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Data Fusion and Semantic Web: Meta-Models of
Distributed Data and Decision Fusion.
Project Report
Vladimir Gorodetski,
Oleg Karsaev,
Vladimir Samoilov
Intelligent System Laboratory of the
St. Petersburg Institute for Informatics and Automation
E-mail: {gor, ok, samovl}@mail.iias.spb.su
http://space.iias.spb.su/ai/english/gorodetski.htm
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Title of the Project
“Autonomous Information Collection, Knowledge
Discovery Techniques and Software Tool
Prototype for Knowledge-Based Data Fusion”
Project from
European Office of Aerospace Research and
Development (EOARD) –AFRL/IF (USA)
(December 2000 - December 2003)
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Outline of the Project Presentation
1. Outline of the Data and Information Fusion problems
2. Project research objectives
3. Examples of case studies and applications used
4. Ontology-centered meta-model of data sources
5. Meta-model of decision fusion
6. Multi-agent architecture
7. Conclusion
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Tasks and Applications of Data and Information Fusion
Application Fields
Critical areas of human society security, life support,
security of critical state infrastructures, large-scale
logistics, natural and man-made disasters, etc.
Examples of Applications
 Assessment and prediction of situations,
 Resource management and rescue operation planning in
large scale natural and man-made disasters,
 Decision making and planning of rescue operations in
systems like US 911, Situational awareness and
prediction for terrorist intents and anti-terrorist activity
planning,
 Military situation assessment,
 Safeguard of critical plants like nuclear power stations,
electrical power grids, etc.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Information Fusion-Definition
Sensor
1
Sensor
2
…
Sensor
N
Distributed data sources
“…data fusion is a formal framework in which means and tools for the
alliance of data originating from different sources are expressed. It
aims at obtaining information of greater quality; the exact definition of
“greater quality” will depend on the application”
(JDL-Joint Directors of Laboratories model, USAF)
Level 0-Pre-processing of sensor data
Areas of the
current and Future
research projects
are yellowed
Level 5-User
refinement
Level 1-Object assessment
Level 2- Situation assessment
Level 3- Impact assessment
Level 4Process
refinement
Data Base Management System
Support DB
Fusion DB
HumanComputer
interface
Distributed
information
sources
Sensor management,
resource management
(Erik Blash, Fusion-2002, July, 2002, Annapolis, USA)
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Project Research Objectives
Development of DF software tool providing support
for design (first of all, for learning!) and
implementation of DF applications of broad spectrum,
in particular, providing support for :
 Development of ontology-based meta-models of data
sources, meta-model of decision fusion and
conceptual model of DF software tool,
 Development of Multi-agent architecture and
 Design and implementation of applications of broad
spectrum.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Examples of case studies and application used in
Projects
Case studies
-KDD Cup99 dataset -- Preprocessed relational data
specifying Intrusion Detection task
http://kdd.ics.uci.edu/databases/kddcup99.html
-Landsat Multi-Spectral Scanner image dataset
http://www.dfc-grss.org/data/grss_dfc_0010.zip
-STULONG dataset– Longitudinal Study of Atherosclerosis
Risk Factors
http://euromise.vse.cz/challenge/en/projekt/index.php
Application to be used in debugging and validation of MAS
DK-DF - Intrusion detection learning system (Project also
funded by EOARD/AFRL)
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Subtasks of the Project matching Semantic Web Mining area
1. Design and implementation of meta-model of data
sources caused by heterogeneity and distribution of
data to be fused.
2. Design and implementation of meta-model of
distributed learning.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Multiplicity of Data Sources Presenting User’s Activity in
Intrusion Detection system
Host-based sources
SPP - Statistical processing program
Log of commands run by
users plus resource
System program 3
System program 2
Log of all user logins/logouts and
system startups and shutdowns
Auditing subsystem of OS
Filtered OS audit trail
DNS log
SPP
DNS statistical data
HTTP log
HTTP service
SPP
statistical data set 2
SPP
SPP
SPP
Telnet log
Telnet service
FTP service
HTTP statistical data
statistical data set 3
OS audit trail statistical data
Mail log
Mail service
statistical data set 1
SPP
Log of all login failures
System program 1
DNS service
SPP
Mail statistical data
SPP
FTP log
Tcpdump
statistical
data
Network-based sources
Telnet statistical data
SPP
SPP
FTP statistical data
Tcpdump
TCPDUMP (WINDUMP)
IP
ICMP
Header Header
Network Packet
…
DNS
Data
…
Network Packet
HTTP
Data
…
Network Packet
SMTP
Data
…
Network Packet
TELNET
Data
Network Packet
…
FTP
Data
UDP/TCP
Header
IP
Header
Network Packet
Network Traffic
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Interrelation of Semantic Web and Ontology-oriented
Research within the Project
Semantic Web considers development and standardization
of the ontology specification languages (XML, RDF,
DAML+OIL), ontology-based query languages, ontology
editors, etc).
Semantic Web Mining considers specific problems of
ontology design technology for (Web-based) Data Mining
systems.
Any DF system technology supposes (Web-based)
distributed Data Mining and KDD and that is why it is a subarea of the Semantic Web Mining.
Ontology-based Data and Information Fusion system
design put a number of specific problems of technological
sort. Among them, the most important one is a technology
for distributed design of distributed ontology.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
What is distributed design of distributed ontology?
Data Sources Meta-model
Sensor
Data Source
Data Source
management
agent
Meta-data
manager
Sensor
Data Source
Data Source
management
agent
Data Source
Manager
Data Source
Manager
Ontology-based
meta-model of
Data sources
…….
Data Source
Manager
Data Source
management
agent
Data Source
management
agent
Sensor
Data Source
Data Source
Manager
“KDD
Master”
Agent
Data Source
Sensor
Meta-model =Ontology + Data source models at meta-level
supporting a unified view of data of particular sources
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
DF system ontology
DF Problem
ontology
Shared component of Application ontology
Private
Private
component of component of
application
application
ontology of data ontology of data
source 2
source 1
…
Private
component of
application
ontology of data
source k
Tower of DF application ontology components
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Distributed Ontology and Protocols for Distributed
Ontology Design
KDD
agent of
source 1
Data
Source 1
DS- 1
management
agent
KDD
agent of
source k
Meta-level
KDD
Agent
Protocols,
Functions
Shared component of
application ontology
Private component of
application ontology-k
Protocols,
Functions
Problem and shared
components of
application ontology
Agent 1
Shared component of
application ontology
Private component of
application ontology-k
…….
Shared component of
application ontology
Agent k
Shared component of
application ontology
Private component of
application ontology-3
Protocols,
Agent 2 Functions
DS- 2
management
agent
Data
Source 2
Data
Source k
DS- k
management
agent
KDD
agent of
source 2
“KDD
Master”
Agent
Protocols,
Functions
Protocols,
Functions
Private component of
application ontology-3
Agent 3
DS- 3
management
agent
KDD
agent of
source 3
Data
Source 3
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Particular Tasks to Be Solved on the Basis of Meta-model
of Data Sources
• Providing for monosemantic understanding of
terminology used in data specification by distributed
analysts;
• Solution of the entity identification problem;
• Providing consistency of data representation (in case if
the same attributes are presented differently in different
data sources);
• Providing a gateway between ontology and distributed
databases accessibility making possible interaction
between ontology and distributed databases, and
several other tasks.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Meta-model of Data Sources: Ontology + Protocols =>
Monosemantic understanding of terminology
Monosemantic understanding of terminology among DF
system components is provided by shared vocabulary used by
DF system distributed entities for communication. This
excludes different naming of the same entities and their
properties in different sources, and equal naming of different
entities within different data sources thus providing integrity
and consistency of shared vocabulary.
Protocols
Supports distributed collaborative design of coherent ontology
by distributed analysts.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Example of Application Ontology:
High-level Part of Intrusion Detection Domain Ontology
Reconnaissance
CI
Collection of
R
Network attack
ABE Applications
Information
IdentificationIS
of services
IO
Identification
of OS
IH
A
UE
and Banners
Enumeration
Implantation and
threat realization
Users and Groups
Enumeration
I
Creating Back
Resource
Doors
RE Enumeration
Getting
Identification
CBD
Access to
of hosts
SPIH
Resources
CT
Network Ping DC Proxy
Port Scanning
Covering
GAR
Sweeps
GAD
scanning
Tracks
ER
ST
Gaining
TCP connect Escalating
PS
Additional
scan
Dumb
Privilege
TR Threat
Data
SS
Notions of
host scan
TCP SYN
Realization
DHS
micro-layer
scan
ID DOS
CD
Scanning SFB
SF
Denial of
'FTP
Confidentiality
Integrity
Service
TCP FIN
Bounce'
SN
destruction
destructio
SX scan
TCP Null
n
TCP Xmas
scan HS
SU
"Part of" relationship
Tree scan
Half scan
UDP scan
Notions of lower levels
“Subclass of" relationship
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
The Simplest ("top-down") Meta-protocol for
Collaborative Ontology Design
…
Source 1. Local
source expert
Source 1: Data
preparation agent
Source N: Local Source N: Data
source expert
preparation agent
Meta-data
Application
description agent domain expert
Forming the basic variant of ontology
Sending the basic variant
Analysis of the suggested basic variant
Sending the basic variant
Analysis of the suggested basic variant
Modifying and expanding the ontology
Synchronization of modifications by the basic protocol
Modifying and expanding the ontology
Synchronization of modifications by the basic protocol
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Ontology Synchronization Protocol Represented in
Terms of UML-sequence Diagram
2
1
Current state
reading
3
4
5
6
7
8
Request for required
ontology descriptions
Legend:
Unconfirmed changes buffer query
Representation of
current state of ontology
Forming the current
representation of ontology
Changes of ontology
Recording the changes
Sending current changes to the shared ontology
Forming the current
representation of ontology
Representation of current state
of ontology
Confirmation/rejection of suggested changes
Periodic request for suggested
changes
Verification of changes
Introducing changes
Introducing of changes
Deletion of verified changes
9
Adding changes to ontology
Deletion of verified changes
1. Local source expert
2. Local source data
managing agent
3. Local source
ontology
4. Local source: buffer
of temporary
changes
5. KDD master (Metadata description
agent)
6. Shared ontology
7. Meta-level agent:
buffer of temporary
changes
8. Application expert
(meta-level)
9. Local source
determining the
modified ontology
part
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Meta-model of Data Sources:
Entity Identification Problem
Explanation of Entity Identification Problem
Data Source 3
# of
case
Attributes of
Data source 3
1
Data
Source 2
2
Data
Source 1
4
8
9
11
# of
case
Attributes of
Data source 1
# of
case
Attributes of Data
Source 2
14
15
1
1
4
3
5
4
9
7
11
9
12
11
14
15
15
19
17
19
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Demonstration of Entity Identification Problem: Intrusion
Detection Application
Host-based sources
statistical data on Connection 1
Log of commands run by
users plus resource
System program 3
SPP
statistical data on Connection N
System program 2
System program 1
Auditing subsystem of OS
SPP
Filtered OS audit trail
OS audit trail statistical data on Connection 1
Mail log
Mail service
statistical data on Connection 1
Log of all user logins/logouts and
system startups and shutdowns
SPP
FTP log
FTP service
Network-based sources
SPP
statistical data on Connection N
OS audit trail statistical data on Connection N
Case 1
Mail statistical data on Connection 1
SPP
Case N
FTP statistical data on Connection N
Tcpdump
Tcpdump statistical data on Connection 1
…
SPP
TCPDUMP (WINDUMP)
Tcpdump statistical data on Connection N
TCP Hdr IP
(FIN) Hdr
…
SMTP
Data
…
SMTP
Data
…
SMTP
Data
…
SMTP TCP IP
Data Hdr Hdr
…
TCP Hdr IP
(ACK) Hdr
TCP Hdr IP
(SYN) Hdr
Connection 1
…………………………………………………………………………………………
TCP Hdr IP
(FIN) Hdr
…
FTP
Data
…
FTP
Data
…
FTP
Data
…
FTP
Data
TCP IP
Hdr Hdr
…
TCP Hdr IP
(ACK) Hdr
TCP Hdr IP
(SYN) Hdr
Connection N
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
A Technique for Entity Identification Problem




In the DF problem ontology, for each instance of an
object to be classified, the notion of entity identifier ("ID
entity") is introduced. This entity identifier plays the role
of the primary key of the instance (in analogy with the
primary key of a table).
For each such identifier, a rule as a component of the
shared part of application ontology is defined, which can
be used to calculate the value of the instance key. A rule
is a function which arguments are chosen from the set of
this entity attributes. A rule is defined for each local data
source to uniquely connect the entity identifier and the
local primary key in this source. This rule specifies:
how to derive the local primary key of instance from the
entity identifier value;
how to derive the entity identifier value from the value of
the local primary key of an instance of the source.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Meta-model of Data Sources: Diversity of Measurement
Scales of the Same Attributes in Different Data Sources
Let X be an attribute in application ontology that is measured
differently in different sources.
1. In the shared component of application ontology, the type
and the measurement unit of the attribute X are
determined. Selection of attribute X specification within
shared part of application ontology is made by experts
during negotiations according to a synchronization
protocol.
2. In all the sources where X is present, expressions are
determined for this attribute, through which it can further
be converted into the same scale in all the sources.
This allows using the values of attributes on the metalevel regardless of the data source from which they
originated.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Meta-model of Data Sources: Interaction of Ontology
and Databases of Sources
The task arises due to the
fact that application
ontology entities are
specified in terms of
ontology notions but their
instances are represented
in terms of database
language.
To provide interaction of
ontology and databases of
sources (accessibility of
data requested in ontology
terms) , a special gateway
is developed.
Application
DF problem ontology
DF application ontology
Client-gateway
DF problem ontology
DF
Application
Local source data
ontology
properties
Access via VIEW objects
Database objects
Local data source
Three-level hierarchy of access to the
database objects
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Meta-model of Distributed Learning
Components of meta-model of distributed learning:
• Meta-model of decision making and combining
decisions of multiple base-level classifiers;
• Model of distributed data management (allocation
training and testing data sets for learning particular
classifiers; management by computation of metadata for upper level example-based learning, etc.);
• Approaches and formal techniques used for
combining decisions.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Conclusion: Future work
.
1. Development of sophisticated ontology editor
supporting distributed design of a distributed
ontology.
2. Further design and Implementation of Data
Fusion System software tool for development and
implementation of particular distributed
applications in Data Fusion area.
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland
Thank you!
For more information and related publications please
contact
E-mail: [email protected]
http://space.iias.spb.su/ai/english/gorodetski.htm
Acknowledgement
This research is funded by
AFRL/IF (EOARD), 1999-2003
2nd Semantic Web Mining Workshop at ECML/PKDD-2002, August 2002, Helsinki, Finland