectories of hypothetical mobile network’s users that travel on territory  The resulting survey of this movements using synthetic ad-hoc GSM coverage (the set of BTSs) So we can analyze the set of LOGs and recontruct trajectories of mobile network’s users

7. Motivation With this model we want to reach:  More rigorous and realistic semantic of generating data.  Possibility to compare synthetic trajectories with reconstructed one.  Chance of validate mining and knowledge discovery algorithms results with synthetic trajectories.

8. CENTRE architecture

9. What CENTRE do… First of all we generate a sequence of spatio-temporal points represent a trajectory. We can customize:  Starting point  Velocity  Agility  Direction  Groups of behavior  Infrastructures, ect. Then we overlap a set of antennas represented by circles of their coverage areas:

10. LOG extraction Where: 1. Obj_ID is the identifier of observed object 2. BTS_ID is the identifier of antenna that made this survey 3. TimeStamp is the time of survey 4. D is a evaluation of distance from object to the center of BTS So LOG is represented by a tuple: ( Obj_ID, BTS_ID, TimeStamp, d) Result of extraction:  LOG at time tt2 (P2) {Cell1, BTS1, tt2, d12}  LOG at time tt3 (P3) {Cell1, BTS1, tt3, d13}, {Cell1, BTS2, tt3, d23}, {Cell1, BTS3, tt3, d33}  LOG at time tt4 (P4) {Cell1, BTS2, tt4, d24}

11. Dataset

12. Trajectories reconstruction Once LOG are produced and stored, we forget about synthetic trajectories and try to reconstruct these only from:  LOG collection  Synthetic coverage

13. Information types Reconstruction was performed considering all LOGs produced on a single temporal instant for a single trajectoty The number of LOGs with same time and same device identificator (id_cell) represent the number of simultaneous relevations 3 LOGs 1 LOG 2 LOGs

14. Recontruction method When we have:  Only one relevation: our point may be inside the entire antenna covered area, so we take antenna center as point positions  With two or more relevations: point may be only inside the intersection area of them, so we take centroid of this area as point position

15. Reconstructed trajectories dataset

16. And now …examples!

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19. Now we work on… Make new extensions to main generation engine  In order to test and validate spatial KD algorithms with more efficiency and accuracy. Change old code (that was derived from GSTD code)  Introducing improvements on class structures  Introducing new data characterization specially on spatial and temporal aspects

20. Multiple generation engines The Idea is to develop extensions to main engine every time we need new features to test and validate KD algorithms. And use each time the best implementation on sinthetic trajectories production engine depending of type of data we need to obtain

21. Density based clustering We have seen that for best results with this algorithm is useful to have a simple method for:  create clusters and  identify relation between objects and clusters.

22. Attraction engine For this particular type of algorithm we are developing a new engine extension that use an attraction-like mechanism. Each objects chooses and tries to reach its next attraction area. When it reaches its destination area chooses another one, and so on…

23. Cluster construction A cluster if formed by a set of objects that are forced to pass through a sequence of areas.

24. …a simple example In this scenario we can see one object that every time chooses a region with a completely random order. Chosen a region, and a point on it, the object tries to reach this point. …and so on

25. Others improvements Formalization of some concepts (at code level):  Spatio-temporal data  Spatio-temporal object  Trajectory and a real measures in data values:  Positions are expressed in meters  Velocities are expressed in meters/seconds  Times are expressed in seconds

26. Conclusions Nowadays work is in progress, and we hope to test as soon as possible a Density Based Algorithm on this new generation engine Contextually we also work on a engine for testing Temporal and Sequential Frequent Pattern Algorithm And also to improve generator use, through simplification of number and form of parameters, graphical interface, ect.