Intelligent Information Systems: Second-Order Informatics for the Bioinformatics Challenge

1. Intelligent Information Systems: Second-Order Informatics for the Bioinformatics Challenge Dr Milorad Tošić Temple University, Center for IST, April 2005

2. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

3. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

4. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

5. 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. M.Tosic, Intelligent Information Systems Lab., University of Nis

6. 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. M.Tosic, Intelligent Information Systems Lab., University of Nis

7. Global challenge: Paradigm Shift • What the next Paradigm will be? • We do not know now!!! • We have to act under uncertainty!!! M.Tosic, Intelligent Information Systems Lab., University of Nis

8. 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) M.Tosic, Intelligent Information Systems Lab., University of Nis

9. Global challenge: Our answer First-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 M.Tosic, Intelligent Information Systems Lab., University of Nis

10. Methodology: ABC • The ABC Model of Organizational Improvement [D.C. Engelbart, “Toward High-Performance Organizations: A Strategic Role for Groupware”, 1992., www.bootstrape.org] M.Tosic, Intelligent Information Systems Lab., University of Nis

11. Methodology: Agile Methodology: • Work in presence of uncertainty Reflective Practice: • “The thing that make us smart” (what people and computers can do together?) [Fisher, 2001] M.Tosic, Intelligent Information Systems Lab., University of Nis

12. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

13. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

14. 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), M.Tosic, Intelligent Information Systems Lab., University of Nis

15. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

16. 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) M.Tosic, Intelligent Information Systems Lab., University of Nis

17. 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. M.Tosic, Intelligent Information Systems Lab., University of Nis

18. Towards Second-Order Informatics: Architecture & System • Behavior and protocol define the system’s dynamics. M.Tosic, Intelligent Information Systems Lab., University of Nis

19. Towards Second-Order Informatics: Architecture & System • Model of the language Lrepresents 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. M.Tosic, Intelligent Information Systems Lab., University of Nis

20. Towards Second-Order Informatics: Architecture & System • Ontologies: • Coarse-Grain Ontology: Common-ground knowledge • Fine-Grain Ontology: Context-specific knowledge M.Tosic, Intelligent Information Systems Lab., University of Nis

21. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

22. Towards Second-Order Informatics: Structure as SHG Definition:A hyper-graphHG 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-graphHG 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 relationis an equivalence relation. M.Tosic, Intelligent Information Systems Lab., University of Nis

23. Towards Second-Order Informatics: Structure as SHG Definition:An directed hyper-graphHG 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 relationis an order relation. M.Tosic, Intelligent Information Systems Lab., University of Nis

24. Towards Second-Order Informatics: Structure as the SHG Example v1 v1: v2: v3 e12 id = v1; type = VERTEX; Container = {G1}; Elements = {}; InElements = {e12}; id = v2; type = VERTEX; Container = {G1}; Elements = {}; InElements = {e12, e23, e24}; e35 e23 v5 v2 e24 e57 e45 v7 v4 . . . e46 e67 v6 e68 v8 e12: e23: id = e12; type = EDGE; Container = {G1}; Elements = {}; InElements = {v1,v2}; id = e23; type = EDGE; Container = {G1}; Elements = {}; InElements = {v2, v3}; G1: id = G1; type = GRAPH; Container = {}; Elements = {v1, … , v8, e12, e23, … ,e68}; InElements = {}; . . . M.Tosic, Intelligent Information Systems Lab., University of Nis

25. Towards Second-Order Informatics: Structure as the SHG Example v3 v5 v1 e35 e57 e23 e45 v6 e12 v5 v7 v2 v4 e68 e24 e46 e45 e67 v8 v2 v4 v6 g1 g2 g3 g4 G2: e1 e2 e3 e1: id = G2; type = GRAPH; Container = {}; Elements = {g1,g2,g3,g4, e1,e2,e3,e4}; InElements = {}; id = e1; type = EDGE; Container = {G2}; Elements = {v2}; InElements = {g1,g2}; g1: g2: e2: id = g1; type = GRAPH; Container = {G2}; Elements = {v1,v2,e12}; InElements = {e1}; id = g2; type = LOOP; Container = {G2}; Elements = {v2,v3,v4,v5,e23,e24,e35,e45}; InElements = {e1, e2}; id = e2; type = EDGE; Container = {G2}; Elements = {v4,v5,e45}; InElements = {g2, g3}; M.Tosic, Intelligent Information Systems Lab., University of Nis

26. Towards Second-Order Informatics: Structure and Topology Search Target chemical molecular structure (source PDB) M.Tosic, Intelligent Information Systems Lab., University of Nis

27. Towards Second-Order Informatics: Structure and Topology Search Two of the resulting chemical molecular structures (source PDB) The structure is eliminated M.Tosic, Intelligent Information Systems Lab., University of Nis

28. Towards Second-Order Informatics: Meta-Architecture - Seeding the Design Process • Reasoning about the Structure • Both interactions and components are First Class Objects M.Tosic, Intelligent Information Systems Lab., University of Nis

29. Intelligent Information System: Client-Server Interaction M.Tosic, Intelligent Information Systems Lab., University of Nis

30. Intelligent Information System: Self-Organizing System Architecture M.Tosic, Intelligent Information Systems Lab., University of Nis

31. Intelligent Information System: Interaction between two systems M.Tosic, Intelligent Information Systems Lab., University of Nis

32. Intelligent Information System: Community building and Ladder of reflection M.Tosic, Intelligent Information Systems Lab., University of Nis

33. 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 M.Tosic, Intelligent Information Systems Lab., University of Nis

34. Questions? Thank you! Through iterations: • Problems become more important • Our capability grow • Results are bigger M.Tosic, Intelligent Information Systems Lab., University of Nis

35. 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. M.Tosic, Intelligent Information Systems Lab., University of Nis