Towards New Models and Languages for Data Mining and Integration

Peter Brezany

Institute of Scientific ComputingUniversity of Vienna, Austria

Presentation at the NeSC, EdinburghAugust 13, 2008

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Outline

Introduction CRISP-DM Model and Methodology

What is CRISP-DM Why update it From CRISP-DM to CRISP-DMI

Impact of CRISP-DMI on the DMI Workflow Language

State of the Art in Language Design Discussion of the 1st Language Design Ideas Conclusions and Future Work

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What is CRISP-DM?

Phases of the CRoss Industry Standard Process for Data Mining

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CRISP-DM Phases

Business Understanding: the process of understanding the project objectives from a business perspective

Data Understanding: the process of collecting and becoming familiar with data

Data Preparation: the process of selecting and cleansing the data that will be fed into the modeling tools

Modeling: the process of applying modeling to manipulate the data so that conclusions can be drawn

Evaluation: the process of evaluating the model and its conclusions

Deployment: the process of applying the conclusions to a business

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Why to Update CRISP-DM?

Support for large-scale data mining a lot of distributed, heterogeneous and large

datasets (primary data, derived data, background data, catalogs): from data to “space of data”

data integration is of great importance new actors (domain expert, data analyst, data

publisher, system administrator) support by new components (e.g. provenance) etc.

Our approach: from CRISP-DM to CRISP-DMI (Cross Research & Industry Standard Process for Data Mining and Integration )

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CRISP-DMI Model

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Space of Data and Services

Author: Ibrahim Elsayed

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TCM Workflow

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Subworkflow Targeted by Provenance

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Visualization of Provenance Data

Authors: Y. Han & F.A. Khan

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Use case

The fields in the data are:

Age: Sex: M or F BP: Blood Pressure-High, Normal, or Low Cholesterol: Blood Cholesterol Level-Normal or High Na: Blood sodium concentration K: Blood potassium concentration Drug: The drug to which this patient responded

The business question: Can we find which drug is appropriate for anyfuture patient?

(from P. Caron, C. Shearer, Interactive Visual Workflow: The Key to Streamlining the Data Mining Process)

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DmiFlow: DMI Workflow Language

The emerging DMI applications lead to the demand of a powerful DMI workflow language

On top of it interactive GUIs can be developed

It should enable optimized implementation of language processors

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DMI Process to be Composed by DmiFlow

Space of Source and Destination Data and Services

Space of Source and Destination Data and Services

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A Possible Position of DmiFlow in the Workflow Management Systems

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Principles for DMI Language Design

Programmer Responsibilities Identification of Parallelism Specifying communication mode between

workflow components Providing hints (sometimes based on domain

knowledge) enabling advanced optimization Language Desiderata

High abstraction level, not too complex (high productivity)

Advanced compositional features Execution of data mining queries (support for the

inductive database model) Extendibility Efficient implementation (high performance)

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Related Work

Low-level workflow notations: XML-based: BPEL4WS, DSCL, WSFL, etc. Other: Sculf (Taverna), MoML (Kepler), etc.

High-level languages (only for workflows integrating business processes): Workflow Prolog Valmont: It includes, process model, information

model, and organization model (It registers organizational structure and resources.)

C & Co: a C based language F#: functional workflow specification at a script

level (MicroSoft development) Martlet: functional workflow specification

Compositional languages (Strand, PCN, etc.)

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Workplan for the Language Design

Phase 1 (ongoing): proposing semantic structure and outlining compositional structure of programs while leaving open some aspects of their concrete representations as strings of symbols.

Phase 2: finalizing the 1st language definition version.

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Basic Features of DmiFlow

Code modules – managing complexity Activities: their types, parameters, locations Virtual communication channels between

activities, which can be represented by Persistent explicit datasets Internal datasets (implementation dependent) Ports used for streaming data

Control structures: parallel & sequential statements, loop statements, conditional statements)

Embedded data mining query execution

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Declaration of Activities and Datasets

activity activity_name: ActivityType at (activity_location);

ActivityType – predefined (type of parameters and semantics)

activity_location ∊ {url, discover, default} this is optional

dataset dataset_name represents (source = source_spec, hints_list);

source_spec ∊ {url, internal, port}

hint ∊ {org = dataset_organization, size = estimated_size, …}

dataset_organization ∊ {set, sequence, bag, …}

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Basic Control Structures

Concurrent execution:

cobegin { activity1(…); … activityn(…);}

Sequential execution:

block { activity1(…); … activityn(…);}

Data mining query execution:

exec dmq (arguments) byactivity (activity_name){ dmq_query_specification}

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Workflow Example – Graphical Form

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DmiFlow Example (1)

module WorkflowExample {

const replaceMethod = "average", splitingMethod = "gini", //hint url1 = "/serverA/dmi/services/integrationService1", url2 = "/serverB/dmi/services/decisionTreeService1", url3 = "/serverB/dmi/services/neuralNetworkService3";

activity integrDS: dataIntegrationActType at (url1), missVals: MissingValuesActType at (discover), normalise: NormalisForNNActType at (default), dt: decisionTreeActType at (url2), nn:NeuralNetworkActType at (url3);

dataset ….

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DmiFlow Example (2)

dataset ds1 represents (source = "http://www.myproject/d1.dat", org = set, size = [1.5, 2.0]), ds2 represents (source = "http://www.myproject/d2.dat", type = set), intConf represents (source = "/server/dmi/config/integr.conf); outIntegr represents (source = internal, org = set), cleaned represents (source = internal, org = set); normalised represents (source = internal, org = set); nnConf represents (source = "/server/dmi/configs/nn.conf); nnMod represents (source = "/server/dmi/models/nn.pmml); dtMod represents (source = "/server/dmi/models/dt.pmml);

defworkflow { . . . }

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DmiFlow Example (3)

defworkflow main () { integrDSets (in ds1, ds2, intConf; out outItegr); missValues (in outIntegr, replaceMethod; out cleaned); cobegin { block { normalise (in cleaned; out normalised); nn (in normalised, nnConf; out nnMod); } dt (in cleaned, splittingMethod; out dtMod); } }

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Future Work

Extend language functionality Investigate DmiFlow execution model

for the ADMIRE architecture Define functional specification of the

DmiFlow language processor Specify concrete language syntax