following the traces of oadymppac …

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Chip User's Club, Paris 16/10/2008 1

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 alrorithms to recognize objects in the trace (identification of observables)

Trace query anguage : efficient filtering

To trace knowledge use and managment

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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:

Analyse 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: digitalisation/numerisation (sensorial), remanent and support (temporal line), conscient recall(abstract)

Towards a memory protese?

•“base dofe stances” (Kiss, Quinqueton 2004)

•Mechanisms for deduction and for recell (LISFS, logical information system, Padiolo, Sogonneau, Ridoux 2004)

•mechanisms for data organisation (ontology's) et to forget

Personal memory organisation system

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6- Epistemology as theory of knowledges

Discretization-spatialisation/numérisation/manipulation

Big steps:

•20 centuries after beginning of néolitic: first numerations (astro)

•12th century BC alphabetic system (“grammatisation”)

•The printing allows the writing to invade society

•17th century, the machine tool is the reproduction of discretized gesture

•1834 discretisation 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?

following the traces of oadymppac …

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