Second-Order Informatics: Architecture, Semantics and Applications

1. Second-Order Informatics: Architecture, Semantics and Applications Dr Milorad Tošić University College of London, January 2007

2. Content: • Global Challenge • Problem statement: Evidence, Consequences and Opportunities • The Example of Bioinformatics and CS • Proposed solution: Second-Order Informatics • Methodology • Theoretical Foundations of Second-Order Informatics: A Systems Approach • Prototypes • Conclusion M.Tosic, Intelligent Information Systems Lab., University of Nis

3. Content: • Global Challenge • Proposed solution: Second-Order Informatics – first class entities • Methodology • Interaction • Model of a Software-Intensive System • Meta-Architecture • Self-Organizing System Architecture • Methodology • Theoretical Foundations of Second-Order Informatics: A Systems Approach • Prototypes • Conclusion M.Tosic, Intelligent Information Systems Lab., University of Nis

4. Content: • Global Challenge • Proposed solution: Second-Order Informatics • Methodology • ABC methodology • Agile methodologies • Theoretical Foundations of Second-Order Informatics: A Systems Approach • Prototypes • Conclusion M.Tosic, Intelligent Information Systems Lab., University of Nis

5. Content: • Global Challenge • Proposed solution: Second-Order Informatics • Methodology • Theoretical Foundations of 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 • Prototypes • Conclusion M.Tosic, Intelligent Information Systems Lab., University of Nis

6. Content: • Global Challenge • Proposed solution: Second-Order Informatics • Methodology • Theoretical Foundations of Second-Order Informatics: A Systems Approach • Prototypes • Wiki as a Collaborative Semantic Community Portal • Collaborative Tagging System • Conclusion M.Tosic, Intelligent Information Systems Lab., University of Nis

7. Relevant Resources • Technologies • Modeling (UML, MOF, MDA) • Web 2.0, Collaborative Tagging, Wiki • Semantic Web and Ontologies • Component-Based Systems • Service Oriented Architecture (SOA) • Concepts • Software Intensive Complex Systems • Distributed Knowledge Systems • Emergent Semantics • Semiotic Dynamics • Prediction Markets • Small-world and Scale-free networks • “Long tail” • Metaheuristics M.Tosic, Intelligent Information Systems Lab., University of Nis

8. 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 domains • Bioinformatics • e-Learning • Web-based Global Intelligent Systems • Google • Flickr.com, del.icio.us, Connotea.com, • YouTube.com, DiggIt M.Tosic, Intelligent Information Systems Lab., University of Nis

9. Global challenge: Problem Statement • Consequences of the disruption • “Research as usual” is not enough anymore • Large budget cuts for NSF in US continue • Strong bias towards commercially promising projects in FP6 • Under-funded research projects suffer increasing pressure for more results!? • “Semantic spam” • Growth rate of the amount of data available about any single topic is huge! • Even peer-reviewed content generation (papers, conferences, books, ..) becoming hard to follow • “Browsing vs. Search” in research and science • High specialization • Required to be able to keep up and produce high-quality research results • Extremely high risk: Due to a close relationship with commercial markets M.Tosic, Intelligent Information Systems Lab., University of Nis

10. Global challenge: Problem Statement • Opportunities opened by the disruption • Research at global scale • Develop infrastructure to unleash the power of collective human intelligence scalable to whole humanity. • Semantic communities • Increasing accumulation rate of the community knowledge • Continuous interactions within community not only by means of published papers, conferences, and other traditional tools • Collaborative filtering of information within the community • High specialization • Research results are not limited by a single application domain • Very short closed loop between scientific research and commercial markets • Unleash the power of collective human intelligence scalable to whole humanity. M.Tosic, Intelligent Information Systems Lab., University of Nis

11. Global challenge: The Example of Bioinformatics and CS [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

12. Global challenge: Proposed solution Second-Order Informatics: Introduction • Object of a research is research itself • Can we design software-intensive system based on humans collaborating with software agents such that the system outperforms human researchers? • How do I scale my research competence? • Scale by numbers? • Scale by application domains? • How do I (as a researcher) compete width: • A person using Wikipedia? • A community of researchers of lesser excellence then my • Outsourcing? • Can I benefit from outsourcing and how? • Can they outsource my job too? M.Tosic, Intelligent Information Systems Lab., University of Nis

13. Global challenge: Proposed solution Second-Order Informatics: Strategy • Select and adopt appropriate methodological framework • Develop strong theoretical background • Using the theory, design (meta) models at multiple semantic and cognitive tiers • Develop prototypes for specific application domains • Scale prototypes to a large number of different application domains • Scale prototypes globally, by markets as well as number of users M.Tosic, Intelligent Information Systems Lab., University of Nis

14. Global challenge: Proposed solution Second-Order Informatics: First-Class Entities • Methodology • ABC and Agile methodologies (work in presence of uncertainty) • Interaction • Model of a Software-Intensive System • Intelligent Information System (model aware system) • Semantics as the enabling driver (knowledge aware system) • Application Domain Transparency • Meta-Architecture • Self-Organizing System Architecture M.Tosic, Intelligent Information Systems Lab., University of Nis

15. 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

16. 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

17. First-Order Informatics: System Model • Intuitive, informal definition • Set of components cause change in the environment. • The actions are communicated 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

18. 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

19. 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

20. 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

21. Towards Second-Order Informatics: Architecture & System • Observable (metadata) is attached to the data (data has a meaning now) only through local interaction between components of the system (including observer (s)) that is about to recognize the meaning. • Observer is considered as one of the interacting components within the system M.Tosic, Intelligent Information Systems Lab., University of Nis

22. 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

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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

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

32. 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

33. 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

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

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

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

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

38. Wiki Prototype: Wiki-based Collaborative Semantic Web Portal • Collaborative workspace that brings together • People • Relevant information, • Knowledge, • Interaction, • Innovative methodologies, and • Supporting Tools M.Tosic, Intelligent Information Systems Lab., University of Nis

39. Wiki Prototype: Wiki-based Collaborative Semantic Web Portal • Prototype: • Inter- as well as intra-community collaboration, • Workflow and Process management, • Interaction, • Knowledge sharing and dissemination, • Heterogeneous information integration, • Awareness building. • Mechanisms: • System login and working groups, • Interaction over content, • Interaction over structure, • Interaction over presentation semantics, M.Tosic, Intelligent Information Systems Lab., University of Nis

40. Wiki Prototype: System login and working groups Not in Work Group? You don’t have privileges to VIEW and EDIT this page content • Permissions for users and working groups: • VIEW page content • EDIT page content • PRINT page content • CREATE new page • ATTACHMENTS per page M.Tosic, Intelligent Information Systems Lab., University of Nis

41. Menu creating and editing Page attachments as documents and pictures Page and content creating and editing Different ways of the content printing M.Tosic, Intelligent Information Systems Lab., University of Nis

42. Wiki Prototype: Interaction over content - Collaborative page editing M.Tosic, Intelligent Information Systems Lab., University of Nis

43. Wiki Prototype: Interaction over content - Printing • Pretty printing • Pure text printing • PDF printing • MS Word printing M.Tosic, Intelligent Information Systems Lab., University of Nis

44. Wiki Prototype: Interaction over structure - Automatic Links • Automatic links for page neighborhood (links pointing to the page and links pointing from the page) • Useful for drop-down menus within main menu as well as page-specific menus reflecting current context M.Tosic, Intelligent Information Systems Lab., University of Nis

45. Wiki Prototype: Interaction over presentation semantics • Importing (parts of) internal and/or external web pages into page content M.Tosic, Intelligent Information Systems Lab., University of Nis

46. Wiki Prototype: Our experience so far • Education • Student-teacher communication is improved • Students are more active on the projects • Management of the course is more natural • Students like this approach • Agile project management • Interactive distributed meeting minutes administration • Project knowledge accumulation • Project members have location-independent access to shared project’s documentation over the Web • Automatic e-mail notification about changes in the shared workspace M.Tosic, Intelligent Information Systems Lab., University of Nis

47. Collaborative Tagging Prototype: The theory • What do we do when we tag? • Assign one or more words, called tags, to some Web resource, usually Web page, or picture, or … • We are doing some mental work, work on semantics • Is our mental process when we tag different then when we search, browse, blog, program or talk over the phone? • No, technological details make the only difference? M.Tosic, Intelligent Information Systems Lab., University of Nis

48. Collaborative Tagging Prototype: A Screenshot M.Tosic, Intelligent Information Systems Lab., University of Nis

49. 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

50. Faculty of Electronic Engineering Second-Order Informatics: Architecture, Semantics and Applications Department of Computer Science Intelligent Information Systems Lab Thank you! Questions? mbtosic@yahoo.comhttp://infosys1.elfak.ni.ac.yu M.Tosic, Intelligent Information Systems Lab., University of Nis