CENTRE

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CENTRE

• Cellular Networks Positioning Data Generator

Fosca Giannotti KDD-Lab Andrea Mazzoni KKD-Lab Pun
toni Simone KDD-Lab Chiara Renso KDD-Lab
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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

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CENTRE

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

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GSM technology
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GeoPKDD Geographic Privacy-Aware Knowledge
Discovery Delivery
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The Idea

• To generate positional mobile data (LOG) by the
  simulation of the event deriving from
• Trajectories of hypothetical mobile networks
  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 networks users

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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.

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CENTRE architecture
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What CENTRE do

• Then we overlap a set of antennas represented by
  circles of their coverage areas

• 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.

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LOG extraction

• 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

• Where
• Obj_ID is the identifier of observed object
• BTS_ID is the identifier of antenna that made
  this survey
• TimeStamp is the time of survey
• D is a evaluation of distance from object to the
  center of BTS

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Dataset
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Trajectories reconstruction

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

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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
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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

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Reconstructed trajectories dataset
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And now examples! ?
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?
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?
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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

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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

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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.

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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

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Cluster construction

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

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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

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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

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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.