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IT-Department Åbo Akademi University

IT-Department Åbo Akademi University. Presentation for Academy of Finland evaluation Barbro Back Department chair. IT in Turku. ICT-house. TUCS. Turku School of Economics/ IS. Åbo Akademi University / IT. University of Turku /Math. University of Turku / IT. Turku

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IT-Department Åbo Akademi University

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  1. IT-DepartmentÅbo Akademi University Presentation for Academy of Finland evaluation Barbro Back Department chair

  2. IT in Turku ICT-house TUCS Turku School of Economics/ IS Åbo Akademi University / IT University of Turku /Math University of Turku / IT Turku Polytechnic /IT

  3. IT-department • New Faculty of Technology at Åbo Akademi 2006 -- • Two departments • IT (Information technology) • KT (Chemical engineering) • IT previously spread out over three different faculties • MNF, KTF, ESF • Present personnel • Teaching appr. 30 • 8 professors, 10 Lecturers,11 others • Administration, technical support 9 • Research 60 – 80 persons

  4. IT-department (cont.) • Students and Degrees • Approx. 600 (masters and Ph.D. students) • 60 - 70 new students and • 10-12 new doctoral students enrolled each year • Financial volume 2006 • Internal funding : 2 Meuros • External funding : 1.8 Meuros • Output • 164 publications in 2006 • 30 in refereed journals • 77 in refereed conference publications • 8 Doctoral theses in 2006 • 53 MSc theses in 2006

  5. IT at Åbo Akademi 2006- Åbo Akademi Faculty of Technology IT department Department of Chemical Engineering CREST 45 IAMSR 35 Research groups 130 20

  6. CREST • Center for Reliable Software Technology • Leader: Academy professor Ralph-Johan Back • Focus: how to model, construct and analyze large reliable software intensive systems • Distributed systems lab: Kaisa Sere + 15 researchers • Embedded systems lab: Johan Lilius + 15 • Software construction lab: Ralph Back, Ivan Porres + 10 • Learning and reasoning lab: Ralph Back, Tapio Salakoski (UTU)+ 5 • Gaudi software factory: Ralph Back, Ivan Porres (in summer 20 programmers)

  7. IAMSR • Institute for Advanced Management Studies • Leader:professor Christer Carlsson • Focus: Combines topics from management science and information systems • Soft Computing: Christer Carlsson + 25 • fuzzy logic, fuzzy optimization, real options and fuzzy real options modeling • intelligent MAS for industry foresight • risk assessment in grid computing • advanced logistics planning and optimisation • Mobile technologies applications: Pirkko Walden + 10 • knowledge mobilization and mobile value services and applications • IAMSR was on shortlist for CoE 2008 - 2013

  8. Other research labs and groups • Computational Biomodeling lab: Ion Petre + 6 researchers • Data Mining and Knowledge Management lab: Barbro Back + 6 • High-Performance Computing lab: Jan Westerholm + 4 • Process Control lab: Hannu Toivonen + 4

  9. Linas Laibinis (J. von Wright) Martin Büchi (R.Back) Elena Troubitsyna (K.Sere) Shuhua Liu (C.Carlsson) Ivan Porres (R.Back) Mauno Rönkkö (K.Sere) Rainer Jansson (C.Carlsson) Vladimir Kvassov (C.Carlsson) Franck Tétard (C.Carlsson) Bill Anckar (P. Walden) Antonina Kloptchenko (B.Back) Eija Koskivaara, TSE (B.Back) Péter Majlender (C.Carlsson) Otieno Mbare (C.Carlsson) Mikael Collan (C.Carlsson) Tomas Eklund (B.Back) Aapo Länsiluoto, TSE (B.Back) Rimvydas Ruksenas (K.Sere) Seppo Wirtanen, UTU (J.Lilius) Dag Björklund (J.Lilius) Cristina Seceleanu (R.Back) Luigia Petre ((K.Sere, R.Back) Lu Yan (K.Sere) Shengnan Han (P. Walden) Jukka Arvo, UTU, (J. Westerholm) Irina Georgescu (C.Carlsson) Adrian Costea (B.Back) Orieta Celiku (J. von Wright) Viorel Preoteasa (R.Back) Luka Milanov (R.Back) Ville Harkke (P.Walden) Chihab BenMoussa (C.Carlsson) Kaj-Mikael Björk (C.Carlsson) Francisco Augusto Alcaraz Garcia (C.Carlsson) Erkki Patokorpi (P. Walden) Dragos Truscan (J.Lilius) Markus Alanen (I.Porres) Ph.D. theses 2000-2007

  10. Research highlights • Short presentation of main research activities • Focus on Computer Science and Engineering • Can only present a few research highlights • Many excellent projects going on • Answer questions after presenting research highlights

  11. CREST backgroundRalph Back • Refinement calculus 1978 – (Ralph Back) • Action systems 1982 – (Ralph Back, Reino Kurki-Suonio) • Programming methods group at Åbo Akademi 1984 - • Refinement calculus development (Back, von Wright) • Action System development (Back, Sere) • PMG splits up into five research labs 2002 • Four labs form Crest • High performance computing lab, Westerholm • Crest Center of Excellence in Research 2002 – 2007 • Crest was on shortlist for CoE 2008 - 2013 • Reviews were excellent, but competition very strong

  12. Teaching mathematics in high schoolRalph Back TapioSalakoski

  13. Structured derivations in high school • Structured derivations: • Based on Dijkstra’s calculational style for proofs • Extended to complete logical system based on sequent calculus (Back and von Wright: Refinement calculus, Springer 1998) • Validated in Kupittaa high school pilot study • 3 year study (test group using structured derivations, control group using standard approach). All courses in high school curriculum covered • Test group clearly and consistently outperformed control group • Pilot projects in a number of other high schools schools • Supported by large course development and teacher training grant • New book: Mathematics with a little bit of logic • Back and von Wright • Presents basic approach as a high school course, lots of examples

  14. Structured derivations example Solve equation |x-1|+|2x-y|=0 |x-1|+|2x-y|=0 {property of absolute values} x-1=0  2x-y=0 {add 1 to both sides of left equation} x=1  2x-y=0 {simplify right conjunct} [x=1] 2x-y=0 {substitute x=1 by assumption} 2-y=0 {solve} y=2 x=1  y=2 motivate each step, on separate line logical notation in derivation nested derivation assumptions have scope collapsabel outlines Publish on web

  15. Invariant based programmingRalph Back

  16. Invariant based programming • Constructing programs and correctness proofs together • Based on early work by Back and early TR by John Reynolds • Start by formulating the invariants of the system, then transitions between invariants, and then verify correctness of transitions • Formal methods light • Easy to learn, easy to use in practice • Have carried out a number of sessions teaching both novices and experienced programmers how to construct verified programs • Now teaching approach to first year CS students at Åbo Akademi • Socos environment provides computer support for method • Drawing diagrams, proving verification conditions automatically, generating executable code, testing and debugging

  17. Nested invariant diagrams Sort array A A: array 1..n of integer; Permutation(A,A0); 1 <= n Organize program in nested invariants i: integer; 1 <= k <= n; Sorted(A,1,i-1), Partitioned(A,i) Transitions between invariants: [guard] assignments i:= 1 [i=/=n] j,k:=1,1 j,k: integer; A[k]=min(A,i,j); k < n; 1 <= k <= j <= n [j=/=n] Formulate invariants before adding transitions [A[j] >= A[k] j:=j+1 [A[j] < A[k]; k:= j Verify correctness of program as it is being constructed [j=n] i:=i+1 A[i],A[k]:=A[k],A[i] High degree of locality in constructing, modifying and verifying program [i= n] Sorted(A,1,n)

  18. Gaudi Software factoryRalph Back Ivan Porres

  19. Research Laboratories Develop tools as demonstrators Software Process Improvement Sandbox Improve courses on Software Engineering Industry Education Software Experience and Software Development Factory Experience Factory Development Factory

  20. Research focus • Industrial projects • Software Process Improvement Efforts • Medium-term projects • Software Development and Experience Factory Setting • Agile and team development processes • Long-term research • Incremental software development & software reuse • Software architectures • Language mechanisms • Domain-specific modeling languages

  21. Distributed Systems DesignKaisa Sere, Marina Walden, Elena Troubitsyna

  22. System approach - from theory to practice • System approach • A system or a computer-based product is first modeled as one entity on a very abstract level. • Thereafter, detail is introduced into the model in a systematic manner (e.g. using patterns). • When the model is detailed enough it is decomposed into meaningful parts or sub-models, e.g. environment, hardware and software. • Dependability: correctness (fault avoidance) , fault tolerance • Formalisms: Action systems, B Method, Event B • The approach gives structure to the design of complex distributed systems and helps in managing the derivation task.

  23. Long-term financing via EU projects • MATISSE (IST FP5 2000-03) • Methodologies and technologies for industrial strength systems engineering • developed a design methodology for critical controlsystems using formal methods • RODIN (IST FP6 STREP 2004-07) • Rigorous open development environment for complex systems • creation of a methodology and a supporting open toolplatform for the cost effective development of dependable complex software systems and services • DEPLOY (IST FP7 IP 2008-12) : • Industrialdeployment of advanced system engineering methods for high productivity and dependability • Status: hearings in Brussels tomorrow

  24. System approach collaborators • ETH/Z (Ch) • Jean-Raymond Abrial, David Basin • Univ. of Newcastle (UK) • Cliff Jones, Alexander Romanovsky • Univ. of Southampton (UK) • Michael Butler, Michael Leuschel • Industrial • Nokia (Fi), Siemens (Fr), PerkinElmer (US/Fi), Atec (UK), Praxis (UK), Gemplus (Fr), Qinetiq (UK), ClearSy (Fr)

  25. System approach results • 2 PhD theses 1995 - 1999 • Marina Waldén: Formal reasoning about distributed systems • Virpi Kasurinen: Informal and formal requirements specification • Program Development by Stepwise Refinement (E. Sekerinski, K. Sere (Eds.), Springer 1998) • 4 PhD theses 2000 - 2006 • Elena Troubitsyna: Stepwise development of dependable systems • Mauno Rönkkö: Stepwise development of hybrid systems • Lu Yan: Systematic design of ubiquitous systems • Luigia Petre: Modelling with action systems • 2 PhD theses will be evaluated summer 2007 • Pontus Boström, Dubravka Ilic

  26. Model Driven DevelopmentJohan Lilius Ivan Porres

  27. Model-Driven Software and System Development The goal of the Model Driven Development group at CREST is to study and develop new ways to build large software-intensive systems in practice using advanced modeling languages and tools Theory Tool Frameworks Applications

  28. MDD • Long term research goals: Understand methods for domain driven design,Understand the foundations (structure and algorithms)of domain driven design. • Results: • A methodological framework verified through 2 substantial case studies (PhD Dragos Truscan) • A framework for the semantics of DSL that enabled codegeneration (PhD Dag Björklund). • A framework for metamodelling (PhD Marcus Alanen)

  29. MDD (cont) • Middle term research goals: Implementation of theoretical results in tool frameworks • Results • CORAL - A tool to define, edit and transform visual modeling languages: Modeling language definition, Visual languages and editors, Model transformation, Model interchange • Rialto - A language for multiple models of computation: Flexible definition of behavioral semantics

  30. MDD(cont.) • Short term research goals: Verify our more theoretical results in practice, Apply the tool frameworks, provide feedback to middleand long term goals • Results • MICAS platform: Tool for definition of mobile phone subsystems • PECOS: Tools and methods for verification of non-functional constraints (energy) in mobile phone subsystems • Future topics • Model based testing • Dealing with non-functional constraints in system design • Version control

  31. Computational systems biologyIon Petre

  32. Models for gene assembly Linked lists in living cells! Invariants Complexity Parallelism Applications in genetic engineering Collaborators: Gregorz Rozenberg (CS,Holland) A.Ehrenfeucht (Math,US) D.M.Prescott (Biol,US) Tero Harju (Math,FIN) Gene assembly in ciliates

  33. Promoter Promoter Heat shock gene Heat shock gene MFP MFP MFP MFP MFP Heat shock response • The cells reaction to elevated temperature • Models • Continuous ODE models • Stochastic models • Collaborators: • Ralph Back(CS, FIN) • John Eriksson(Biol, FIN) • Lea Sistonen(Biol, FIN) • Rick Morimoto(Biol, US) 37C HSP HSP RNA pol HSP HSP HSP:HSF HSF 42C

  34. Thank You

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