Kdd Process Design in Collaborative and Distributed Environments

Advisor: Prof.ssa Claudia DiamantiniCurriculum supervisor: Prof. Sauro Longhi

Università Politecnica delle MarcheScuola di dottorato in Scienze dell’IngegneriaCurriculum in Ingegneria Informatica, Gestionale e dell’Automazione

28 Febbraio 2012

KDD Process Design in Collaborative and Distributed Environments

Emanuele Storti

Emanuele Storti04/14/23 KDD Process Design in Collaborative and Distributed Environments

Summary

I. Introduction• Background & Motivation• Related Work• Research Question

II. ApproachIII. Knowledge LayerIV. Platform

• Service Discovery• Process Composition• Collaboration features• Formal Verification of processes

V. Conclusion


Introduction

“Knowledge is the common wealth of humanity”*

Access to vast collections of data is the key to understanding and responding to complex problems

* A. Samassekou, UN World Summit on the Information Society

Growing capability in data production: Data Deluge• availability of cost-effective technologies• enhancement of communication infrastructures

• global problems ask for data-intensive computation (genomics, physics, climatology)• global effort through world-wide scientific collaboration

Need of tools for supporting distribution and collaboration in data analysis

Not only organizations, also e-Science

1


Background

Knowledge Discovery in Databases (KDD)Process of identifying valid, novel, potentially useful patterns in data [Fayyad, 1996]

• knowledge refinement• many steps, several iterations• strong user interaction • need of user knowledge

Support to:• decision making in organizations• e-Science experimentations

2


Related Work

Early proposals (Intelligent Data Analysis systems):

Distribution of tools & computational aspects:

• local frameworks• single-user• predefined set of tools (little extensibility)

Distribution of users & evolution of organizations:

• SOA [Ali, 2005] [Kumar, 2005]• Grid [Kgrid, 2003]• OGSA: SOA + Grid

• user support in advanced activities [Bernstein, 2005] [Žáková, 2011]• collaboration [myExperiment, 2009]

3


Motivation

Main issues in open, distributed and collaborative scenarios

• Localization of many available distributed tools

• Integration of heterogeneous tools (interfaces, programming languages, OSs, transfer protocols,…)

• Complexity in their usage (data preparation, I/O interpretation, precondition

satisfaction, process design), many possible combinations

• Coordination in cooperative work (source of complexity)

4


Research Question

How to support a community of users in the design of a KDD project in an open, heterogeneous, distributed and collaborative scenario?

1. Which kind of support and functionalities should a platform offer?2. Which principles should the platform be based on?3. Which resources are involved in a KDD process and how to

represent them?

5


Functional Requirements

1. Support functionalities that should be offered by the platform:

• importing new heterogeneous tools for several KDD tasks• retrieving tools useful for certain purposes• designing a process by connecting tools together

• understanding whether such connections are meaningful• suggesting tool’s sequences effective for a given goal

• execution of the process• supporting collaboration all through the design process

• co-operative design• communication among team’s members

6


Non-functional Requirements

2. Principles on which the platform should be based

Requirements • Interoperability• Flexibility• Modularity• Reusability• Transparency• Usability

Main issues • heterogeneity• complexity• distribution• coordination

7


Resources in a KDD project

3. Identification of resources involved in a KDD process

• Computational units (gathering, preparation, modeling, visualization, …)• Data and Models (dataset, input parameters, intermediate results, final model)• Actors (domain experts, DB/DWH administrators, DM and KDD experts) • Computational processes

8


Knowledge- and user-centric approach

Collaborative functionalities• co-operation• communication facilities

Knowledge LayerData model: systematization of knowledge regarding each KDD resource, from both a theoretical and an operational perspective

Basic ServicesServices for every KDD phase. • tools are wrapped as services, • deployed on a server,• published in a common repository

Support Services• back-end functionalities• advanced functionalities

Service-Oriented platform

tool servicerunning as

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Conceptual: abstract representation

Knowledge Layer

Methodology • degrees of abstraction for description of resources• semantic technologies for their representation

• formal ontology building methodology [Fernandez, 1997] [Staab, 2001]• quality requirements [Gruber, 1995]

Domain ontologies

Concrete: actual resources of the platform

Execution: logs and traces

Semantically annotated XML descriptors

10


Knowledge Layer > Computational units

tool

service

algorithm

running as

implemented as

KDDONTO

Algorithm

Task

Method

Phase

Data

Performance Index

• algorithms arranged in a taxonomy• method, task, KDD phase, performances• data arranged in a taxonomy• algorithms’ interfaces (I/O, pre/post-conditions)• …

Implemented in OWL-DL (ALCOIF expressivity)

[IDA 2009] [ECML 2009]

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SAWSDL: Semantic annotations for WSDL (w3c)

Descriptors are stored in a UDDI registry

<location><I/O><algorithm><performance><author>

eSAWSDL

• location (<xmls:impl>)• I/O <message>: type, syntax

Further extensions:• implemented <algorithm>• <performance>• <author>• …

tool

service

algorithm

running as

implemented as

UDDI

Algorithm alg

SERVICE

WSDL urlQos val

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Mappings between abstraction levels

Labeled Dataset

abc

C4.5algorithm

Remove missing valuesalgorithm

KDDONTO

• KDDONTO provides a shared vocabulary which services refer to• such mappings can support process composition

<location><output><algorithm><performance><author>

<location><input><algorithm><performance><author>

eSAWSDL2eSAWSDL1

13


Knowledge Layer > Actors

TeamONTO is a formal ontology devoted to represent details of actors [CTS 2012]

Person

Project

Algorithm

WebService

Publication

Domain

Organization

about

memberOf

worksIn

writes

hasSkill

hasSkill

hasSkill

TeamONTO

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Knowledge Layer > Processes


eSAWSDL 1


eSAWSDL 2


eSAWSDL 3


eSAWSDL 4

XML-descriptor for concrete level processes• <services> included in the process• <connection> among their I/O interfaces• <users> in charge of settings service parameters• metadata: creation <date>/<time>, <author>, <comments>

Process descriptors are stored in a Process Repository

ProcRep index

project

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ProcRepindex

• extracts the most frequent and common subprocesses• arrange them in a lattice

Process Repository indexing method [SEBD 2011][SAC 2012]

Application of graph-based hierarchical clustering [Jonyer, 2002]

hierarchical clustering

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ProcRepUser query index

graph matching

• extracts the most frequent and common subprocesses• arrange them in a lattice

Process Repository indexing method [SEBD 2011][SAC 2012]

Application of graph-based hierarchical clustering [Jonyer, 2002]

Support for process retrieval: subgraph isomorphism on the index is more efficient than on the whole repository

16


Knowledge Layer > Overview

KDDONTO

ProcRep index

eSAWSDL 1

Process

Person

Project

Algorithm

WebService

Algorithm

WebServiceDataPerformance


UDDI

Algorithm alg

SERVICE

WSDL urlQos val

TeamONTO

eSAWSDL 2


project

Advantages: loose-coupling, modularity, reusability, support to advanced functions

17


KDDVM Platform

Service-oriented platform for KDD process design

support services

collaborative functionalities

[CTS 2010][CTS 2011][IFS]

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KDDVM Platform > KDDDesigner

• integration point of other support services• platform front-end

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KDDONTOAlgorithm

GoalData

UDDI

Algorithm

SERVICE

WSDL urlQos val

alg

Retrieval of KDD services satisfying user requirements• syntactic service discovery (service name)• semantic service discovery (functionalities, goal, interface,…)

1. Search, in KDDONTO, for algorithm satisfying the requirements2. Search, inside UDDI, for services implementing such algorithms


eSAWSDL

Service Discovery

20


Service Discovery

Retrieval of KDD services satisfying user requirements• syntactic service discovery (service name)• semantic service discovery (functionalities, goal, interface,…)

1. Search, in KDDONTO, for algorithm satisfying the requirements2. Search, inside UDDI, for services implementing such algorithms

21


Process Composition

Verification of I/O data compatibility

22


Process Composition: matchmaking

Checking the validity of the match [IDA 2009] [ITAIS 2009] [ECAI 2010]

abc

KDDONTO

Same format?Same syntax?

• syntactic compatibility: comparison between service descriptors



eSAWSDL1 eSAWSDL2

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Checking the validity of the match [IDA 2009] [ITAIS 2009] [ECAI 2010]

abc

data2data1

KDDONTO

same concept?subconcept?part-of concept?

Output:match cost

match evaluationfunction:

• syntactic compatibility: comparison between service descriptors• semantic compatibility: comparison between ontological annotations of the services (kind of match between I/O, preconditions/postconditions...)



eSAWSDL1 eSAWSDL2• kind of match• preconditions• etc…

23



Usage of Matchmaking:

• provide the user with information about the validity of the match• find all services compatible with a given one• support an advanced functionality for semi-automatic process

composition

24


Process Composition: semi-automatic procedure

Procedure to generate conceptual processes [IDA 2009] [ITAIS 2009] [ECAI 2010]

• planning technique: goal-driven, backwards strategy • basic step based on matchmaking• pruning criteria, stop criteria, user constraints• results: abstract processes useful as templates, ranked according to a metric

25


Collaborative Features

Collaborative features for process design within a team 1. Team building: to find users with certain skills & build a team2. Multi-user Process design and Versioning3. Communication4. Task assignment

Retrieval of users with certain competencies, inside TeamONTO

[CTS 2011]

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Collaborative features for process design within a team1. Team building: to find users with certain skills & build a team2. Multi-user Process design and Versioning3. Communication4. Task assignment

Asynchronous edit of a process, and its storage as a new version in the repository

[CTS 2011]

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Talk page & comments attached to versions

[CTS 2011]

26




Assignment to a user of the team the parameter setting for the service execution

[CTS 2011]

26


Formal Verification of Processes

1. Process representation through REO (CWI, Amsterdam) [Arbab, 2004]

• language for representation of interaction among services• formal and compositional semantics

2. Specification of the desider behavior through mCRL2 [Groote, 2006]

3. Model-checking to verify whether the process satisfies the behavior

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27







Purposes [SEBD 2010]

• formal verification of KDD processes at design-time• reuse of typical pre-verified KDD subprocesses

27


Conclusion

KDD process design in distributed and collaborative environments

• Distributed and heterogeneous settings:• systematic use of semantic information all through the process• loosely-coupled service architecture

• Community-centered approach:• support functionalities • users with various degrees of experience

• General-purpose attitude:• domain-indipendence• generalization to experimental processes in e-Science

28


References

[Fayyad, 1996] “From data mining to knowledge discovery: an overview”, American Association for Artificial Intelligence.

[Ali, 2005] “Web services composition for distributed data mining”, Parallel Processing[Kgrid, 2003] “The knowledge grid”, Comm. ACM[Bernstein, 2005] “Towards Intelligent Assistance for a Data Mining Process: An Ontology

Based Approach for Cost-Sensitive Classification”, IEEE TKDE [Žáková, 2011] “Automating Knowledge Discovery Workflow Composition Through

Ontology-Based Planning”, IEEE T. Automation Science and Engineering[myExperiment, 2006] “The Design and Realisation of the Virtual Research Environment

for Social Sharing of Workflows”, FGCS[Fernandez, 1997] “METHONTOLOGY: from Ontological Art towards Ontological

Engineering”, Proc. of the AAAI97 Spring Symposium.[Staab, 2001] “Knowledge Processes and Ontologies”, IEEE Intelligent Systems.[Gruber, 1995] “Toward principles for the design of ontologies used for knowledge

sharing”, Int. J. Hum.-Comput. Stud.[Jonyer, 2002] “Graph-based hierarchical conceptual clustering”, J. Mach. Learn. Res.[Groote, 2006] “The formal specification language mcrl2”, in MMOSS.[Arbab, 2004] “Reo: a channel-based coordination model for component composition”,

Mathematical. Structures in Comp. Sci.


Publications

[IDA 2009] “Ontology-driven KDD Process Composition”. LNCS, Springer, 2009.[ECML 2009] “KDDONTO: an Ontology for Discovery and Composition of KDD

Algorithms”. ECML/PKDD09 Workshop.[ITAIS 2009] “Automatic Definition of KDD Prototype Process by Composition”.

“Management of Interconnected World”, Springer.[CTS 2010] “Semantic-Driven Design and Management of KDD Processes”. CTS 2010,

IEEE.[SEBD 2010] “Towards Coordination Patterns for Complex Experimentations in Data

Mining”. SEBD 2010.[ECAI 2010] “Supporting Users in KDD Process Design. A Semantic Similarity Matching

Approach”. Planning To Learn Workshop in ECAI2010.[CTS 2011] “Semantic-Aided Designer for Knowledge Discovery”. CTS 2011, IEEE.[SEBD 2011] “Clustering of process schema by graph mining techniques”. SEBD 2011.[SAC 2012] “Mining Usage Patterns from a Repository of Scientific Workflows”. SAC2012,

ACM.[CTS 2012] “Semantically-supported Team Building in a KDD Virtual Environment”. CTS

2012, IEEE.[ISF] “A Virtual Mart for Knowledge Discovery in Databases”. Information System

Frontiers, Springer, accepted.

Technology

Kdd Process Design in Collaborative and Distributed Environments