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Following the traces of OADymPPac …
The OADymPPaC project (Tools for Dynamic Analysis and Debugging of Constraint Programs), whose COSYTEC was a partner, officially ended in 2004. One of the objective was generic trace definition. Focused by short delays on tracer prototypes realisation and trace analysis, the project did not go deep in the problem of trace creation and management. We invite you here to follow the path initiated by OADymPPaC, until some epistemological ultimate consequences.
Pierre Deransart CR INRIA-Paris-Rocquencourt
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Budget: 162 M€ (tax not incl.) including 20% from contracts, software licenses, etc.
2,900 scientists1,000 doctoral candidates 450 post-docs 300 R&D engineers
1,500 budgetary positions570 research scientists740 ETA
300 interns
8 Research Centres (2008)
A workforce of 3,700Key figuresJan. 2007
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Location
External research project-team
Research Centre
INRIA NancyGrand Est
INRIA Grenoble Rhône-Alpes
INRIA Sophia AntipolisMéditerranée
INRIA RennesBretagne Atlantique
INRIA BordeauxSud-Ouest
Metz
INRIA LilleNord Europe
Lannion
Marseille
Lyon
Montpellier
INRIASaclayÎle-de-France
Headquarters
Nantes
Besançon
StrasbourgINRIA ParisRocquencourt
Pau
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Nancy - Grand Est
Paris - Rocquencourt
Sophia Antipolis - Méditerranée
Rennes - Bretagne Atlantique
Grenoble - Rhône-Alpes
The Research Centres
Bordeaux - Sud-Ouest
Saclay - Île-de-France
Lille - Nord Europe
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Try to remember….
« Sur la route de Rocquencourt » par Pissaro …
2004, so long time ago….
1999
2004
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CONTEXT, HISTORYFrom DiSCiPl to OADymPPaC
•DiSCiPl (1997-2000): to improve debogage of constraint solvers: resulted in prototypes which remained « ad-hoc » ones for correctness and performance analysis. This project has shown the usefulness of semi-automated approaches based on trace analysis by visualization tools.
Book: P. Deransart and M. Hermenegildo and J. Maluszynski, Analysis and Visualization Tools for Constraint Programming, LNCS 1870, 2000
•OADymPPaC (2001-2004) URL: http://contraintes.inria.fr/OADymPPaC participants: A. Aggoun, T. Baudel, P. Deransart, M. Ducassé, F.Fages, J.D. Fekete, N. Jussien, C. de Sainte-Marie, …
Challenges :• Interoperability of the tools : complete separation between trace production
and trace analysis, studied and realized by different specialists
• scaling: possibilité possibility to consider thausends of variables and constraints using specialized HMI
The project resulted in prototypes et products, but limited to the constraint resolution domain. Several problems have been identified.
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PROBLEMS Can such approach be extended to other domains?
1.Trace interpretation: to give a meaning to a trace, reconstruction models (trace analysis, interpretative semantics IS).
2.Sémantics of the traces of a given family of processes (trace generation model, observational semantics OS).
3.Data stream managment between the observed and observing processes: trace filtering, tracer driver, workload balance, interactions, properties of the stream (efficiency, invariance of the semantics, faithfulness)
Relationships with other research and application domains: event-condition-action models, data stream analysis, cognitive sciences
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The two traces of a process(Full Traces)
•Virtual Trace TV = <S0,et*> SO
•Actual Trace TA = <S0,wt*> SI
TV
TA
IE
E: extraction
I: interpretation (reconstruction)
E ° I = I ° E = i
Notion of FAITHFULNESS:
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Small example (extract of Prolog trace)
goal:-p(X),eq(X,b).
p(a).
p(b).
eq(X,X).
chrono nu(u) lp(u) port pd(u) Reached virtual state
1 1 1 Call goal S2
2 2 2 Call p(X) S3
3 2 2 Exit p(a) S4
4 3 2 Call eq(a,b) S5
5 3 2 Fail eq(a,b) S6
6 2 2 Redo p(a) S7
7 2 2 Exit p(b) S8
8 4 2 Call eq(b,b) S9
9 4 2 Exit eq(b,b) S10
10 1 1 Exit goal S11
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But if the trace is smaller …?
nu(u) port
1 Call
2 Call
2 Exit
3 Call
3 Fail
2 Redo
2 Exit
4 Call
4 Exit
1 Exit
What can we see ?
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PLAN: domains and “challenges”
1. Tracer development for dynamic analysis of programs
2. Modelling and abstraction
3. Data mining et data stream filtering, ECA models and WEB semantics
4. Analysis of human behaviour
5. Brain, memory prothesis
6. Epistemology
Idea: traces are everywhere, investigating on traces, is investigating on ideas too
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Everywhere ?
http://www.college-de-france.fr/default/EN/all/ger_ber/index.htm
GérardBerry2007
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1- Tracer Construction for dynamic analysis of programs
1. Incremental development of tracer (full trace)
2. Trace filtering and query (language for trace events selection), Tracer Driver
3. Interactions (server tracer / clients analysers)
4. Optimization of the communication (with faithfulness)
5. MDA Approach «trace componants» (enrichment, fusion, abstraction, selection)
6. Genericity
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C4RBCP
TchromeV
TCHRV
TRslamV
TV
And
querying
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Grand Challenge 1: conception et manipulation of traces (« traces algebra»)
Enrichment
Selection
Fusion
Abstraction
Genericity
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2-Modelling and abstraction
Full TraceIntricated abstractions levels
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Modelling and abstraction (genericity)
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Grand Challenge 2: models of trace production (SO)
Abstract interpretation give a possible theoretical framework for the OS
possible use of « Fluent calculus »
Tracer implementation, simulations and verifications are possible for a domain of processes (« model checking », Clarke, Emerson, Sifakis, Turing 2007)
Theoretical trace Analysis (relationships with trace theory, The Book of Traces, 1995),
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3-Data mining and interogation of data stream, semantic WEB, ECA Models
ADSL traffic
Looking for a meaning…
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Data mining as trace analysis
Using data stream analysis algorithms in order to recognize objects (optimized traces)
•Selection of suspicious portions of programs (Zaidman & al, 2005)
•« model checking » for intrusion detection (Garavel & al. 2004) on execution traces
•Symmetries discovery
(OADymPPaC)
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Data stream analysis with unknown origin
Massive flow analysis (probabilistic algorithms, Rabin 1980)
Query langages of data flows (Arasu, 2002)
Interactions between observer/observed and between traces (ECA models and WEB semantics, Alferes et al. 2004)
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Grand Challenge 3: to interpret traces (IS)
Using data stream analysis algorithms to recognize objects in the trace (identification of observables)
Trace query language : efficient filtering
To trace knowledge use and management
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4-Human behaviour Analysis
To trust the data
Formalisation of contexts (data fusion), traces of contexts and human behaviour
Construction of scenarios from traces
Till were can we or shall we go?
Go around the limits af automation
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Trusting data (access to the knowledge)
Knowledge base = rule system = computations
Using it require more than just computations: seeking, identifying, reasoning (without predefined strategy)
Example: problem internet sites guarantees (ex law of 13 août 2004 on “ la certification des sites internet dédiés à la santé” (Haute Autorité à la Santé))
HON code (Health On the Net): ex
•Qualification of writers
•Justification of affirmations
•Clear distinction between edited contents and advertisement
•Transparency of founding
•Personal data protection, keeping traces on consultations or updates
•…
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TRACESTRACES
Construction od a virtual world (Lyon1/INRETS)
Virtual Trace
Actual Trace
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An “Infernal” ExampleLe Monde de l'Intellligence, num 11 janv-fev-mars 08 Sudoku infernal p 60 (par Bernard Gervais)
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Experimentation
To build and compare traces of the player’s behaviour and of the automaton
•D’analyser le comportement du joueur
•D’identifier la règle utilisée par le joueur
•De mesurer la satisfaction du joueur
•De comparer avec la résolution automatique
•D’identifier les points de réelle difficulté du joueur
•De corréler de la difficulté pour le joueur et la difficulté théorique
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Grand Challenge 4:
Analyze of a knowledge domain
Construction of scenarios
Limits of the formalization (beginning of the “human” work)
The ability to build “good” traces Is mandatory in order to perform “good” analysis
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5-Brain: a world of fusions
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Actual Trace Twt = <(S0,)wt*>
Unbounded sequence of trace events wt
wt : (t, At)
•t : chrono: time of the trace
•At: set of attributes values
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La mémoire
Personal Memory Project: Memex (Vannevar Bush, 1945)
Accumulation of trace events (multimedia)
-------------------------------
Mechanisms of the human memory:
Axes (Chapoutier, 2006):
•Sensations
•Temporal (work, episodic / reference, sustainable)
•Abstract (procedural memory and implicit memory )
each memory has its recall mode (implicit, inconscient / explicit, conscient)
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Gand Challenge 5: personal memory artefact
Axes: digitalization/numerisation (sensorial), remanent and support (temporal line), conscient recall(abstract)
Towards a memory protese?
•“base of stances” (Kiss, Quinqueton 2004)
•Mechanisms for deduction and for recell (LISFS, logical information system, Padiolo, Sogonneau, Ridoux 2004)
•mechanisms for data organization (ontology's) et to forget
Personal memory organization system
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6- Epistemology as theory of knowledges
Discretization-spatialisation/numerisation/manipulation
Big steps:
•20 centuries after beginning of néolitic: first numerations (astro)
•12th century BC alphabetic system (“grammatization”)
•The printing allows the writing to invade society
•17th century, the machine tool is the reproduction of discretized gesture
•1834 discretization sounds and images
•Economy of immaterial (management of knowledges)
Information processing plays a dominant role in all spheres of activity (industry or research) and is based on an uninterrupted accumulation of traces ….
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Grand Challenge 6: society viewed as a system of interacting traces
http://www.inria.fr/40ans/forum/video.fr.php
Le réseau numérique, à l'origine d'un nouveau modèle industriel Conférence de Bernard Stiegler
Les nouvelles technologies : révolution culturelle et cognitive Conférence de Michel Serres
etc…
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Following the traces of ….
Thank you!Jusqu’où ne risque-t-on pas d’aller trop loin?L’homme réinventé?