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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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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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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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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http://abyss.uoregon.edu/~js/glossary/paradigm.html

http://abyss.uoregon.edu/~js/glossary/paradigm.html


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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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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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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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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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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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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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


action(change)

protocol

interaction structure

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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),


action(change)

protocol


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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



action(change)

protocol

contextknowledgeobservable

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SystemArchitecture



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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SystemArchitecture



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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System'sDinamics

SystemArchitecture



action(change)

protocol

Towards Second-Order Informatics: Architecture & System Behavior and protocol define the

system’s dynamics.


behavior

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System'sDinamics

SystemArchitecture



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.


behavior

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System'sDinamics

Fine-Grain Ontology

Coarse-Grain OntologySystem

Architecture



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


behavior

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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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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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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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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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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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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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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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Intelligent Information System: Client-Server Interaction

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Intelligent Information System: Self-Organizing System Architecture

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Intelligent Information System: Interaction between two systems U

sabi

lity

refle

ctio

n

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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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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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Questions?

Thank you!

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Through iterations:

Problems become more important

Our capability grow

Results are bigger


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.

intelligent information systems: second-order informatics for the bioinformatics challenge

Documents

paradigm shiftwhat

old paradigm

prevailing paradigm

learningglobal challenge

synergy of cs

effort cs people

systems structure

computer science