Download - Intelligent Information Systems: Second-Order Informatics for the Bioinformatics Challenge
Temple University, Center for IST, April 2005
Department of Computer Science
Intelligent Information Systems Lab
University of Niš
Intelligent Information
Systems: Second-Order Informatics
for the Bioinformatics
Challenge
Dr Milorad Tošić
4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Content: Global Challenge
Problem statement Paradigm shift Bioinformatics?
Methodology for Approaching the Problem
Towards Second-Order Informatics: A Systems Approach Interaction, Knowledge and Systems Structure: Hyper-Graph model Meta-Architecture Example: Client-Server interaction Example: Self-Organizing architecture Example: Semantic view on Interaction between two systems Example: Community of Practice
Conclusion
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Relevant Resources Technologies
Modeling (UML, MOF, MDA)
Knowledge Management
Computer-Human Interaction
Semantic Web
Multi-agent Systems
Component-Based Systems
Concepts Ontology
Meta Data Structures
Service Oriented Architecture (SOA)
Metaheuristics
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Global challenge: Problem Statement Evidence of disruption in
environment of the Informatics
“.com” bubble burst
Globalization Internet infrastructure Outsourcing on the global scale
Software intensive systems Bioinformatics e-Government e-Learning
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Global challenge: Paradigm Shift What is a Paradigm?
Based on Dr James Schombert’s glossary http://abyss.uoregon.edu/~js/glossary/paradigm.html
Thomas Kuhn's landmark book, The Structure of Scientific Revolutions : "paradigms" - conceptual world-views, that consist of formal theories, classic experiments, and trusted methods.
Scientists typically accept a prevailing paradigm and try to extend scope of the paradigm by refining theories, explaining puzzling data, and establishing more precise measures of standards and phenomena.
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Global challenge: Paradigm Shift What is the Paradigm Shift?
However, accumulation of the results eventually leads to insoluble theoretical problems or experimental anomalies that expose a paradigm's inadequacies or contradict it altogether.
This accumulation of difficulties triggers a crisis that can only be resolved by an intellectual revolution that replaces an old paradigm with a new one.
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Global challenge: Paradigm Shift
What the next Paradigm will be?
We do not know now!!!
We have to act under uncertainty!!!
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Global challenge: Bioinformatics[Cohen, J., “Computer Science and Bioinformatics”,
Communications of the ACM, March 2005, Vol.48, No.3, pp.72-78]
Synergy of CS and Science communities:
How much effort CS people have to invest to be able to work in bioinformatics? (Investment)
What bioinformatics topics are closest to CS? (Application scope)
Should CS departments prepare their graduates for careers in bioinformatics? (Education)
How to deal with the cultural differences between CS and natural science communities? (Social networks)
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Global challenge: Our answerFirst-Class Entities of the Second-Order Informatics Methodology
ABC and Agile methodologies (work in presence of uncertainty)
Interaction Model of a Software-Intensive System
Intelligent Information System (model aware system) Ontology is the enabling driver (knowledge aware
system) Semantics Application Domain Transparency
Meta-Architecture Self-Organizing System Architecture
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Methodology: ABC The ABC Model of Organizational Improvement
[D.C. Engelbart, “Toward High-Performance Organizations: A Strategic Role for Groupware”, 1992., www.bootstrape.org]
ACore Business
Activity
BImproves A'sCapabilities
CImproves B'sCapabilities
Product R&D, mfg, marketing,sales, etc. Examples:Aerospace - producing planes,Congress - passing legislation,Bioinformatics - new drugs
Reduce product life-cycletime - to make faster, smarter,more innovative, higher-qualityA activities
Reduce improvement-cycle time- to make faster, smarter, moreinnovative, higher-quality Bactivities
Info
rmat
ics
Sec
on
d-O
rder
Info
rmat
ics
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Methodology:
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Agile Methodology:
•Work in presence of uncertainty
Reflective Practice:
• “The thing that make us smart” (what people and computers can do together?) [Fisher, 2001]
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
First-Order Informatics: System Model Intuitive, informal definition
Set of components cause change in the environment.
The actions are transferred as data by means of a protocol constituting medium for the transfer
The protocol and data together constitute the communication medium over which the information about change is communicated back to components
Observer is outside of the system
data information communicationcomponents
action(change)
protocol
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Structure Reasoning about interactions between components
Observer is still outside of the system
Structure is a tuple <S,ρ>, where S is set of structures and ρ is relation in S, ρ ⊂S2
data information communicationcomponents
action(change)
protocol
interaction structure
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
SystemArchitecture
Towards Second-Order Informatics: Architecture & System System is a collection of systems and structures, also called
components, that a) Interact together (towards one or more goals),
data information communicationcomponents
action(change)
protocol
interaction structure
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Architecture & System System is a collection of systems and structures, also called
components, that a) Interact together (towards one or more goals), b) Exhibit set of observables that may be different from the collection of observables exhibited by
individual components
SystemArchitecture
interaction structure
data information communicationcomponents
action(change)
protocol
contextknowledgeobservable
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
SystemArchitecture
interaction structure
data information communicationcomponents
action(change)
protocol
Towards Second-Order Informatics: Architecture & System Observable (metadata) is attached to the data (data has a meaning
now) only through interaction between components of the system (including observer (s)) that is about to recognize the meaning.
Observer is considered within the system now (one of the interacting components within the system)
contextknowledgeobservable
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
SystemArchitecture
interaction structure
data information communicationcomponents
action(change)
protocol
Towards Second-Order Informatics: Architecture & System The architecture exhibits uncertainty in perceived behavior due to the
interaction within the structure. The behavior represents:
goals, cultural aspects, self-interest, social protocols, trust, etc.
contextknowledgeobservable uncertainty
behavior
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
System'sDinamics
SystemArchitecture
interaction structure
data information communicationcomponents
action(change)
protocol
Towards Second-Order Informatics: Architecture & System Behavior and protocol define the
system’s dynamics.
contextknowledgeobservable uncertainty
behavior
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
System'sDinamics
SystemArchitecture
interaction structure
data information communicationcomponents
action(change)
protocol
language L &vocabulary conceptualization model of L
Towards Second-Order Informatics: Architecture & System Model of the language L represents context of the interaction.
It is refinement of the adopted conceptualization.
Language L and the corresponding vocabulary define domain of the observable. The domain is one of the possible realizations of the conceptualization.
contextknowledgeobservable uncertainty
behavior
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
System'sDinamics
Fine-Grain Ontology
Coarse-Grain OntologySystem
Architecture
interaction structure
data information communicationcomponents
action(change)
protocol
language L &vocabulary conceptualization model of L
Towards Second-Order Informatics: Architecture & System Ontologies:
Coarse-Grain Ontology: Common-ground knowledge
Fine-Grain Ontology: Context-specific knowledge
contextknowledgeobservable uncertainty
behavior
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Graph-Theoretic model of the Structure
Serializable Hyper-Graph (SHG)
[Tosic, M., “Persistent object-oriented hyper-graph model for Maximal Common Substructure (MCS) search”, 1998]
Structured way to reason about a Collection[About Collection see: Quan,D., Karger,D., “How to Make a Semantic Web Browser”, WWW 2004, May 17-22, 2004, New York]
Different characteristic substructures are represented on an uniform way
Efficient implementation of topology-based comparison criteria
Pointer-based data structure with no extra delay due to serialization
Persistent storage of such objects is straightforward
Easy to adopt to any distributed objects technology
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Structure as SHGDefinition: A hyper-graph HG is an ordered two-tuple
HG = (C,E) ,
where C is set of hyper-graphs that are containers of HG, and E is a set of hyper-graphs that are elements of HG:
C = { c | c > HG }, E = { e | e < HG }
Definition: An undirected hyper-graph HG is an ordered two-tuple
HG = ((C, E), I) ,
where (C,E) is hyper-graph, and I is set of undirected hyper-graphs that are neighbors of the HG. We say that HG is in undirected connection relation with its neighbors.
Definition: The undirected connection relation is an equivalence relation.
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Structure as SHGDefinition: An directed hyper-graph HG is an ordered three-tuple
HG = ((C, E), I, O) ,
where (C,E) is hyper-graph, I is set of directed hyper-graphs that are input neighbors of
the HG, and O is set of directed hyper-graphs that are output neighbors
of the HG.
We say that HG is in directed connection relation with its neighbors.
Definition: The directed connection relation is an order relation.
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Structure as the SHG Example
v1
v5
v7
v8
v6
v4
v2
v3
e23e12
e45e24
e35
e57
e46 e67
e68
v1:id = v1;type = VERTEX;Container = {G1};Elements = {};InElements = {e12};
v2:id = v2;type = VERTEX;Container = {G1};Elements = {};InElements = {e12, e23, e24};
G1:id = G1;type = GRAPH;Container = {};Elements = {v1, … , v8, e12, e23, … ,e68};InElements = {};
. . .
e12:id = e12;type = EDGE;Container = {G1};Elements = {};InElements = {v1,v2};
e23:id = e23;type = EDGE;Container = {G1};Elements = {};InElements = {v2, v3};
. . .
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Structure as the SHG Example
v5
v7
v6
v4e45 e57
e46 e67
G2:id = G2;type = GRAPH;Container = {};Elements = {g1,g2,g3,g4, e1,e2,e3,e4};InElements = {};
v1
v2
e12 v5
v4
v2
v3
e23
e45e24
e35
v8
v6e68
g1 g2 g3 g4e1 e2 e3
g1:id = g1;type = GRAPH;Container = {G2};Elements = {v1,v2,e12};InElements = {e1};
g2:id = g2;type = LOOP;Container = {G2};Elements = {v2,v3,v4,v5,e23,e24,e35,e45};InElements = {e1, e2};
e1:id = e1;type = EDGE;Container = {G2};Elements = {v2};InElements = {g1,g2};
e2:id = e2;type = EDGE;Container = {G2};Elements = {v4,v5,e45};InElements = {g2, g3};
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Structure and Topology SearchTarget chemical molecular structure (source PDB)
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Towards Second-Order Informatics: Structure and Topology Search
The structure is eliminated
Two of the resulting chemical molecular structures (source PDB)
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Towards Second-Order Informatics: Meta-Architecture - Seeding the Design Process Reasoning about the Structure
Both interactions and
components are
First Class Objects
«metaclass»mClass
«metaclass»Agregation
«metaclass»mComponent
1
*
1 *
«metaclass»mActor
«metaclass»mRole
«metaclass»Communication
«metaclass»Association
1
0..1
1
1..*
* 1..*
* 1..*
STRUCTURE: Generalisation together with Agregationintroduce composition over the set of
instances of the mComponent.
«metaclass»Generalisation
1
*
1
1
Generalisation is representedat the lower meta level (level 3)by the same symbol (an arrow)
as in UML class diagrams.
The Communication is represnetedat the lower meta level (level 3)
by the same symbol that representsbinary association class in UML diagrams .
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Intelligent Information System: Client-Server Interaction
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Intelligent Information System: Self-Organizing System Architecture
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Intelligent Information System: Interaction between two systems U
sabi
lity
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Intelligent Information System: Community building and Ladder of reflectionIIS Concept
Metamodel
CoreOntology
"RE
FL
EC
TIV
E S
YS
TE
M"
IIS Domain SpecificInstance
Systeminstance
DomainOntology
Application domain instances(different aspects of the
reflective system)
NON-DENOMINATIONAL
DESIGN
REFLECTIVE
SOFTWARE
ARTIFACTS
VIRTUAL
ENTERPRISE
BUSINESS
MODEL
DEVELOPMENT
COLLABORATION
SOCIAL
COMPUTING
COMPETENCY
MODEL
COLLABORATIVE
CONFLICT
MECHANISM
DESIGN
REFLECTIVE
SYSTEM
ARCHITECTURE
Methodology at meta level
Reflection-in-context
Reflectivepractitionerscommunity
"Broad" reflection
Reflection-in-context
Reflection-in-action"Deep" reflection
RepertoireRepository
Interaction Agent
Artifact
Practitioner
Business Plan
Web Service
Client-Server
Negotiation Dialog
Collaborative Conflict
IIS Architecture InstanceUsa
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Conclusions: Second-Order Informatics promises exponential growth of
value generated by CS
It is possible to efficiently search for new problems and application domains for existing solutions
Bioinformatics: Efficient application of meta-heuristics for automatic search for more efficient solutions for both existing and new problems
Bioinformatics: a systematic and formal approach to semantics of bioinformatics is possible
Infrastructure for synergy between CS and natural sciences, such as biology, chemistry, sociology, psychology, etc.
Large number of theoretical as well as practical problems open
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4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Questions?
Thank you!
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Through iterations:
Problems become more important
Our capability grow
Results are bigger
4/15/2005 M.Tosic, Intelligent Information Systems Lab., University of Nis
Talk by: Dr. Milorad Tosic, Faculty of Electronic Engineering, University of Nis
Title: Intelligent Information Systems: Second-Order Informatics for The Bioinformatics Challenge
Abstract: IT advancements are rapidly becoming leading force in human society development, where IT not only changes the way humans live and work but also suffers tremendous pressure to deliver tangible human-oriented value. The railway analogy may be appropriate because it spawned a speculative boom and bust in its early days. Each of the technologies had a potential that was almost beyond hype; the problems began with the social adoption of the technology, when people started to believe that anything to do with the technology was bound to make money. In fact, the phenomenon may be interpreted as a new technology driven discruption within a technology-intensive system, where the technology-intensive system, particularly software-intensive system, is a complex system, probabilistic in it’s nature, evolving essentially heterogeneous entities, such as technology, humans, interaction, knowledge, society, nature, behavior, beliefs, etc.
This talk will present grounding work on the intelligent information systems: an umbrella paradigm covering second-order informatics (i.e. meta-informatics, informatics-about-informatics) particularly important for dealing with the software-intensive systems. The emerging framework enables us to exercise bioinformatics (but also management, economics, finance, social systems, etc.) within the context of software-intensive systems. As an illustrative benefit, we are able to identify some important bioinformatics challenges steaming from it’s multi-disciplinary nature as well as high complexity of the target problems. Also, some of the solutions developed within the intelligent information systems framework appear very promising when applied on the identified bioinformatics challenges. Note that most of the presented ideas are still in the infancy and the presentation is not intended to constitute a tutorial. Instead, it should be considered as a communication medium for diverse scientific communities, particularly useful for the bioinformatics community.
Speaker Bio Sketch: Milorad Tosic is an assistant professor at the Faculty of Electronic Engineering, University of Nis, Serbia. He received the PhD degree in computer engineering from University of Nis in 1998. He was visiting scientist associated with PDB group at Rutgers University, NJ for three years. His research focuses on design methodologies for interactive systems, particularly aspects of cross-domain system models, semantics, concurrency, heuristics and meta-architectures. In particular, he is interested in applications for science of design, bioinformatics, collaborative systems, knowledge management, semantic web, multi-agent systems, distributed management, middleware, and networks.