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Knowledge and Data Management
in Grids
(or using Grids to climb data mountains
and find knowledge nuggets)
Domenico Talia
CoreGRID / University of Calabria
www.coregrid.net
[email protected]
CereGRID Summer School – Budapest – 3-7 September, 2007
AGENDA
• Introduction
• Objectives
• Some Data Grid Projects
• Distributed Data Mining
• The KDM Institute
• Conclusions
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Introduction (1)
• Data and knowledge management is becoming key element in
GRIDs as or more than high performance delivery.
Distributed Knowledge and Data Management
Dealing with issues concerning representation, storing,
querying, mining, exchanging and integration of data (and
resulting knowledge) in dynamic distributed environments.
• Those issues are today addressed by exploiting features offered
by Grid/P2P/GC/UC (Distributed) Technologies.
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Introduction (2)
• Many activities all over the word on
– GRID/P2P databases and distributed repositories
– Distributed metadata management
– Pervasive information systems
– GRID-based digital libraries
– Distributed data streaming management
– Distributed knowledge management
– Data-oriented services
• A more important role is expected in the near future.
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Introduction (3)
• Today the information stored in digital data
archives is enormous and its size is still growing
very rapidly.
The world has created 161 exabytes (161 billion
gigabytes) of digital information in 2006.
(source: IDC)
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Introduction (4)
•
Lots of data collected and warehoused.
– Data collected and stored at enormous speeds in local databases,
from remote sources, from the environment and from the sky.
– Traditional techniques are infeasible for large raw data.
•
Scientific simulations generating terabytes of data.
– Huge data sets are hard to understand.
– Most data will never be examined by humans; it is analyzed and
summarized by computers.
•
Storage costs are currently decreasing faster than computing costs: this
trend makes things worse.
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Introduction (5)
Whereas until some decades ago the main problem
was the shortage of information, the challenge now
seems to be
– the very large volume of information to deal with
and
– the associated complexity to process it and to
extract significant and useful parts or
summaries.
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Data Management in Science
• Data intensive applications are those that explore, query, analyze,
visualize, and in general, process very large-scale data sets.
•
Computational science is evolving toward data intensive applications
that include
 data integration and analysis,
 information management, and
 knowledge discovery.
•
Data intensive applications in science help
 scientists in hypothesis formation
 companies to provide better, customized services and support
decision making.
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Evolution of Science Methods
Jim Gray's formulation of the evolution of science
methodologies:
– Thousand years ago: science was empirical, describing
natural phenomena.
– Few hundred years: theoretical branch, using models,
generalizations.
– Last few decades: a computational branch, simulating
complex phenomena.
– Today: data exploration (eScience) - unify theory,
experiment, and simulation.
(Data captured by instruments, or generated by simulator;
processed by
software; information/knowledge stored in computer; scientist
analyzes database/files, using data management and statistics.)
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GRIDS: From Computing to Data (1)
• The use of computers is changing our way to make discoveries
and is improving both speed and quality of the scientific
discovery processes.
• In this scenario the Grid provides an effective computational
support for distributed data intensive application and for
knowledge discovery from large and distributed data sets.
• Grid services can be the basic element for composing software
and data elements, and executing complex applications on Grid
and Web systems.
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GRIDS: From Computing to Data (2)
• The Grid allows to federate and share heterogeneous resources and
services such as software, computers, storage, data, networks in
a dynamic way.
• Today the Gris is not just compute cycles, but it is also a
distributed data management infrastructure. Integrating
those two features with “smart” algorithms we can obtain a
knowledge-intensive platform.
• In the latest years many significant Grid-based data intensive
applications and infrastructures have been implemented.
• The service-based approach is allowing the integration of
Grid and Web for handling with data.
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The EUROPEAN DATA GRID
 The LHC generates 1GB/sec
or 10PB/year
 Applications:
 particle physics,
 earth observation,
 bioinformatics & medical
http://cern.ch/eu-datagrid/
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NASA INFORMATION POWER GRID
 First “production quality” Grid
 Linking NASA & academic
supercomputing sites at 10 sites
 Applications: computational
fluid dynamics, meteorological
data mining, Grid benchmarking
http://www.ipg.nasa.gov/
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TERAGRID
 Linking supercomputers
through a high-speed
network 4x 10GBps
between SDSC, Caltech,
Argonne & NCSA
 Compute and data
services for science
applications & users
 http://www.teragrid.org/
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GOOGLE & NASA
Source: Charlie Catlett blog – Jan 2007
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TerraService.NET
• A photo of the United States
– 1 meter resolution
(photographic/topographic)
– USGS data
– Some demographic data
(BestPlaces.net)
– Home sales data
– Linked to Encarta Encyclopedia
15 TB raw, 6 TB cooked (grows 10GB/w)
• Offered as a Web Service to many
applications (business, public
administrations, scientits)
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OPEN SCIENCE GRID
• The Open Science Grid
links storage and
computing resources at
more than 30 sites across
the United States
• Support a variety of
services and applications,
many concerned with
large-scale data analysis.
• Thousands of computers
and tens of terabytes of
storage
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myExperiment
"Scientists would rather share
their toothbrush than their data"
Mike Ashburn, University of Cambridge
• myExperiment is a collaborative research environment
which enables scientists to share, re-use and repurpose
experiments.
• myExperiment has been influenced by social networking
programs such as Wired and Flickr, and is based on the
mySpace infrastructure.
• myExperiment creates an environment for scientists to
adopt Grid technologies, where they can define, when they
share data, with whom they share it and how much of it can
be accessed.
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DATA MINING ON GRIDS
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Data Mining and Computational Needs
•
Is not uncommon to have sequential data mining applications that
require some days or weeks to complete their task.
•
Parallel computing can bring significant benefits in the implementation of
data mining and knowledge discovery applications by means of the
exploitation of inherent parallelism of data mining algorithms.
Main goals:
 performance improvements of existing techniques,
 implementation of new (parallel) techniques and algorithms,
 concurrent analysis with different data mining techniques and result
integration to get a more accurate model
→ Ensemble Learning
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Data Mining and Computational Needs
• Today many data is distributed geographically or locally.
• When
– large data sets are coupled with
– geographic distribution of data, users, and systems,
it is necessary to combine different technologies for
implementing high-performance distributed knowledge
discovery systems.
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Parallel and Distributed DATA MINING
– Parallel data mining
•
•
•
•
Task or control parallelism
Independent parallelism
SPMD parallelism
Hybrid parallelism
Can be a
component of
– Distributed data mining
• Voting
• Meta-learning, ensemble learning etc.
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Parallel and Distributed DATA MINING
Three main strategies in the exploitation of parallelism in data
mining algorithms:
 independent parallelism
 control parallelism
 SPMD parallelism.
•
•
•
Independent parallelism: processes are executed in parallel in an
independent way; generally each process has access to the whole data
set.
Control parallelism (or Task parallelism): each process executes different
operations on (a different partition of) a data set.
SPMD parallelism: a set of processes execute in parallel the same algorithm
on different partitions of a data set; processes exchange partial results.
D. Talia, "Parallelism in Knowledge Discovery Techniques", Proc. Sixth Int. Conference on
Applied Parallel Computing, Helsinki, LNCS 2367, pp. 127-136, June 2002.
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Task and SPMD Parallelism
Work1
Work2
Work3
P0
P1
P2
Work1
Work2
Work3
Data1
Data2
Data3
Data1
Task Parallelism
Data2
Data3
Uniprocessor
Work
Work
Data i
Data i
Work
Data i
SPMD Parallelism
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Parallel DM Strategies
•
These three basic strategies are not necessarily alternative for
parallelizing data mining algorithms.
•
They can be combined to improve both performance, scalability and
accuracy of results.
•
With parallel strategies different data partition strategies can be used
• sequential partitioning
• separate partitions without overlapping
• cover-based partitioning
• some data can be replicated on different partitions
• range-based query

partitions based on some queries that select data according to attribute
values.
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Parallel in DATA MINING Techniques
•
Parallel Decision Trees
 tree construction in parallel ( processes  subtrees )
•
Discovery of Association Rules in Parallel
 rule and/or data partitioning on different processors
•
Parallel Neural Networks
 parallelism exploitation: training, layers, neurons, weights
•
Parallel Rough Set mining
 Parallel computing of reducts (construction of the rows of the
discernibility matrix)
•
Parallel Cluster Analysis
 different clustering in parallel, data partitioning, computing
similarity matrix in parallel.
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Parallel Decision Trees
Classification: assigning new items to predefined classes.
Tree leaves represent classes and tree nodes represents attribute values.
Task parallel approach
One process is associated to each sub-tree.
– The search occurs in parallel in each sub-tree.
– The degree of parallelism P is equal to the number of active processes
at a given time.
P1
P3
P2
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Parallel Decision Trees
SPMD approach
Each process classifies the items of a subset of data.
– The P processes search in parallel on the whole tree using a partition of the
data set D/P.
– The global result is obtained by exchanging partial results.
P1
P2
P3
– The data set partitioning can be operated:
• partitioning the tuples of the data set: (D/P) per processor.
• partitioning the N attributes of each tuple: D tuples of (N/P) attributes per processor.
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Parallel Cluster Analysis
 SPMD approach :

Each processor executes the same algorithm on a different
partition of the data set to compute partial clustering results.

Local results are then exchanged among all the processors to
get global values on every processor.

The global values are used in all processors to start the next
clustering step until a convergence is reached or a certain
number of steps are executed.
The SPMD strategy can be also used to implement clustering
algorithms where each processor generates a local approximation of
a model (classification) that at each iteration can be passed to the
other processors that can use it to improve their clustering model.
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Parallel Cluster Analysis
The SPMD approach has been used in P-AutoClass.
Execution times
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Parallel Cluster Analysis
The SPMD approach has been used in P-AutoClass.
Speedup
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Towards Data and Knowledge Services
• This objective can be achieved through
– development of techniques and tools for supporting data mining
applications and
– integration of Data and Computation Grids with Knowledge Grids.
to support the process of unification of data management and
knowledge discovery systems with Grid technologies for
providing knowledge-based Grid services.
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Parallel & Distributed KDD on Grids
The basic principles that motivate the architecture design of
the Grid-aware KDD systems
 Data heterogeneity and large data size management
 Algorithm integration and independence
 Grid awareness
 Openness
 Scalability
 Security and data privacy.
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What The Grid Offers
• Grid tools, such as the Globus Toolkit, Legion and UNICORE,
provide basic services that can be effectively exploited in the
development of distributed data
and knowledge
management applications.
• Data Grid middleware (e.g. Globus RSL, RFT, …) implements
data management architectures based on two main services:
storage system and metadata management.
• Additional services are needed.
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Parallel & Distributed KDD on Grids
• By exploting a service-oriented approach, knowledge discovery
applications can be developed on Grids to deliver high performance
and manage data and knowledge distribution
• Efforts are on going for the development of
– Data access and management
– knowledge discovery tools and services
on the Grid.
Examples:
- OGSA-DAI, OGSA-DQP, Discovery Net, KNOWLEDGE GRID,
Data Cutter, GDIS, …
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KDD PROJECTS and PROTOTYPES
KNOWLEDGE GRID
University of Calabria
Discovery Net
Imperial College (e-Science)
DataMiningGrid
FP6 EU project
ADaM
University of Alabama
Terra Wide Data Mining Testbed
University of Illinois at Chicago
Grid Miner
University of Vienna
Data Cutter
University of Maryland
Weka4WS
University of Calabria
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COREGRID KDM INSTITUTE
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CoreGRID KDM Institute
• The KDM Institute is providing a collaborative environment
for 13 research teams working on:
– Distributed storage management on GRIDs
– Data Access and Semantic GRID techniques and tools for
supporting data intensive applications
– Knowledge discovery and data mining in GRIDs.
• With focus also on
– Service Level Agreement Negotiation and
– Security Requirements for Data Management
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Objectives for the Institute (1)
MAIN OBJECTIVE:
• Strengthen joint activities of European research groups and
promoting larger leading teams, operating as a Research
Institute working on models and tools for KNOWLEDGE and
DATA MANAGEMENT in GRIDs and P2P SYSTEMS.
• Consolidate the research activities carried out till now by
partners in the KDM Institute and among the CoreGRID
Institutes.
• Establish cooperation with future partners and look for
interaction with other players in the area of KDM.
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Objectives
• Discuss R&D issues in Data Management in Grids
scenarios.
• Identify:
– Missing Solutions in Distributed Data Management
– Research Challenges in Global Data Management
– Potential Overlaps and Gaps in current Research Activities
– Common vision of Data Management and research interests
– Industrial needs and transfer
– Synergies and future common work
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Partners and Tasks
Partners
• JPA: three tasks in the areas
1. Distributed Storage
Management
• Storage Infrastructure
• Storage Management
Mechanisms
• Specifying Management
Policies
2. Information and Knowledge
Management
CETIC
Belgium
CNR-ISTI
Italy
ICS-FORTH
Greece
INFN
Italy
PSNC
Poland
STFC-RAL
UK
SZTAKI
Hungary
University of Calabria
Italy
• Semantic Modeling
Cyprus
University of Cyprus
• Semantic Representation
UK
University of
• Standardization and
Manchester
Integration
University
of Newcastle
• Data
Integration
andresearchers
Query
More
than 50 active
and
PhD students
are involved UK
reformulation in OGSA
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CNR-ICAR
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Grids
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Institute Roadmap (1)
• The research tasks that compose KDM Institute give a unified
vision of the data and knowledge management in Grids
through a layered approach that starts from efficient data
storage techniques (Task 2.1) up to information management
(Task 2.2) and knowledge representation and discovery (Task
2.3).
• The main vision of this Institute is based on common models
and frameworks that can integrate the research results of the
involved partners
and
result in common activities that advance the present results
and systems.
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Institute Roadmap (2)
•
We started with Phase 1:
“Exchanging partner information, experiences, and knowledge
about techniques, tools and systems for Data and Knowledge
Grids.”
•
Then moved to Phase 2:
“Sharing and integration of common goals, research results,
projects and system prototypes of Environments and
Services for Data and Knowledge-based Grids.”
•
Now we are in Phase 3:
Use of the results of the previous phases for providing a set of
solutions in the KDM area and for envisioning a unified framework
for handling data, information and knowledge on GRIDs.
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KDM Research Groups
•
The Joint Research Groups are working on:
1. GRID Data Storage Access and Management Architecture
• Partners: FORTH, PSNC, SZTAKI, UCY, INFN
2. Storage security
• Partners: INFN, FORTH, STFC
3. GRID Data Integration Models and Architectures
• Partners: UNICAL, UoM
4. Methods for Deriving GRID Trust and Security Policies for
Managing VOs
• Partners: CETIC, STFC
5. Distributed Data Mining in GRIDs and P2P Systems
• Partners: UNICAL, ISTI-CNR, UCY
6. Adaptivity in Distributed Query and Workflow
• Partners: UoM, UNCL
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Achievements – KDM Research Work
• The KDM Institute developed new research results on:
– Grid Services for distributed data mining (WekaxWS,
Knowledge Grid)
– Dynamic loading of services: extensions to support loading at
different granularities (DYNASOAR)
– Data integration and query reformulation in Grids (GDIS on
OGSA-DQP)
– A methodology for deriving Grid Trust and Security Policies for
VOs
– A distributed storage virtualization architecture
– Adaptive scalable data mining algorithms (Frequent Itemsets
Mining - FIM)
– A model for self-configuring Storage Area Networks
(Conductor)
– The design of an ontology for Grid scheduling
– Scalable information services for large-scale Grids
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Cooperation With Other Institutes
•
Semantic support for Meta-scheduling in Grids
(University of Manchester & Fraunhofer SCAI & JUELICH)
WP2-WP6
•
P2P Models for Resource Discovery, Data Management and Reliable Grid
Services
(University of Calabria & SICS & KTH)
WP2-WP4
•
Grid Scheduling for data-intensive applications
(University of Dortmund
& University of Calabria )
WP2-WP6
•
Dynamic adjustment of block size for minimum data transfer cost in Data
Grids
(University of Cyprus & University of Manchester)
WP2-WP4
•
Trust management in Grids
WP2-WP4
(STFC, University of Coimbra)
•
Public resource computing for Data Management
(University of Calabria & University of South Wales)
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WP2-WP7
Joint publications
•
Many joint papers and technical reports on
the KDM topics have been published in
journals and conferences by KDM
researchers in the second year of activities.
•
A book has been published in the SPRINGER
CoreGRID series as a Post proceedings of
the First Workshop on Knowledge and Data
Management
•
The post-proceedings of the First CoreGrid
Middleware workshop have been published
by Springer in the LNCS series.
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Conclusions (1)
• Science and industry must be able to handle very large
data sources (archives, databases, flat files).
• Data management and knowledge discovery tools are
necessary to find what is interesting in them.
• Grids may be used as a distributed infrastructure for
service-based data intensive applications.
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Conclusions (2)
• We are much more able to store data than to extract
knowledge from it.
• The integration of knowledge discovery and Grid technologies
can help in this task.
• Future Applications in Science and Business:
Internet-scale distributed computing integrating
data and knowledge services + computing services
• Collection of world-wide Grid/Web services
implementing complex applications.
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THANKS
www.coregrid.net
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