CENTRE

Cellular Network’s Positioning Data Generator

Fosca Giannotti KDD-Lab Andrea Mazzoni KKD-LabPuntoni Simone KDD-LabChiara Renso KDD-Lab

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

CENTRE:

CEllular Network Trajectory Reconstruction Environment:

A positioning data (LOG) generation Environment aimed to Mobile technology

Developed as tool of GeoPKDD projects

GSM technology

GeoPKDD: Geographic Privacy-Aware Knowledge Discovery & Delivery

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

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.

CENTRE architecture

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:

LOG extraction

Where:1. Obj_ID is the identifier of

observed object2. BTS_ID is the identifier of

antenna that made this survey3. TimeStamp is the time of survey4. 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}

Dataset

Trajectories reconstruction

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

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

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

Reconstructed trajectories dataset

And now …examples!

…

………

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

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

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.

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…

Cluster construction

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

…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

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

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.