BUILDING SCALABLE DATA MINING GRID APPLICATIONS
An Application Description Schema and Associated Grid Services
Vlado Stankovski
Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova cesta 2, Ljubljana, Slovenia
Dennis Wegener
Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany
Keywords: Grid, distributed applications, data mining, middleware.
Abstract: Grid-enabling existing stand-alone data mining programs, data and other resources, such as computational
servers, is motivated by the possibility for their sharing via local and wide area networks. Expected benefits
are improved effectiveness, efficiency, wider access and better use of existing resources. In this paper, the
problem of how to grid enable a variety of existing data mining programs, is investigated. The presented
solution is a simple procedure, which was developed under the DataMiningGrid project. The actual data
mining program, which is a batch-style executable, is uploaded on a grid server and an XML document that
describes the program is prepared and registered with the underlying grid information services. The XML
document conforms to an Application Description Schema, and is used to facilitate discovery and execution
of the program in the grid environment. Over 20 stand-alone data mining programs have already been grid
enabled by using the DataMiningGrid system. By using Triana, a workflow editor and manager which
represents the end-user interface to the grid infrastructure, it is possible to combine grid enabled data mining
programs and data into complex data mining applications. Grid-enabled resource sharing may facilitate
novel, scalable, distributed data mining applications, which have not been possible before.
1 INTRODUCTION
Data mining in grid computing environments is
motivated by resource sharing via local and wide
area networks (Stankovski et al 2008a, 2008b).
Increased performance, scalability, access and
resource exploitation are the expected key benefits.
Furthermore novel distributed data mining
applications may facilitate the automated extraction
of potentially useful information from increasingly
large, geographically distributed data volumes.
However, grid-enabling large-scale data mining
applications is difficult to achieve due to a number
of factors. Grid computing itself is a novel field of
research and relevant standards and technologies are
still evolving (Foster, Kesselman, Tuecke, 2001;
Plaszczak and Wellner, 2006; Sotomayor and
Childers, 2006; Antonioletti et al., 2005). Moreover,
there exists a plethora of data mining technologies
and a staggering number of largely varying data
mining application scenarios (Kumar, Kantardzic
and Madden, 2006; Guedes, Meira and Ferreira,
2006; Stankovski and Dubitzky, 2007; Conguista,
Talia and Trunfio, 2007). Finally, data mining users
range from highly domain-oriented end users to
technology-aware specialists. To the former user
group transparency and ease-of-use is paramount,
whereas the latter group needs to be in control of
detailed data mining and grid technology aspects.
In the DataMiningGrid project, we have aimed to
address the requirements of modern data mining
application scenarios, in particular those which
involve sophisticated resource sharing. A detailed
technical account of the DataMiningGrid system is
presented elsewhere (Stankovski et al, 2008a,
2008b) as well as the actual applications (e.g.
Trnkoczy and Stankovski, 2008). We have designed
and implemented a workflow-oriented, scalable,
high performance computing system that supports
emerging grid interoperability standards and
technology. The system itself is freely available
under the Apache Open Source License V2.0 via
221
Stankovski V. and Wegener D. (2008).
BUILDING SCALABLE DATA MINING GRID APPLICATIONS - An Application Description Schema and Associated Grid Services.
In Proceedings of the Third International Conference on Software and Data Technologies - PL/DPS/KE, pages 221-228
DOI: 10.5220/0001891302210228
Copyright
c
 SciTePress

SourceForge.net, including all supporting
documentation.
In the present study, we investigate the problem
of how existing, stand-alone data mining
applications can be grid-enabled and subsequently
executed on a grid service infrastructure.
Our final goal is to enable users from various
disciplines to build and utilize complex, scalable
data mining applications. To that end, an effective
mechanism, which was developed under the
DataMiningGrid project, is presented in this paper.
2 GRID RESOURCES
In a grid environment, it is possible to exploit
numerous, potentially unlimited resources, such as
data, data mining applications, CPUs, storage,
networks and clusters.
Given the nature of data mining applications, a
variety of computational resources that may be
shared were identified:
 Data. The data to be mined, which may exist in
the form of relational databases, data files
(documents in various formats) and directories
consisting of collections of documents;
 Programs. Data mining programs providing
the implementation of data mining algorithms
used to mine data. Seen from the grid resource
viewpoint, a data mining program, application
or algorithm is an executable with associated
input data, output data and parameter settings.
The executable can be anything starting from
a Java, C, Python or a BashShell program.
 Computational Machines. Computational
machines providing raw computing power to
run the data mining program and process the
data. Important parameters about
computational machines are speed,
occupancy, memory which can be used during
processing, architecture and so on;
 Storage. Data storage devices to physically
store the input and output data of data mining
applications. For storage devices it is mainly
important to have the ability to reserve space
in advance, and to have safe and fast
mechanisms for storing and retrieving data.
Should storage be used for storing relational
databases, than the necessary server-side
software is also essential to be implemented
on the actual site;
 Streaming Devices. Sensors and other devices
streaming data in a network are special kind of
resources. These, however, are currently not
(directly) supported by the DataMiningGrid
technology; and
 Networks. Optimization of network parameters
should also be considered for time-critical
applications.
It is important to realize that certain resources
from the list above can easily be moved in the
network (notably data, programs and associated
libraries) and existing transfer protocols could be
used for that purpose (e.g. ftp, GridFTP or RFT),
while other resources can not be moved in the
network (e.g., computational machines). All
resources, however, have different parameters that
should be considered when developing a distributed,
grid-based data mining application.
We have investigated several large-scale data
mining scenarios in which it is impractical or
impossible to move the data in the network due to
variety of reasons, e.g., the large amount of data or
security restrictions. In such cases, it must be made
possible to move the data mining programs to the
data rather then the data to the data mining
programs.
3 GRID SERVICES
Grid middleware services represent virtualization of
grid resources. In other words, grid resources can
only be accessed by using grid services, while local
resources have to be grid-enabled before they are
actually shared.
3.1 Virtualization
In the past years, considerable research and
development effort has been put towards the
development of middleware services and tools
targeting some of the access and composition
obstacles to large-scale resource sharing and
exploitation. The key benefit of grid services is that
they provide an effective and popular way of
abstracting the complexity of distributed data and
computational resources and also represent a variety
of utility services. DataMiningGrid represents a
platform that enables the sharing of grid resources
between applications which facilitates reuse,
embedding, modification or extension of an
application’s content to enable a far more rapid
development cycle than what was previously
possible with conventionality programming
methodologies.
In particular, users are not concerned either with
the technical details associated with constructing
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grid services, or the technical details of the
underlying grid and Web service infrastructures, but
are interested in exploring their data. To that end,
DataMiningGrid provides workflow-editing
software components that are integrated with the
existing Triana workflow editor and manager. By
using the workflow editing components it is possible
to develop applications which are tailored to the
specific needs of the end-users. Moreover,
interoperability between services within a distributed
system is enabled by using the OGF’s WSRF
standard, so as to liberate users to use the grid
services regardless of their own systems.
3.2 Workflows
Workflows are effectively declaratively defined as
coordinated “plumbing” between services, executed
as steps. Triana is an advanced system for editing
and managing workflows, which is used as a user
interface to Web services (Churches at al., 2005) and
recently also as a user interface to grid services
(Stankovski et al., 2008a). It may be used to link
efficiently and effectively data resources, analytical
tools and computing processes together in a form of
various domain-specific graphs representing
information flows. It is complete and self-defined,
and represents a far more effective mean of sharing
knowledge, processing, communication, storage or
content than the more elementary building blocks
that represent the individual services.
As more data is produced by users every day and
more utility services are provided online, coupled
with ‘time-to-market’ pressures, the complexity and
dynamicity of information flows in the past years
have been increasing dramatically. It is therefore
necessary in many scientific and business
communities, and will be even more so in the near
future, to discover, extract and share the knowledge
contained in complex workflows.
3.3 Grid Middleware and Services
Different grid middleware solutions exist and
continue to be developed over the past years. Here,
we provide a short overview of ready-to-use grid,
WSRF-compliant services, which were used to build
a grid service infrastructure (a test-bed). These
include the Globus Toolkit 4, Condor and
DataMiningGrid high-level services.
3.3.1 Globus Toolkit 4 Middleware and
Services
One of the first grid middleware toolkits
implementing the Web Services Resource
Framework (WSRF) v. 1.2 specification, a
specification promoted by the Organization for the
Advancement of Structured Information Standards
(OASIS), is the Globus Toolkit 4 (GT4) (Foster,
Kesselman and Tuecke, 2001). GT4 provides a
range of grid services that can be directly used to
build a distributed grid environment. These include
data management, job execution management,
community authorization services etc. All these
services can be used to build custom grid
applications, and are elaborated in detail elsewhere
(Sotomayor and Childers, 2006). Besides these
ready-to-use services, the GT4 provides an
Application Programming Interface (API) that
allows for development of proprietary WSRF-
compliant services. Due to the reasons listed above
the GT4 was used as grid middleware in this study.
Following is a short overview of relevant ready-
to-use grid services from the GT4 toolkit.
 The Web Service - Grid Resource Allocation
and Management (WS-GRAM) provides all
basic mechanisms required for execution
management, i.e., initiation, monitoring,
management, scheduling, and coordination of
remote computations.
 Data Management Services, such as GridFTP
and Reliable File Transfer (RFT). These data
services are mainly used for transfer and
management of distributed, file based data,
including program executables and their
software libraries. GridFTP is used, e.g., to
transfer executables and required libraries to
the selected computational server in the grid.
 Information Services are used to discover,
characterize and monitor resources, services
and computation. The GT4’s Monitoring and
Discovery System 4 (MDS4) provides
information about the available grid resources
and their status. It has the ability to collect and
store information from multiple, distributed
information sources. This information is used
to monitor (e.g., to track usage) and discover
(e.g., to assign computing jobs and other
tasks) the current state of services and
resources in a grid system. The
DataMiningGrid high-level services (in
particular the Resource Broker and
Information Services) are using the MDS4
service.
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In our test bed, the following GT4 services are
used extensively: WS-GRAM, GridFTP and MDS4.
3.3.2 Condor
Scheduling of grid jobs in local computing clusters
is achieved by using the Condor middleware.
Condor is specialized workload management
software for submitting compute-intensive jobs to
local computational clusters, which has been
described in detail elsewhere (Thain, Tannenbaum
and Livny, 2005). In our application, the GT4
submits a subset of parallel jobs to appropriate
Condor clusters, and it is up to the Condor software
to place them into a local queue, choose when and
where in the local cluster to run the jobs, monitor the
progress of the jobs, and ultimately inform GT4
services upon their completion.
3.3.3 DataMiningGrid High-Level Services
In addition to the core grid services provided by
GT4, other high-level WSRF compliant, ready-to-
use grid services have recently been developed
under the DataMiningGrid project. Here, we provide
a brief overview of the Resource Broker and the
Information Integrator Service. These services fully
support the parallel execution of a variety of data
mining (batch-style) programs in the grid
environment.
 The Resource Broker Service is responsible
for the execution of data mining programs
anywhere in the grid environment. It provides
a matching between the request for data
mining program execution, which is also
called a job in grid terminology, and the
available computational and data resources in
the grid. It takes as input the computational
requirements of the job (CPU power, memory,
disk space etc.) and data requirements of the
job (data size, data transfer speed, data
location etc.) and selects the most appropriate
execution machine for that particular job. The
job is passed on to the WS-GRAM service and
executed either on an underlying Condor
cluster or by using the GT4’s Fork
mechanism. The Resource Broker service is
capable of job delegation to resources
spanning over multiple administrative
domains. The execution machines are
automatically selected so that the inherent
complexity of the underlying infrastructure is
hidden from the users. The Resource Broker
service performs the orchestration of
automatic data and application transfers
between the grid nodes, using the GridFTP
component of GT4 for the transfers. The
Resource Broker is designed to execute multi-
jobs. Multi-jobs are collections of single jobs
that are bound for parallel execution. In
DataMiningGrid, a multi-job usually consists
of a single data mining program, which is
instantiated with different input parameters
and/or different input data sets. The individual
jobs are then executed in parallel on various
computational servers in the grid environment.
Each job, therefore, represents one execution
of a data mining program (i.e., an executable)
with specific input parameters and data inputs.
The Resource Broker makes extensive use of
the associated Information Integrator service.
 The Information Integrator Service provided
by DataMiningGrid operates in connection to
the MDS4 service provided by GT4. The
Information Integrator service is designed to
feed into other grid components and services,
including services for discovery, replication,
scheduling, troubleshooting, application
adaptation, and so on. Its key role is to create
and maintain a register of grid-enabled data
mining programs. By doing so, it facilitates
the discovery of grid-enabled programs on the
grid, and their later use through the Resource
Broker service.
4 AN APPLICATION
DESCRIPTION SCHEMA
A system whose main function is to facilitate the
sharing of grid resources within a grid environment
and supporting the development of distributed,
scalable data mining applications has to take into
account the unique constraints and requirements of
data mining programs with respect to the data
management, their execution requirements, and so
on.
In order to cope with the complexity of the
dynamically changing grid environment, an
Application Description Schema (ADS), which is a
novel metadata model in form of an XML schema,
was developed. The ADS defines properties
necessary to describe data mining programs and
other grid resources that may be shared in
distributed grid applications in a uniform way. For
example, the XML schema provides properties to
describe the data mining program and all its input
data, output data, parameter settings etc, so it can be
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used to describe any program (i.e., executable) in
general.
The ADS consists of two parts, which are:
 A common part, which can be used to describe
any program, i.e., not necessarily data mining
programs; and
 A data mining (domain-specific) part
containing additional information relating to
data mining programs, e.g., the program’s
application domain(s), the name of the atomic
algorithm, the problem solving technique etc.
The common part of the ADS is subdivided into:
 General part that contains definitions of
properties of the program, such as a unique
ID, the program’s name, vendor etc. This
information is used to build a grid wide
registry of available programs;
 Execution part contains definitions of
properties related to the execution of the
program. This includes the application type
(Java, C, Bash Shell or Python), the location
of the executable in the grid, list of libraries
required for execution etc.
 Application part provides definitions of
properties needed to use the program. This
includes options, data inputs and outputs,
parameter lists and loops, requirements,
environment variables etc.
The ADS adds a great range of functionality to
the system. It is used through the whole execution
process in the grid environment, beginning from the
registration of data mining and other programs
through program discovery, selection, parameter,
and input data specification to the actual execution
of the program on the grid.
5 GRID ENABLING DATA
MINING PROGRAMS
5.1 Batch-Style Programs
A data mining program is grid enabled when it is
made available in the grid environment so that the
grid users can actually share it and make use of it,
hence, the program may be considered a grid
resource. To cope with interoperability and other
high-level aspects it is necessary to provide an
extensive description of each data mining program
by means of the ADS.
From an infrastructural viewpoint, all
executables and their associated libraries represent
files. In DataMiningGrid, any data mining program
that can be invoked from command-line (and is
implemented to run without a graphical user
interface), can be grid enabled. A predominant
variety of general and domain specific programs can
be adapted to be invoked from a command-line. For
example, input data, output data, and parameter
settings can be presented to a program via the
command-line, using a specific format, e.g. a flag
followed by the associated value (‘flag <space>
value’, e.g., ‘-number 77’). Additionally the
program may have some system requirements like
minimum free disk space, minimum memory or
required operating system, which have to be
specified in order to later run the program on
appropriate computational machine(s) in the grid.
Execution of data mining algorithms in the grid
is based always on a valid description of the
prerequisites the algorithm needs. The ADS was
developed to describe the algorithms including all
their parameters, input/output data, necessary
environment prerequisites etc.
5.2 Grid-Enabler (Web) Application
By using the ADS schema, the developers can create
a very detailed description of their program, which
guarantees that it will run successfully and on the
other hand provides information for its discovery on
the grid. Providing the description will always rely
on the developer of the grid application, someone
who may not be acquainted with the underlying grid
system. The presented solution is a very simple
procedure: the actual data mining program (i.e.,
executable) is uploaded on a grid server and an ADS
instance that describes the program is prepared and
registered with the underlying Information Integrator
service.
To speed-up the grid-enabling process, the
DataMiningGrid project developed a Web
application which consists of several form-based jsp
web pages, leading the user through the whole
process of creating and uploading his data mining
program. The following Web pages are provided:
 General information
 Execution information
 Input data specification
 Output data specification
 Requirements specification
 Executable and libraries upload
5.3 Life-Cycle of the ADS Instance
The ADS instance file contains all invariant
properties of the respective data mining program
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(e.g., system architecture, location of the executable
and libraries, programming language). These
attributes cannot be altered by users of the system,
but are typically specified by the developer of the
program during the process of publishing the
program on the grid. The ADS instance also includes
default values for all options, but the exact values
are not set.
When querying for a data mining program, the
client side components (implemented as Triana
units) use the ADS instance in order to dynamically
create a GUI, which conforms to the description of
that particular data mining program. For example,
for each option a form field is generated, where the
user can specify the values for that option. At this
stage the user provides the exact values for the
applications parameters (during runtime, e.g.,
application parameter values, data input, additional
requirements) of the program.
A fully specified ADS instance represents a
multi-job description and is submitted to the
Resource Broker for parallel execution in the grid.
The Resource Broker uses the information
contained in the ADS instance to aggregate
appropriate resources. Particularly useful are the
following information:
 Static Resource Requirements. regarding
system architecture and operating system.
Applications implemented in a hardware-
dependent language (e.g., C) typically run
only on the system architecture and operating
system they have been compiled for (e.g.,
PowerPC or Intel Itanium running Linux). For
this reason, the Resource Broker has to select
execution machines that offer the same system
architecture and operating system as required
by the application.
 Modifiable Resource Requirements. memory
and disk space. While data mining
applications may require a minimal amount of
memory and disk space at start-up time,
memory and disk space demands typically rise
with the amount of data being processed and
with the solution space being explored.
Therefore, end users are allowed to specify
these requirements in accordance with the data
volume to be processed and their knowledge
of the application’s behaviour. The Resource
Broker will take into account these user-
defined requirements and match them to those
machines and resources that meet them.
 Modifiable Requirements. identity of
machines. In some cases end users may
generally wish to limit the list of possible
execution machines based on personal
preferences, for instance, when processing
sensitive data. To support this requirement, it
is possible for the user to specify the IPs of
such machines in the job description. Such a
list causes the Resource Broker to match only
those resources and machines listed and to
ignore all other machines independent of their
capabilities.
 The Total Number of Jobs. Instead of
specifying single values for each option and
data input that the selected application
requires, it is also possible to declare a list of
distinct values (e.g., true, false) or a loop (e.g.,
from 0.50 to 10.00 with step 0.25). These
represent rules for variable instantiations,
which are translated into a number of jobs
with different parameters by the Resource
Broker. This is referred to as a multi-job. As a
result, the Broker will prefer computational
resources that are capable of executing the
whole list of jobs at once in order to minimize
data transfer. Typically, such resources are
either clusters or high-performance machines
offering many distinct processors. As an
example, if the user specifies two input files
(a.txt, b.txt) for the same data input and two
loops running from 1 to 10 with step 1 as
parameters for two options, the Resource
Broker will translate this into 200 (2 x 10 x
10) distinct jobs. If no singe resource capable
of executing them at once is available, the
Broker will distribute these jobs over those
resources that provide the highest capability.
In addition, the Resource Broker evaluates
further information from the job description that
becomes important at the multi-job submission
stage. This information is briefly described below:
 Instructions. on where the program
executables are stored, including all required
libraries, and how to start the selected
program. These are required for transferring
executables and associated libraries to
execution machines across the grid, which is
part of the stage-in process. By staging-in
programs together with the input data
dynamically at run-time, the system is capable
of executing these applications on any suitable
machine in the grid without prior installation
of the respective data mining program.
 All Data Inputs and Data Outputs. that have
to be transferred prior the execution.
 All Option Values (Data Mining Program
Parameters). that have to be passed to the
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program at start-up. As the Resource Broker is
capable of scheduling executables that are
started in batch-mode from a command line, it
passes all option values as flag-value pairs.
Here, each flag is fixed and represents a single
option. The values, however, may change for
each call if a multi-job is specified.
6 GRID ENVIRONMENT
From this point forward the data mining program is
ready to be used in the grid execution environment.
6.1 Test Bed
The DataMiningGrid test bed was developed on the
bases of the grid middleware and ready-to-use grid
services discussed in the previous sections. It is a
grid service infrastructure, with services running at
various sites across different administrative domains
in three European countries (Ireland, Slovenia and
Germany).
The test bed provides a number of capabilities,
the most important being the following:
 The ability to execute a variety of batch-style
programs, at any appropriate
computational server in the grid. Over 20
grid-enabled programs are currently stored in
executable repositories on various grid servers
in the test bed. These programs may be
combined in complex-workflows, containing
several multi-jobs executed sequentially;
 Meta-scheduling, i.e., dynamic and automatic
allocation of optimal computational servers in
the grid environment is achieved through the
use of the Resource Broker, the Information
Integrator service and MDS4.
 Program and data movement across
different administrative domains is
achieved through the use of the GridFTP and
RFT services.
In addition to these, grid environments based on
GT4 and DataMiningGrid high-level services have a
number of other capabilities, such as a Grid Security
Infrastructure, which is described in Stankovski et
al. (2008a, 2008b). Over 20 grid-enabled data
mining programs were already combined into large
scale data mining applications. To demonstrate the
flexibility and extensibility of the developed
software, we have developed applications for
various research and industrial sectors, such as
bioinformatics, industry (text-mining), medicine,
open publishing and digital libraries, civil
engineering. For example, grid-enabled Federated
Digital Libraries have been described by Trnkoczy,
Turk, and Stankovski (2006), and Trnkoczy and
Stankovski (2008).
7 CONCLUSIONS
It seems obvious that emerging large-scale data
mining applications shall rely increasingly on
distributed computing environments.
Many data mining programs require a repeated
execution of the same process with different
parameters that usually control the behaviour of the
implemented data mining algorithm or different
input data sets. This is typically required in
optimization or sensitivity analysis tasks. Hence,
properties like performance, scalability, usability
and security are critical for this kind of applications.
The DataMiningGrid system was developed to
address these requirements. More details about the
results of this project can be found at its web site
(DataMiningGrid). The developed software is
distinguished by its flexibility, ease of use,
conceptual simplicity, compliance with emerging
grid and data mining standards, and the use of
mainstream grid and open technology.
The DataMiningGrid project developed a
coherent framework, which offers data miners, who
are usually not grid experts, the ability to easily grid
enable existing, stand-alone data mining programs,
construct complex data mining tasks, which are
represented by complex Triana workflows and
execute these workflows in a grid environment. A
case study on the extensibility of the
DataMiningGrid platform is given in the literature
(Wegener and May, 2007).
The developed DataMiningGrid software is
freely available under the Apache Open Source
License V2.0 via SourceForge.net, including all
supporting documentation.
Despite its promise, however, there are still a lot
of issues to be resolved before grid technology is
commonly applied to large-scale data mining tasks.
(Stankovski and Dubitzky, 2007).
ACKNOWLEDGEMENTS
This work was supported by the European
Commission FP6 grant DataMiningGrid,
http://www.datamininggrid.org, contract no. 4475.
The collaboration of all project partners is
BUILDING SCALABLE DATA MINING GRID APPLICATIONS - An Application Description Schema and Associated
Grid Services
227

acknowledged. They have jointly participated in
developing the system.
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