Towards New Models and
Languages for Data Mining
and Integration
Peter Brezany
Institute of Scientific Computing
University of Vienna, Austria
Presentation at the NeSC, Edinburgh
August 13, 2008
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 CRISPDMI (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
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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
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Authors: Y. Han & F.A. Khan
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Use case
The fields in the data are:
(from P. Caron, C. Shearer, Interactive Visual
Workflow: The Key to Streamlining the
Data Mining Process)
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 any
future patient?
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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
Composition
Space of Source
and Destination
Data and
Services
DMI
Process
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A Possible Position of DmiFlow in the
Workflow Management Systems
User-relevant information flow
Feedback
Visualisation
User
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System-relevant information flow
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System support
e23
System support
e22
Sub-worklfow for e2
e3
oth
e21
System support
e2
High-level workflow composition
e1
BPE
er la L or
ngu
age
UM
L
Textual representation
UM
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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
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