Report on Multi-agent Data Fusion System: Design and

implementation issues1

By Ganesh Godavari

Data Fusion

• Data Fusion : task of data processing aiming at making decisions on the basis of distributed data sources specifying an object

• Data sources– Different physical nature

• Electromagnetic signals, sensor data…

– Different accuracy• Reliability?

JDL views• Data and Information Fusion

– Multi level process – Level0

• Fusion of sensor signals to produce semantically understandable data– Level1

• Make decisions with regard to classes of objects– Level2

• Asses a situation constituted by the set of aboce objects – Level3

• Impact assessment i.e. adversary intent assesment on the basis of situation development prediction

– Level4• Calculation a feedback like planning resource usage, sensor management

etc– Level5

• Human activity and situation management

Applications of DF

• Some applications of data fusion – Detection of intrusions into computer networks

• Large data available through tools like tcpdump, IDS…

– Analysis and prognosis of natural and man-made disaster development

• Prediction and prevention of calamities like earthquakes, floods, weather conditions, nuclear explosions effect

Focus/strategy of the paper

• Focus– Design and implementation of DF system at

Level1

• Proposed strategy– Multilevel hierarchy of classifiers– Source based classifiers

• Decision based on data of particular sources followed by meta-level decisions

Advantages of the strategy

• Advantages– Decrease of the data sources information

exchange– Simplicity of data source classifiers fusion

even if they use different representation structures, certainty, accuracy etc..

– Use of mathematically sound mechanism for combining decisions of multiple classifiers

Problems inherent to DF applications

• Cause of concern– Data sources are physically distributed

• Spatially distributed, represented in different databases, located on different hosts

– Hetrogeneous• Diversity of possible data structures, difference in

data structures/data specification language

Problem list

• Problem – development of the shared thesaurus

providing for monosemantic understanding of the terminology

– Entity identification problem• Data specifying an object is represented in

different data sources• Non coherency of data measurement scales

– data specifying in different sources the same entity attribute can be of different structures.

Technical terms

• Decision– In DF tasks its classification of an entity

(object, state of an object, situation etc)

• Base-level/base classifiers– scheme of data fusion, each local data source

is associated with a single or several classifiers

Classification of multi-level classifiers

• Approaches for combining decisions of multilevel classifiers can be grouped into four groups:– Voting algorithms;– Probability-based or fuzzy algorithms;– Meta-learning algorithms based on stacked

generalization idea;– Meta-learning algorithms based on classifiers'

competence evaluation.

Meta classification scheme

Competence based approach to combine decisions of multiple

classifiers

Meta model of training and testing data

• Important peculiarities from learning viewpoint– Data are distributed in space and stored in different

databases;– Each data source only partially specifies the same

object to be classified in terms of attributes which can be different in different data sources;

– Data can be incomplete; it can contain particular attribute values and also the total records in a source missed

Questions

?

References

• Multi-agent Data Fusion Systems: Design and Implementation Issues by Vladimir Gorodetski, Oleg Karsayev and Vladimir .Samoilov