A WORKFLOW BASED APPROACH FOR KNOWLEDGE GRID
APPLICATION
Marcello Castellano and Cesaria Digregorio
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via E. Orabona, 4 - 70125, Bari, Italy
Keywords: Grid Computing, Middleware, Workflow, Knowledge Discovery, Knowledge Grid Workflow, Knowledge
Grid Workflow Management System.
Abstract: An expanding area of study regards the development of computing platforms able to provide easy online
access to geographically distributed data collections with the aim of producing knowledge synthesis from
that data. Computational grid technology can form a computing infrastructure which can assist in providing
a platform for distributed computing. In this paper is presented a case study to propose a Knowledge-Grid
Workflow Management System as a middleware solution to develop a Knowledge Grid application.
1 INTRODUCTION
The first decade of the new millennium has been
called the Data Decade due to the fact that data
growth is surpassing computational growth, and
many of the more important advances in science and
engineering originate from analyses and syntheses of
large data collections. Biomedical images, protein
deposits, digital maps and research into the building
blocks of matter are just a few examples of data
collections that could furnish raw data to online
searches and data analyses (F. Berman, 2001). The
activity that must be carried out on the data is
connected to the way in which they are retrieved and
to the ability to analyse them in order to create a
synthesis or, better yet, to transform the data into
something useful, knowledge for the user. Hence,
the development of computing platforms able to
furnish easy online access to geographically
distributed data collections with the aim of
producing scientific knowledge through simulation,
phenomena modelling, and the results of data
analyses is an area undergoing rapid expansion.
There are several methods, strategies and
techniques for accessing and synthesising
knowledge from data as mining and inference.
However, satisfying requests for knowledge often
involves coordinating several tasks and orchestrating
different contributions, communications and
components of information retrieval and syntheses
(S. Sarnikar, 2007). Today, grid technologies are the
most promising answer for the creation of applied
scenarios with scalable performance and a large
capacity for sharing both physical and logical
computing resources. A Knowledge-Grid is the
convergence of a comprehensive computational
infrastructure with scientific data collections and
applications for routinely supporting the synthesis of
knowledge from that data. Grid Computing
applications can become truly operative through the
development of a middleware which operates
between the grid system and the user’s application.
In this paper, we propose the use of a
Knowledge-Grid Workflow Management System as
a middleware which both allows the user to specify
the model of knowledge synthesis desired and
transparently implement it, distributing the workload
suitably onto the Knowledge-Grid system in use. In
particular section 2 briefly discusses concepts as
knowledge and related synthesis techniques, grid
systems, the Knowledge-Grids and workflow.
Section 3 presents functional models of Grid
Workflow and Knowledge-Grid Workflow. Section
4 discusses a case study which presents a workflow
model for knowledge synthesis applied to text
mining to automatically extract data related to
symptoms and pathologies from a repository of
scientific documents.
230
Castellano M. and Digregorio C. (2009).
A WORKFLOW BASED APPROACH FOR KNOWLEDGE GRID APPLICATION.
In Proceedings of the International Conference on Knowledge Management and Information Sharing, pages 230-235
DOI: 10.5220/0002331702300235
Copyright
c
 SciTePress

2 BACKGROUND
In general, knowledge can be experience, concepts,
values, or beliefs that increase an individual’s ability
to take effective action (F. Zheng, 2008).
Knowledge can be either implicit or explicit. The
former is represented by tacit experience which can
come through individual ideas, intuition, experience,
values and judgements. This type of knowledge is
dynamic in nature. It can be accessed only through
direct participation and communication with field
experts that possess this knowledge. The know-how
of each knowledge worker is accordingly based on
this tacit (or implicit) knowledge. Instead, explicit
knowledge usually includes anything that is saveable
in an electronic format or in other words what we
are able to transcribe and to share.
Knowledge discovery can be defined as “the non-
trivial extraction of implicit, unknown, and
potentially useful information from data. When
working with texts, knowledge discovery refers
generally to the process of extracting interesting
information from a large amount of unstructured
textual documents. The goal of this process is to find
and extract useful patterns. To do this, specific
methods and algorithms from the fields of machine
learning and statistics are applied. Text mining is,
thus, the application of these algorithms and
methods to texts ”(U.M. Fayyad, 1996).
Grid is an infrastructure which allows shared
resources to be coordinated inside dynamic
organisations, be they individuals, institutions or
resources. It offers a flexible environment where
resources can be dynamically reorganised without
altering any active processing on the GRID and
provide connectivity for data distributed in different
locations. This can resolve transparency problems
related to location while providing a mechanism
which allows easier access to and management of
distributed data as well as the virtualisation and
sharing of GRID connected resources. To
manipulate intensive computation procedures, the
platform can provide automatic allocation of
resources, scheduling and algorithm implementation
in relation to the availability, capacity and position
of these resources. A GRID can increase efficiency
while reducing the cost of computational networks
by decreasing data processing times, optimizing
resources, and distributing the workload. Thus, users
are provided the results of large operations with
greater speed and lower costs (I. Foster, 2001).
Attempts to automate knowledge processes date
to the early 1980s. Several processes have been
employed on parallel computing platforms to
achieve high performance on the analysis of large
data sets stored on a single site. Recently, the
demand for knowledge processes has expanded to
include the management and analyses of multi-site
and multi-owner data repositories. This task involves
large data-sets, the geographic distribution of data,
users and resources, and computational intensive
analysis demands for new parallel and distributed
platforms for knowledge processes as computational
grid technology. The resulting application of grid
technology to the knowledge field has been termed
Knowledge-Grid (M.Cannataro, 2001).
Workflow automation technology has been
developed to facilitate organizational coordination
and collaboration by automating entire work
processes and controlling the flow of information
among participants. A workflow can be used to
define the work process, control activity requests,
route relevant documents to the appropriate agents,
enforce deadlines, and monitor the progress of work
(S. X. Sun, 2008). The Workflow Management
Coalition (WFMC) defines a workflow as “… the
total or partial automation of business procedures
where documents, information or tasks are passed
between participants according to a defined set of
rules …” (www.wfms.com). A business process is a
group of necessary tasks and a set of conditions
which determines the order of their completion. A
task is a logical unit of work that must be performed
by a resource in its entirety. A resource can be a
person or machine or it can be a group of persons or
machines that perform specific tasks. The
performance of a task by a resource is called an
activity (Wil van der Aalst, 2002).
Hence, a workflow can be seen as a structure which
not only contains tasks/activities but also
coordinates and supervises their execution.
Different types of workflow have been identified:
Collaborative workflows manage less rigid
processes and allow connections among those users
closest to the collaboration as well as work groups;
Structured workflows manage structurally well-
defined and repeatable activities which can be
specified through a series of rules. Examples of
structured workflows are: (a) administrative
workflows which manage the flow of electronic
forms, integrating them with message systems or
email; and (b) production workflows which manage
the flow of well-structured work, defined by well-
formalised rules and dependencies;
Ad Hoc workflows are created by using lighter
systems which give the user the task of identifying
the correct procedural steps to take each time a
A WORKFLOW BASED APPROACH FOR KNOWLEDGE GRID APPLICATION
231

dynamic modification is required in the performance
procedure.
In other words, workflows are a specific kinds of
processes, whose transitions between activities are
controlled by an information system called a
Workflow Management System (E. A. Stohr, 2001).
Workflow Management Systems (WfMS) are a
new type of computer technology which aid the
automation of business procedures. More
specifically, a WfMS defines, manages and performs
workflow through the use of software
(www.wfms.com). This software can also be
considered a middleware which connects an
organisation’s offices and legacy applications (E. A.
Stohr, 2001). A WfMS forms a bridge between
those who are required to develop the given tasks
and the computer applications which are the
operating tools used by the people to develop each
activity. It has been designed to produce work which
can be monitored and to encourage communication
among the different parts [Wil van der Aalst, 2002].
WfMSs can be schematically outlined in three
functional areas (Fig. 1):
1. The built-time modelling operation which
defines and eventually modifies the task or
activity assigned.
2. The runtime workflow enactment service
operation which manages workflow
procedures in an operative environment and
orders the various activities to be managed as
part of a procedure.
3. The interactions amongst the runtime, the
human user and the IT application tools in
order to control the activities.
Business Process Analysis,
Modelling & Definition Tools
Process
Definition
Workflow Enactment Service
Applications
& IT Tools
Process Design
& Definition
Build Time
Run Time
Process
changes
Interaction with
Users &
Application Tools
Figure 1: Workflow Management System characteristics.
The ability to distribute tasks and information
between participants is a major distinguishing
feature of workflow runtime infrastructure. The
distribution function may operate at a variety of
levels (workgroup to inter-organisation) depending
upon the scope of the workflows. It may also use a
variety of underlying communications mechanisms
(electronic mail, message passing, distributed object
technology, etc) (Fig. 2) (www.wfms.com).
Figure 2: Distribution within the workflow enactment
service.
WFMSs bring with them a series of advantage like
increased efficiency, reduced time and errors,
improved control over procedures through the
standardisation of work methods, and the availability
of verification tools.
3 KNOWLEDGE-GRID
WORKFLOW
In recent years, several techniques for workflow
management have been developed, especially, in the
business field and any of these approaches have
been investigate for Grid Workflow applied in
scientific fields (J. Yu, 2005). A Grid Workflow is a
collection of tasks processed in a well-defined order
on distributed resources. WfMSs which define,
manage and perform workflow for Grid applications
are constantly expanding. The systems offer many
advantages: the possibility of dynamically building
applications which take advantage of distributed
resources; the use of resources located in different
areas to improve data treatment or reduce the
performance costs; the ability to perform extended
runs in different administrative areas to obtain
specific procedure capacities; the integration of
groups involved in the management of different
aspects of an experiment which make up a
workflow, encouraging collaboration between
organisations (
J. Yu, 2005).
Figure 3 shows the functional architecture and
features of components of the Grid-based workflow
system model proposed by the WfMC. The user
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interacts with tools that produce a workflow model
(Workflow Design & Definition) which then
undergoes the implementation service (Workflow
Execution & Control). This service provides a series
of functions as scheduling, fault tolerance, and data
movement. It is built on a Grid middleware through
which the Grid resources can be used. Both in the
model building phase and in that of execution, the
resource and application information is retrieved by
examining the Grid services dedicated to the
information (J.Yu, 2005).
Grid Workflow Application Modeling & Definition Tools
Grid Workflow
Specification
Workflow Scheduling
Data Movement
Fault
Management
Grid Workflow Enactment Service
Grid Middleware
Grid Resources
Resource Info Service
Application Info Service
Grid Information Services
workflow change
Interaction with
Grid resources
Workflow Execution
& Control
Run Time
Build Time
Workflow Design &
Definition
Grid Users
Figure 3: Grid Workflow Management System.
Currently, knowledge sharing and knowledge
transfer processes require the manual integration and
organization of information channels supported by a
fragmented set of tools such as email, discussion
forums, search engines, etc. The lack of an approach
that can model knowledge flows and coordinate the
working of various tools to automate them is a major
drawback and deterrent to knowledge sharing and
improved knowledge worker productivity in
organizations (Bruno 2002; Moore and Rugullies
2005).
The automation of knowledge flow requires the
integration of information retrieval mechanisms with
workflow systems. While information retrieval
mechanisms provide discovery and matching
services, workflow systems coordinate the call for
the appropriate intelligent service and automate the
routing and delivery of messages and documents.
Specifically, given a set of user specified constraints,
a workflow can be designed so that it can
automatically invoke intelligent services, contact
experts, retrieve documents and present the results to
the user (S. Sarnikar, 2007).
A knowledge workflow has several unique features
when compared to a generic workflow. For example,
the number of parallel activities needed to locate
experts and databases is decided dynamically at
runtime and is not modelled a priori as in typical
production workflows. Similarly, the user’s choice
of activities following a response cannot be
modelled statically beforehand and is instead
represented by a ‘model update point’ in the
knowledge workflow.
Although knowledge workflows are similar to
structured business procedures in some aspects,
there are some main differences to perform them
using existing WfMSs. The first problem is that in a
typical business procedures the processing of orders,
the flow of control and the data flow are all
predetermined. Knowledge flow is “ad hoc” by
nature and evolves on the basis of user requests and
on the limits of systems that are difficult to define a
priori. The second difficulty is that the assemblage
of Knowledge flow models is carried out at the
moment of execution. Thus, it is necessary to
combine the process modelling and the workflow
management enactment function with the
information modelling and delivery mechanisms of
knowledge management (E. A. Stohr, 2001).
Figure 4: KWMS architecture.
A Knowledge Workflow Management System
(KWMS) can be implemented by extending the
WfMC architecture of workflow management
systems with two knowledge workflow specific
components: (1) a pattern-based knowledge flow
model generator and (2) an IR System (see fig. 4).
The knowledge workflow modeller is used to
develop knowledge workflow models by assembling
basic knowledge workflow patterns to meet the
requirements of a specific knowledge request. The
IR
System engine consists of an information retrieval
engine and an expertise locator engine. The
information retrieval engine is used to execute
functions such as document recommendation,
aggregation, filtering and other retrieval related
functions (S. Sarnikar, 2007).
4 A CASE STUDY
The Knowledge-Grid Workflow application here
taken into account is designed to extract new and
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useful information about symptoms and pathologies
from a large collection of unstructured scientific
documents (M.Castellano, 2009). This is a frequent
study activity in biomedicine called bio-entity
recognition and it is receiving ever greater attention.
Bio-entity recognition aims to identify and classify
technical terms corresponding to the instances of
concepts that are of interest to molecular biologists.
Examples of such entities include the names of
proteins, genes, their location of activity (i.e., the
names of cells or organisms), drugs, symptoms,
pathologies and so on. Entity recognition is
becoming increasingly important with the massive
increase in reported results due to high throughput
experimental methods (H. Shatkay, 2003).
1. The first workflow step is the creation of a
Textual Data Repository from unstructured texts
such as scientific medical publications, abstracts
of clinical articles, or parts of texts concerning
health services. Mining large document
collections requires pre-processing the text
documents and storing the information in a data
structure since the latter is more appropriate for
further processing than a plain text file. Once a
Textual Data Repository is created, the
Knowledge Discovery in Text technique can be
applied. The Knowledge Discovery in Text
process is consists of Text Refining and Text
Mining phases.
2. Text Refining input are not-structured data such
as texts or semi-structured data like HTML
pages. This next step requires dividing the
documents into a stream of words by removing
all punctuation marks and by replacing tabs and
other non-text characters with single white
spaces. Articles, conjunctions, prepositions, etc.
must be removed from the documents. Text
Refining output can be stored in a database,
XML file or in other structured forms which are
referred to as an Intermediate Form and Text
Mining techniques are applied to this Form.
At this point, our Knowledge Workflow is broken
down into three Text Mining sub phases: document
clustering, document categorization, and pattern
extraction.
3. Document clustering is the assignment of a
multivariate entity to a few categories (classes,
groups) previously undefined. The goal is to
gather together similar entities. Textual
clustering is used as an automatic process which
divides a collection of documents into groups.
Inside these groups, the documents have
similarities based on selected characteristics:
author, length, dates, and keywords. Textual
clustering can be used to provide a planning of
the contents of document collections, to identify
hidden similarities, to facilitate the process of
browsing and to find correlated or similar
information. If the clustering works with
keywords or features that represent the semantics
of the documents, the identified groups will be
distinguished on the basis of the different topics
being discussed in the corpus.
4. Document categorisation requires that the objects
must be attributed to one or more class or
category which will already have been identified.
Classification is the process in which
meaningful correlations among frequent data are
identified. There are association rules for Text
Categorization. All algorithms operate in two
phases to produce association rules. First, all the
whole keywords with greater or equal support
with respect to the reference are listed to create
what is called the frequent set. Then, all the
association rules, that can be derived from the
frequent set and that satisfy the given confidence,
are established.
5. Pattern Extraction is the identification of some
patterns following the analysis of associations
and tendencies. The discovery of associations is
the process in which meaningful correlations
among frequent whole data are found.
At the end of Text Mining, the analysis of a large
collection of biomedical papers will allow us to
extract biological entities related to symptoms and
pathologies. The Knowledge-Grid Workflow
described above is illustrated in fig.5:
Figure 5: Example of a Knowledge-Grid Workflow used
for Symptom and Pathology Discovery.
Application problems often occur in data-intensive
situations based on knowledge methods. These
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234

circumstances require that the same logic be applied
to a large collection of different data which are
independent from each other. Hence, the limitations
will be technological if these problems are addressed
with traditional machines that sequentially perform
the same set of instructions on an entire collection of
homogeneous and independent data. The time
required for execution will also increase according
to the size of the collection and this will become the
limiting factor in these applications (M.Castellano,
2009). Therefore, the creation and management of a
dynamic, multi-step workflow is necessary. Our
workflow model represents a knowledge synthesis
process concerning the discovery of terms which
describe medical symptoms and pathologies. It can
be generated and executed by a Knowledge-Grid
Workflow Management System which automated
both the model building procedure as well as its
execution, guided by the Grid technology. In
particular, by retrieving the Grid Workflow
architecture, it was possible to interact with tools
that first generated a workflow model (Workflow
Design & Definition) and then underwent an
execution service (Workflow Execution & Control).
When the Knowledge Workflow model is being
defined and also during the execution phase, the
Grid services can be examined and the availability
of resources can be verified. It should be noted that
the resources allowed us to perform the various
phases of the Knowledge Workflow or, better, the
phases of Text Refining and Text Mining which
resulted in the extraction of the terms describing
symptoms and pathologies.
5 CONCLUSIONS
The development of computing platforms able to
provide easy online access to geographically
distributed data collections with the aim of
producing knowledge synthesis from that data is
showing an increasing interest. The complexity of
the knowledge process find the operational answer
through the use of workflow technologies. In this
paper has been presented a case study based on
automatic extraction of symptoms and pathologies in
order to expose the workflow approach for
knowledge-Grid. The Knowledge-Grid Workflow
Management System is a suitable middleware that
allow both an high level design of the whole
Knowledge process and the execution of it having a
set of tools already configured and running in a Grid
based environment.
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