SEMANTIC WEB SERVICES FOR COMPUTATIONAL MECHANICS                                        by                             Th...
ii
TABLE OF CONTENTSChapter   Title                                                           Page          Title Page       ...
6.1.1 Specialization Systems                          41        6.1.2 Declarative Descriptions                        42  ...
LIST OF FIGURESFigure   Title                                                                 Page 3.1     Problem Decompo...
7.1    An Overview of the Proposed Semantic Web Services for Com-       putational Mechanics Framework                    ...
LIST OF TABLESTable   Title                                                                Page 5.1    Comparison between ...
viii
CHAPTER 1                                     INTRODUCTION1.1   Background         In performing the analysis of structure...
are technologies that “will transform the web from a collection of information into a dis-tributed computational device” (...
the agent would consult its ontology to identify the parameters required for a typical  stress analysis, which are the nod...
1.2   Problem Statement       Semantic Web Services, which is the combination of Web Services and the SemanticWeb technolo...
2. To capture and construct the process ontologies in computational mechanics such as      discretization, formulation of ...
tions can cooperate on analysis tasks in a unified manner using the shared conceptualizationsdefined by ontologies. This fra...
CHAPTER 2           FEM AND THE NEED TOWARDS DISTRIBUTED COMPUTING2.1   Formulation of the Finite Element Method         T...
1 ν                                                                     0                E            E=        ν 1    ...
Substitution of (2.3) and (2.8) into (2.1) yields                                    1 numel T          numel             ...
Accuracy of the finite element methods depends on many factors such as the basisof shape functions used for interpolation, ...
computers may not be powerful enough to solve a problem. One parallel supercomputer maynot deliver enough power for real-t...
12
CHAPTER 3                              DISTRIBUTED COMPUTING3.1   Distributed Computing Concepts       Distributed computi...
Processor A         Processor B         Processor C                  Figure 3.1: Problem Decomposition: Data Distributiont...
Process A             Process B             Process C  Figure 3.2: Problem Decomposition: Algorithmic Distribution    Proc...
Process A1              Process B1              Process C1                  Process A2              Process B2            ...
grow centralized computing cannot be used to solve the problems efficiently. Researchersand practitioners have then turned ...
3.3.2 Grid Computing         Grid computing is a recent field in distributed computing. The term grid was intro-duced in th...
Figure 3.5: Model of the NASA X-38 Crew Return Vehicle (Barnard et al., 1999)   Figure 3.6: Mach Contours for the X-38 Cre...
munity has also formed their own organization called the Global Grid Forum. Currentlywith more than 5,000 members world-wi...
CHAPTER 4                                          WEB SERVICES4.1     The Web Services Architecture         Web Services ...
Find                  Service Registry            Publish                 Service                           Bind         S...
Service Publication/Discovery                         UDDI                 Service Description                            ...
SOAP and WSDL, two important components of Web Services, are adopted in this standard.Since there are already a significant...
CHAPTER 5                                        THE SEMANTIC WEB5.1   General        The World Wide Web has turned into a...
<html>       <head>         <title>Material Properties</title>       </head> 5     <body>         <b>Mechanical properties...
<rdf:RDF      xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">      <rdf:Description about="http://www.computings....
5.2    Resource Description Framework (RDF)5.2.1 Introduction        The Resource Description Framework (RDF) is an XML ap...
http://www.computings.org/index.html                                http://purl.org/dc/elements/1.1/creator               ...
http://www.computings.org/index.html                                                                 http://purl.org/dc/el...
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal
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Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal

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Issue Date: Nov-2003

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Publisher: Asian Institute of Technology

URI: http://dspace.siu.ac.th/handle/1532/130

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Semantic Web Services for Computational Mechanics : A Literature Survey and Research Proposal

  1. 1. SEMANTIC WEB SERVICES FOR COMPUTATIONAL MECHANICS by Thiti VacharasintopchaiA dissertation proposal submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering Examination Commitee: Dr. William Barry (Chairman) Prof. Worsak Kanok-Nukulchai Prof. Vilas Wuwongse Dr. Voratas Kachitvichyanukul Nationality: Thai Previous Degrees: B. Eng. (Civil Engineering) Chulalongkorn University Bangkok, Thailand M. Eng. (Structural Engineering) Asian Institute of Technology Bangkok, Thailand Scholarship Donor: Royal Thai Government Followship Asian Institute of Technology School of Civil Engineering Thailand November 2003 i
  2. 2. ii
  3. 3. TABLE OF CONTENTSChapter Title Page Title Page i Table of Contents iii List of Figures v List of Tables vii 1 Introduction 1 1.1 Background 1 1.2 Problem Statement 4 1.3 Objectives 4 1.4 Scope 5 1.5 Research Approach 5 1.6 Contributions 5 2 FEM and the Need towards Distributed Computing 7 2.1 Formulation of the Finite Element Method 7 2.2 Parallel Computing and Applications in Computational Mechanics 10 2.3 Towards an Application of Distributed Computing Technique 10 3 Distributed Computing 13 3.1 Distributed Computing Concepts 13 3.2 Methods of Problem Decomposition 13 3.2.1 Data Distribution 13 3.2.2 Algorithmic Distribution 14 3.2.3 Load Balancing 16 3.3 Applications in Scientific Computing 16 3.3.1 SETI@home Project 17 3.3.2 Grid Computing 18 4 Web Services 21 4.1 The Web Services Architecture 21 4.2 Applications of Web Services in Scientific Computing 23 5 The Semantic Web 25 5.1 General 25 5.2 Resource Description Framework (RDF) 28 5.2.1 Introduction 28 5.2.2 An RDF Statement 28 5.2.3 Identification of Resources 28 5.2.4 The RDF Model 28 5.2.5 Defining RDF Vocabularies 29 5.3 DAML+OIL 31 5.4 DAML-S: Semantic Markup for Web Services 36 5.5 Inferences – Making Use of Resources and Ontologies 38 6 XML Declarative Description 41 6.1 Declarative Description Theory 41 iii
  4. 4. 6.1.1 Specialization Systems 41 6.1.2 Declarative Descriptions 42 6.1.3 Semantics of Declarative Descriptions 42 6.1.4 Equivalent Transformations 43 6.2 XML Declarative Description 44 6.2.1 XML Elements and XML Expressions 44 6.2.2 Formulation of XML Declarative Description 45 6.2.3 XML Equivalent Transformation 46 6.3 Ontology Modeling and Inference with XDD 497 Methodology 55 7.1 A Semantic Web Services Framework for Computational Mechanics 55 7.2 An Overview of the Research Tasks 58 7.3 Infrastructure Design and Development 58 7.3.1 Construction of Domain Ontologies 58 7.3.2 Construction of Ontology Mapping Facilities 59 7.3.3 Construction of Service Enactment Facilities 62 7.4 Application Web Services Development 63 7.4.1 Design of XML Schemas and Ontologies 65 7.4.2 Construction of DAML-S Ontology Instances and WSDL Documents 65 7.4.3 Implementation and Deployment of Application Web Services 66 7.5 Illustrative Applications of the Framework 66 References 83 Index 91 iv
  5. 5. LIST OF FIGURESFigure Title Page 3.1 Problem Decomposition: Data Distribution 14 3.2 Problem Decomposition: Algorithmic Distribution 15 3.3 Problem Decomposition: Hybrid Distribution Method 1 15 3.4 Problem Decomposition: Hybrid Distribution Method 2 16 3.5 Model of the NASA X-38 Crew Return Vehicle (Barnard et al., 1999) 19 3.6 Mach Contours for the X-38 Crew Return Vehicle (Barnard et al., 1999) 19 4.1 Conceptual Web Services Model (Basha et al., 2002) 22 4.2 Roles of SOAP, WSDL and UDDI in Web Services Architec- ture (Basha et al., 2002) 23 5.1 Example of an HTML Document 26 5.2 HTML Document in Figure 5.1 as Rendered on a Web Browser 26 5.3 Example of an RDF Description 27 5.4 A Simple RDF Statement (adapted from W3C, 2003b) 29 5.5 Several RDF Statements about Resources (adapted from W3C, 2003b) 30 5.6 A Vehicle Class Hierarchy (adapted from W3C, 2003b) 31 5.7 An RDF/XML Encoding that Corresponds to Figure 5.6 (W3C, 2003b) 32 5.8 An Instance of a Class Defined in Figures 5.6 and 5.7 (W3C, 2003b) 32 5.9 Definition of Properties Corresponding to Classes in Figures 5.6 and 5.7 (adapted from W3C, 2003b) 33 5.10 An Instance of a Class using Properties Defined in Figure 5.9 (W3C, 2003b) 34 5.11 An Example of daml:Restriction Construct 36 5.12 Upper Levels of DAML-S Ontology (DAML-S, 2003b) 39 5.13 Semantic Web Stack Diagram (W3C, 2002) 40 5.14 Definition of XML Namespace 40 6.1 Typical Structure and Syntax of an XET Program (Anutariya et al., 2002) 48 6.2 XDD Description Modeling the Ontology Definitions of Class Person (Suwanapong, 2001) 50 6.3 XDD Description Modeling the Ontology Instances of Class Person (Suwanapong, 2001) 50 6.4 XDD Description Modeling the Ontology Axiom daml:inverseOf (Suwanapong, 2001) 51 6.5 An XET Program Corresponding to the XDD Descriptions in Figures 6.2 to 6.4 (Part 1 of 2) 52 6.6 An XET Program Corresponding to the XDD Descriptions in Figures 6.2 to 6.4 (Part 2 of 2) 53 6.7 Information Derived from the XDD Descriptions in Figures 6.2 to 6.4 (Suwanapong, 2001) 54 v
  6. 6. 7.1 An Overview of the Proposed Semantic Web Services for Com- putational Mechanics Framework 677.2 The Multi-tier System Architecture adopted in the SWSCM Framework (adapted from Cyran, 2002) 687.3 Example of a Material Ontology 697.4 The DAML-S Ontology 707.5 A Service Profile Hierarchy for Computational Mechanics Ap- plication Web Services 717.6 A Hierarchy of Matrices Involved in Computational Mechanics 717.7 DAML-S Ontology Instance Describing Science.net Matrix Inversion Service 727.8 DAML-S Ontology Instance Describing NumMethods.org Ma- trix Inversion Service 727.9 DAML-S Ontology Instance Describing Optimize.com Matrix Inversion Service 737.10 DAML-S Ontology Instance Describing NumericalRecipe.net Matrix Inversion Service 737.11 WSDL Description of a Matrix Inversion Web Service by Num- Methods.org (adapted from Sintopchai et al., 2003) (Part 1 of 2) 747.12 WSDL Description of a Matrix Inversion Web Service by Num- Methods.org (adapted from Sintopchai et al., 2003) (Part 2 of 2) 757.13 Model of a Structure to be Analyzed by a Structural Analysis Agent 767.14 Example of an Input Data that Represents the Model in Fig- ure 7.13 (Part 1 of 2) 777.15 Example of an Input Data that Represents the Model in Fig- ure 7.13 (Part 2 of 2) 787.16 DAML-S Ontology Instance Describing Finite Element Ser- vice by Structural Analysis Agent B 797.17 Preliminary Schedule for the Proposed Research Tasks 80 vi
  7. 7. LIST OF TABLESTable Title Page 5.1 Comparison between RDF(S) and DAML+OIL (DAML, 2002) 34 6.1 Definition of the XML Expression Alphabet (Wuwongse et al., 2000) 45 6.2 Definition of the Basic Specialization Mapping Operator νX (Wuwongse et al., 2000) 47 7.1 Examples of Mathematical and Physical Ontologies Related to Key Operations in Computational Mechanics 60 7.2 Examples of Conceptual Ontologies Related to Key Opera- tions in Computational Mechanics 61 7.3 Examples of Axioms Related to Key Operations in Computa- tional Mechanics 61 7.4 Summary of Matrix Inversion Service Profiles in Figures 7.7 to 7.10 63 7.5 Preliminary List of Application Web Services to be Developed 64 7.6 Expenditure Estimates for the Proposed Research 81 vii
  8. 8. viii
  9. 9. CHAPTER 1 INTRODUCTION1.1 Background In performing the analysis of structures using numerical methods, computers mustbe closely guided by human users. For example, consider the case of finite element anal-yses, at first we have to examine a real world problem and model it as a problem domainand boundary conditions subjected to applied forces. We may have to consult many designcodes or experimental results to obtain the forces for the analysis. Next, we have to discretizethe problem domain, either manually or through the help of automatic meshing software be-fore letting the computer compute and assemble the element stiffness matrices, apply theboundary conditions and solve for nodal solutions. We also have to use our experience andjudgment to select the element types and the constitutive models for the material and problembeing considered, examine the accuracy of the analysis results and, if necessary, repeat theentire process to obtain accurate results. Moreover, for large and complex analyses, runningan entire process may take hours or days and special visualization techniques such as ani-mating graphics or virtual reality may be needed to effectively interpret the analysis results.This situation may be more complicated in the case that there exist incompatibilities betweeninput and output data format of software components employed, and may be tedious, take alot of time, and lead to inaccurate analyses if performed by inexperienced users. Solutions are available to improve the performance and user-friendliness of structuralanalysis software. One could be modifying or inventing a new formulation of the analysismethod. Another could be using computer technologies to improve computing performanceand the interaction between computers and users. One example of the former is the develop-ment of meshless methods such as the element-free Galerkin method (EFGM) (Belytschkoet al., 1994) which is an analysis procedure that avoids the need for meshing by employinga moving least-squares technique (Lancaster and Salkauskas, 1981) in approximating thefield quantities of interest. An example of the latter is the application of parallel computingtechniques into the finite element analysis procedure, cf. Adeli and Kamal (1993) and Ya-gawa et al. (1991), so that complex analyses are performed in shorter time. Improvementof user-friendliness in structural analysis software may come at the cost of an increase incomputational requirement, thus in some cases combination of the two solutions is also con-sidered. For example, a parallel computing technique was employed to make analyses usingEFGM analyses more practical to users (Barry and Vacharasintopchai, 2001b). Distributed computing technologies, in particular, Web Services (W3C, 2003d) andthe Semantic Web (Berners-Lee et al., 2001), are available in computer science to help unifyand utilize the scattered resources, such as personal computers, clusters, supercomputers,databases or knowledge-bases, and can be applied in structural engineering to make numer-ical analysis of structures performed in a fast, accurate and more automated manner. WithWeb Services and the Semantic Web, we wil not have to be actively involved in the analysesby feeding the computers all the data and elaborate instructions, but instead be involved in aless active way by giving simple instructions to the computers, watching them work for us,and making a final decision on the results. With the advent of high-speed networks and thematurity of researches in artificial intelligence, the Internet is not just a hyper-library for usto search for information nor a communication tool for us to send emails or messages. Wecould use the Internet as a very large platform for scientific computations with the help ofintelligent software agents that collaborate to accomplish a given task. Web Services and the Semantic Web, two technologies built on top of the Internet, 1
  10. 10. are technologies that “will transform the web from a collection of information into a dis-tributed computational device” (Fensel and Bussler, 2002). Web services are a new breedof Web application that enables collaboration on the Web. They are self-contained, self-describing, modular applications that can be published, located, and invoked across the Web.Web services perform functions, which can be anything from simple requests to complicatedprocesses. Once a Web service is deployed, other applications (and other Web services) candiscover and invoke the deployed service (Tidwell, 2000). The Semantic Web, on the otherhand, is a technology that enables computers accessing data on the Web to be intelligent. Upto now, the World Wide Web has developed rapidly as a medium of documents for peoplerather than of information that can be manipulated automatically by computers. By augment-ing Web pages with data targeted at computers and by adding documents designed solely forcomputers, the Web will be transformed into the Semantic Web where computers can find themeaning of semantic data by following hyperlinks to definitions of key terms and rules forreasoning about them logically (Berners-Lee et al., 2001). The combination of Web Servicesand Semantic Web technologies, termed Semantic Web Services (McIlraith et al., 2001), willenable automated discovery, execution, composition, and interoperation of web services toaccomplish any tasks requested by human users. Automation is not achieved by humanshard-coding programs into software agents but rather by the agents themselves through rea-soning processes that lead to understanding. Such reasoning and automated operation arebased on ontologies, which are the formal, explicit specifications of shared conceptualiza-tions of the world (Broekstra et al., 2002) and, like web pages, can be individually publishedand linked to other ontologies across the Internet. In terms of numerical analysis of structures, Semantic Web Services may be appliedto assist human users in the analysis and design of structures as in, but not limited to, thefollowing scenario: • At first one examines a real world problem, defines the problem domain for the analy- sis, and inputs the data as well as important analysis keywords into a structural analysis software agent, which is a web service that performs analysis of structures on behalf of human users or other agents. The keywords may be related to material types such as mild steel, aluminum, reinforced concrete or ASTM1 A36 steel, material charac- teristics such as linear elastic, elastoplastic, viscoelastic, or viscoplastic, modes of analysis such as static, dynamic, buckling, or fracture, or boundary conditions such as cantilever, simply-supported, three-edge fixed supported, building codes such as the Uniform Building Code (UBC) and the National Building Code (NBC), and design specifications such as the American Institute of Steel Construction (AISC) specifica- tions, the American Concrete Institute (ACI) specifications, and the British Standards (BS) specifications. • Next, the agent consults its ontology to understand the user’s request and construct a set of problem parameters to perform an analysis. For example, if the user instructs the agent to find the maximum service stresses of an ASTM A36 steel plate with dimen- sions of 1.00 m wide by 200 cm long with a quarter inch thickness simply supported on all edges and subjected to the residential floor live load spec- ified in the latest version of UBC code, 1 American Society for Testing and Materials 2
  11. 11. the agent would consult its ontology to identify the parameters required for a typical stress analysis, which are the nodal coordinates, modulus of elasticity, Poisson’s ratio, vector of applied forces, and boundary conditions, and then prepare the parameters to perform the analysis accordingly. Specifications of the boundary conditions, i.e. simply-supported, are available in the boundary condition ontology. Specifications of the design live load, i.e. UBC residential live load on floors, are available in the building code ontologies accessible to the agent. In the same manner, modulus of elasticity, yield stress, and ultimate strength of the ASTM A36 steel are also specified in the ASTM standard material ontology. If the keywords used in the building code ontologies or material ontologies are different from the ones that the agent knows, the agent can infer from the ontologies that define those keywords and identify the one that specifies the parameter needed. Differences among units of measurements, i.e. meters, centimeters and inches, will also be arbitrated since the agent can identify meters, centimeters, and inches as units of lengths and consult the measurement ontology.• The agent would consult its process ontology and understand that maximum stresses in typical analyses can be derived from nodal displacement solutions obtained by a finite element analysis. Inferences on the process ontology also suggests the agent that, to perform a finite element analysis, it needs to discretize the problem domain, which, in this case, is the geometry of the plate given by the user, into a mesh consisting of a number of nodes and their connectivities. After discretization, it needs a global stiff- ness matrix, which is constructed by assembling the element stiffness matrices derived from nodal coordinates and material properties, and a global force vector, which is constructed by assembling the element force vectors derived from nodal coordinates and specifications of forces, including the live load it obtained from the building code ontology.• The agent would consult the process ontology further that once it obtains the global stiffness matrix and the global force vector, it needs to set up a linear system of equa- tions, apply the boundary conditions obtained from the boundary condition ontology, and solve the system of equations to obtain the vector of nodal displacements. It then needs to find the derivatives of the nodal displacements to obtain stresses in the plate and search for the maximum values of stresses as requested by the human user.• During the analysis, the agent is aware that it cannot solve the linear system of equa- tions nor search for the maximum values of stresses efficiently because the agent was originally designed to solve some other types of problems. Therefore, it seeks the help of other agents. It starts by making a request to a service registry for a list of available software agents that offer solutions to linear system of equations and the ones that can identify maximum values from a list of given floating point numbers. The service reg- istry would return to the agent the requested list which includes the estimated time to get results from the agents, the characteristics of the input, e.g. dense matrices, sparse matrices, banded matrices, and the formats of input and output data. The agent would consult the list, reason and select the best third-party agent for each operation, prepare the suitable input data for each agent, request them to perform the computations on its behalf, convert the results back to its own format, and utilize the results in its struc- tural analysis procedure in the same manner as it would do with the results from its own subroutines. 3
  12. 12. 1.2 Problem Statement Semantic Web Services, which is the combination of Web Services and the SemanticWeb technologies, can be used to improve the performance and user-friendliness of structuralanalysis software as well as the collaboration among them by enabling intelligent agent-based analyses of structures in a parallel and distributed manner. The realization of such aparadigm with an example described at the end of Section 1.1 involves issues in the followingareas: 1. Representation of knowledge in scientific computing and structural engineering 2. Modeling of structural analysis processes such as the processes in the finite element methods or meshless methods 3. Description of software agents for automatic discovery and delegation of analysis tasks 4. Design of languages and mechanisms for communications among software agents 5. Design of the services registry broker to support automatic discovery and collaboration of software agents. Knowledge and tools in structural engineering and computer science, and in par-ticular, computational mechanics and artificial intelligence, are required in this study. Theformer are required to construct ontologies in scientific computing and structural engineeringas well as to model structural analysis processes. The latter are required to properly designand construct the ontologies as well as to reason on them.1.3 Objectives The primary objective of this study is to construct and implement a framework for auser-friendly intelligent structural analysis paradigm that enables collaboration of structuralanalysis software agents on sequential and parallel computers by means of the Semantic WebServices technology. To fulfill the primary objective, the following secondary objectives areto be achieved: 1. To capture and construct the domain ontologies in scientific computing and structural engineering, which include (a) the ontology on quantities such as scalars, vectors, matrices, tensors and the op- erations on them (b) the ontology on measurements such as lengths, forces, masses and the conver- sions between different units such as SI units and US customary units (c) the ontology on material properties such as strength properties and elastoplastic or viscoplastic properties (d) the ontology on geometric properties such as those of points, lines, triangles, rectangles, boxes, and tetrahedra. (e) the ontology in computational mechanics domain such as nodes, displacements, stresses, strains, and boundary conditions. 4
  13. 13. 2. To capture and construct the process ontologies in computational mechanics such as discretization, formulation of shape functions, formulations of element stiffness ma- trices and their assembly, applications of the boundary conditions, solutions of the system of equations, and calculations of stresses and strains 3. To examine and apply available techniques in computer science, which include de- scriptions of software agents, agent communication languages, and services registry brokering, to create a mechanism that enables automatic discovery and collaboration among the structural analysis software agents. 4. To demonstrate and evaluate the usability of the framework by implementing the pro- posed components and applying them to selected classes of analysis problems.1.4 Scope The goal of this study is to propose a framework for Semantic Web Services in com-putational mechanics and to provide an implementation to demonstrate its usability. Thestudy will involve semantic collaboration of structural analysis software agents located onsequential and parallel computer clusters on the Internet to solve problems in linear elasticityand elastoplasticity. The SI and the US customary units of measurements, loadings from theUBC building code, load factors from ACI and AISC structural design manuals, and materialproperties specified in ASTM standards will be supported in the implementation by means ofontologies. In the computer science aspect, networking security issues will not be taken intoaccount. Implementation of the software components will utilize efficient and appropriatealgorithms. However, exhaustive searches and uses of the most optimal ones will be of lessconcern.1.5 Research Approach Semantic Web Services is an area in the Extensible Markup Language (XML) (Brayet al., 2000; Fallside, 2001; Thompson et al., 2001; Biron and Malhotra, 2001) family of tech-nologies. Therefore, all software components that comprise the structural analysis agents andthe services registry broker will be based on state-of-the-art XML technologies and will beimplemented in the Java programming language which is the prevalent programming lan-guage among the XML community. The Message-Passing Interface (MPI) software library(MPI, 1995) for parallel programming will be used in the implementation of the softwarecomponents on parallel computer clusters. Modeling of processes, representation of knowl-edge, and inference capabilities of the software agents will be by means of XML DeclarativeDescription (Wuwongse et al., 2001) and XML Equivalent Transformation (XET) (Anutariyaet al., 2002), which are a unified modeling language for the Semantic Web and the associatedcomputation and inference engine, respectively.1.6 Contributions This study will try to improve human-to-machine and machine-to-machine collabora-tion on structural analysis problems across the Internet using Web and artificial intelligencetechnologies. The result of this study will be a framework that turns the Internet into alarge platform for numerical analysis of structures where distributed software agents or webservices individually developed and located on heterogeneous computer platforms, e.g. per-sonal computers, parallel computer clusters, and supercomputers, with different specializa- 5
  14. 14. tions can cooperate on analysis tasks in a unified manner using the shared conceptualizationsdefined by ontologies. This framework would improve knowledge sharing and collaborationamong researchers and implementors in structural engineering field. It may be further linkedwith other works in the Semantic Web and Web Services research and commercial areas,thus making numerical analysis of structures more accessible and applicable to the public. 6
  15. 15. CHAPTER 2 FEM AND THE NEED TOWARDS DISTRIBUTED COMPUTING2.1 Formulation of the Finite Element Method The finite element method (FEM) is a numerical procedure for analyzing structuresand continua. Usually the problems addressed are too complicated to be solved satisfactorilyby classical analytical methods. The finite element procedure produces many simultaneousalgebraic equations, which are generated and solved on computers ranging from personalcomputers to mainframe and super computers (Cook et al., 1989). The formulation of typical displacement-based finite element methods from Cooket al. (1989) is presented in this section. In the displacement-based finite element method,displacements are taken as the dependent variables with the total potential energy of a bodyΠ p as the associated functional. An admissible displacement field is defined in a piecewisefashion such that displacements within any elements are interpolated from nodal degree offreedoms (d.o.f.) of that element. The total potential energy functional is then evaluated interms of nodal d.o.f. Using the principle of stationary potential energy, we write dΠ p = 0 andobtain a simultaneous system of algebraic equations to be solved for nodal d.o.f. Detailedderivation of the displacement-based finite element method is as follows: The total potential energy in a linearly elastic body is described as: 1 T Πp = ε E ε − εT E ε0 + εT σ0 dV − uT F dV − uT Φ dS − DT P (2.1) V 2 V Sin which u = u v w T , the displacement field ε = εx εy εz γxy γyz γzx T , the strain field E = the material property matrix ε0 , σ0 = initial strains and initial stresses F = Fx Fy Fz T , body forces Φ = Φx Φy Φz T , surface tractions D = nodal d.o.f. of the structure P = loads applied to d.o.f. by external agencies S,V = surface area and volume of the structure The material property matrices E for isotropic materials are given as (1 − ν)c νc νc   0 0 0  νc (1 − ν)c νc 0 0 0  νc νc (1 − ν)c 0 0 0    E =  (in three dimensions) (2.2a)  0 0 0 G 0 0   0 0 0 0 G 0 0 0 0 0 0 G E E where c = and G = , (1 + ν)(1 − 2ν) 2(1 + ν) 1−ν ν   0 E  ν E= 1−ν 0  (for plane strain conditions), (2.2b) (1 + ν)(1 − 2ν) 1−2ν 0 0 2 7
  16. 16. 1 ν   0 E  E= ν 1 0  (for plane stress conditions). (2.2c) 1 − ν2 1−ν 0 0 2E and ν in the above expressions are Young’s modulus of elasticity and Poisson’s ratio,respectively. Displacements within an element are interpolated from element nodal d.o.f. d, u = Nd (2.3)where N is the shape function matrix. For 4-noded plane rectangular bilinear element (2.3) is specialized as   u1    v1      u N1 0 N2 0 N3 0 N4 0   = u2 (2.4) v 0 N1 0 N2 0 N3 0 N4  .  . .     v  4where subscripts 1 . . . 4 respectively denote the first node to the fourth node of the planeelement. For an element of 2a wide by 2b long, each Lagrange’s shape function Ni abovehas the form (a ± x)(b ± y) Ni = (2.5) 4ab For 8-noded solid rectangular trilinear element (2.3) is specialized as    u1      v1         u N1 0 0 N2 0 0 . . . w     1 v =  0 N1 0 0 N2 0 . . . u (2.6)  2 w 0 0 N1 0 0 N2 . . .  .     .   .        w  8where subscripts 1 . . . 8 respectively denote the first node to the eight node of the brick ele-ment. For an element of 2a wide by 2b long by 2c thick, each Ni above has the form (a ± x)(b ± y)(c ± z) Ni = (2.7) 8abc The strains are obtained from displacements by differentiation. Thus ε = ∂u yields ε = Bd, where B = ∂N (2.8) Relation between strains and displacements in the equation above is given in two andthree dimension respectively by ∂  0 0  εx   ∂x ∂     0  εy    0     ∂ ∂y   εx  0  ∂x    0 0 ∂ u    εz   ∂ u εy =  0 ∂y  =  ∂ ∂ ∂z  v  and (2.9)  v γxy   ∂y ∂x 0    γxy ∂ ∂     w γyz    ∂y ∂x   0 ∂ ∂     ∂z ∂y  γzx     ∂ ∂ ∂z 0 ∂x 8
  17. 17. Substitution of (2.3) and (2.8) into (2.1) yields 1 numel T numel Πp = ∑ 2 n=1 d n kn d n − ∑ d n ren − DT P T (2.10) n=1where summation symbols indicate that we include contributions from all numel elements ofthe structure. Element stiffness matrix k and element load vector re are derived, respectively,from k= B T EB dV (2.11) Ve re = B T E ε0 dV − B T σ0 dV + N T F dV + N T Φ dS (2.12) Ve Ve Ve Sewhere Ve denotes the volume of an element and Se denotes its surface. In the surface integral,N is evaluated on Se . Every d.o.f. in an element vector d also appears in the vector of global d.o.f. D. Thus,when k and re of every element are expanded to structure size, D can replace d in (2.10).Equation (2.10) becomes 1 Π p = DT K D − DT R (2.13) 2where numel numel K= ∑ kn and R = P+ ∑ re n (2.14) n=1 n=1 Summations indicate assembly of element matrices by addition of overlapping terms.Making Π p in (2.13) stationary with respect to small changes in the Di we obtains ∂Π p =0 (2.15) ∂D KD = R (2.16)Matrix equation (2.16) is a set of simultaneous algebraic equations to be solved for d.o.f. D. From the formulation presented above, the finite element procedure for linear elasticproblems is summarized. Given a description of the problem which consists of a problemdomain, a specification of body forces, surface tractions, initial stresses, initial strains, andprescribed boundary conditions, the problem domain is divided into a finite number of partsor elements identified by nodes and their connectivities. The goal of the displacement-basedfinite element procedure is to obtain the displacement at the nodes D by solving the simulta-neous system of equations (2.16). The global stiffness matrix K and the global load vector inthis equation are assembled from their element counterparts, which are the element stiffnessmatrices and the element load vectors obtained by evaluating Equations (2.11) and (2.12),respectively. The global stiffness matrix K is singular by its nature. It cannot be invertednor can a unique set of nodal displacements D be obtained by solving the equations. Thephysical reason for this is that rigid-body motion is still possible. Without supports, thestructure will float away even if the smallest external load is applied. Thus, prescribed dis-placement boundary conditions are applied to the system of equations after assemblies tomake them solvable. Once the nodal displacements are obtained, strains within an elementare derived from ε = Bd in Equation (2.8). Stresses are obtained by multiplying the strainvector by the corresponding material property matrices E presented earlier. For nonlinearproblems in elastoplasticity, Equation (2.16) becomes K (D) D = R(D) and displacementsare incrementally solved in an iterative manner. 9
  18. 18. Accuracy of the finite element methods depends on many factors such as the basisof shape functions used for interpolation, the fineness of discretization of the problem do-main, and constitutive models of the materials. More accurate constitutive models givesmore accurate responses of a structure to given applied loads whereas finer discretizationsand higher order of bases make responses of the structure closer to the ones that would beobtained if it was a continuum. For large and complex analysis problems such as nonlinearanalyses of three dimensional bodies, the increased accuracy may come at the expense of asignificantly increase in analysis time, which could be hours or days. Thus, techniques fromcomputer science such as parallel and distributed computing are often applied to improve theperformance of computer-aided structural analyses. The next sections present an overviewof parallel computing technique and a discussion which leads to the need for an applicationof distributed computing techniques in structural engineering.2.2 Parallel Computing and Applications in Computational Mechanics Parallel computing is a field in computer science that deals with how to accomplisha task faster by dividing it into a set of subtasks assigned to multiple workers (Kumar et al.,1994). It differs but is closely related to distributed computing which is the field that dealswith techniques to spread a computational task across several programs, processes or pro-cessors (Brown, 1994). Distributed computing is mainly concerned with problems such asreliability, security, and heterogeneity which are generally regarded lightly in parallel com-puting, however, the basic task of developing programs that can run on many computers atthe same time is a parallel computing problem (Foster, 1995). Parallel software needs a parallel computing platform to run. In the past, the platformis available on expensive time-shared parallel supercomputers such as Cray-1 or IBM SP-2(Foster, 1995) only. This made parallel computing a field that not all people can have accessto. Beginning in 1990s the development of cluster computing technology which enables per-sonal computers connected to a high-speed local area network to form a virtual parallel com-puter has made it possible for the public to have access to parallel computing environments.Toolkits such as the Parallel Virtual Machine (PVM) library (PVM, 2002) and the MessagePassing Interface (MPI) library (MPI, 1995) enable the communication and coordination ofprocessors by means of message passing. The libraries provide routines to initiate and con-figure a messaging environment as well as sending and receiving of data between processorscomprising a virtual parallel computer (Baker and Buyya, 1999). A parallel computer basedon the message-passing cluster technology is classified as the Multiple Instruction stream,Multiple Data stream (MIMD) type in parallel computing taxonomy (Kumar et al., 1994;Foster, 1995). Cluster-type parallel computer is now a less expensive alternative platformfor parallel scientific computing with applications ranging from biomedicine (Warfield et al.,2002), computational fluid dynamics (Gropp et al., 2001), fracture mechanics (Heber et al.,2001) to meshless analysis of structures (Barry and Vacharasintopchai, 2001b).2.3 Towards an Application of Distributed Computing Technique History suggests that as a particular technology satisfies known applications, newapplications will arrive that are enabled by that technology and that will demand the devel-opment of new technology (Foster, 1995). A personal computer in 2001 is as fast as a su-percomputer of 1990 (Foster, 2002a). But in 1990 analysts were satisfied with approximatesolutions while in 2001 analysis of large structures with minor details taken into account arepreferred. In some applications, only one personal computer or one small cluster of personal 10
  19. 19. computers may not be powerful enough to solve a problem. One parallel supercomputer maynot deliver enough power for real-time simulation of natural phenomena with increasinglycomplex problem formulation. More computational power from many computers may beneeded to perform the analysis in a given amount of time. In some tasks, special analysis en-gines or visualization modules may be required but are not accessible on a local computer. Insome cases, knowledge or information necessary for an analysis may not be available locally.Computational power, proprietary analysis or visualization modules, and knowledge or in-formation are collectively termed resources. In order to utilize the scattered resources suchthat the ever-increasing application demand is satisfied, a technique in distributed computingfrom computer science is necessary. The next chapter presents an overview of developmentsin distributed computing. 11
  20. 20. 12
  21. 21. CHAPTER 3 DISTRIBUTED COMPUTING3.1 Distributed Computing Concepts Distributed computing is an area in computer science that deals with the spreading ofa computational task across several programs, processes or processors (Brown, 1994). Thereare many forms of distributed computing with many different reasons for each and a widerange of research activity in the area. The forms of distributed computing can be classifiedby the benefits that they offer. Based on Brown (1994), the benefits are listed as follows: 1. By splitting the solution to a specific problem into a number of steps, we can use existing general-purpose programs to handle some of these steps, and so reduce the amount of new code we have to write. We may often be able to avoid writing any new code altogether. This benefit is referred to as tool building. 2. By using several processors concurrently, we can solve the problem more quickly than if we used a single processor. This benefit is referred to as concurrency. 3. If the problem itself is sometimes of the form “Do A, B and C in parallel”, the most natural solution may be to use separate parallel processes to perform A, B and C. Forcing the solution into a strictly sequential form for execution by a single process is unnatural and makes it harder to understand. This benefit is referred to as parallelism. 4. Sometimes, the resources needed to solve the problem are themselves spread around among several computers on a network. In distribute computing context, we can view the network as a whole as a collection of shared resources. This benefit is referred to as resource sharing.3.2 Methods of Problem Decomposition To benefit from distributed computing techniques, a problem has to be decomposedinto pieces so that the solution may be distributed. In the following, various methods todecompose a problem, as described in (Brown, 1994), will be presented.3.2.1 Data Distribution The first way of distributing an application is to divide the input data set into pieces,and hand each piece to a separate process, as shown in Figure 3.1. This is known as datadistribution or domain decomposition. Data distribution divides the input data set amongseveral processors. The code is replicated on each processor and each does the same op-erations but on a different piece of data. Depending on the amount of input data and thenature of the problem, there will be an upper limit on the number of processors which can beusefully employed and a lower limit on the smallest amount of data that can sensibly be pro-cessed on each processor. The limits are related to ordering and synchronization of the tasksdistributed to each processor. Consider a number of tasks with many processors processingdata on each task. Some data may need to be processed by other tasks and we cannot haveall of these tasks performed simultaneously. Information from other processors performingthe same task may be necessary to process a set of data on a given processor, and thus aprocessor may have to wait for other processors in processing a set of data. The former isan issue on ordering whereas the latter is an issue on synchronization. Two terms are used 13
  22. 22. Processor A Processor B Processor C Figure 3.1: Problem Decomposition: Data Distributionto categorize problems based on their synchronization requirements. Loosely coupled prob-lems are problems that do not require frequent synchronization of activity and the exchangeof data among processors while tightly coupled problems are the opposite. The couplingdegree is very important for problems that involve a large number of processors because itdetermines the extent to which one can achieve a speed-up which is linearly related to thenumber of processors.3.2.2 Algorithmic Distribution The second way for distributing a problem solution is algorithmic distribution orfunctional decomposition. In this method, various tasks are operated in a pipeline manner,as illustrated in Figure 3.2. An analogy for algorithmic distribution is the production linesin manufacturing factories. The key characteristic of algorithmic distribution is that eachprocessor sees the same data items but performs a different operation on them. In a multi-processor implementation, this is the situation when each processor is running different code.The number of processors that can be employed in this way is limited by the number of steps(or processes) in the pipeline, and is usually quite small. It does not grow as the size of theinput data set increases. Loose synchronization is inherited with algorithmic distribution.In Figure 3.2, when Process A receives so much input data that its production rate cannotmatch the input capacity of Process B, Process B will have to wait for Process A. This causesa bottleneck problem and data distribution techniques may be employed in the process or thewhole pipeline, as shown in Figures 3.3 and 3.4 to alleviate this problem. 14
  23. 23. Process A Process B Process C Figure 3.2: Problem Decomposition: Algorithmic Distribution Process A1 Process A2 Process B Process C Process A3Figure 3.3: Problem Decomposition: Hybrid Distribution Method 1 15
  24. 24. Process A1 Process B1 Process C1 Process A2 Process B2 Process C2 Figure 3.4: Problem Decomposition: Hybrid Distribution Method 23.2.3 Load Balancing When a computing task is to be spread across multiple processors for concurrent ex-ecution, it is desirable to make sure that each processor has an equal amount of work todo. If some processors are given less work than the others, they will finish sooner and beidle until the others are done. In some applications using data distribution, load balancing iseasily achieved by giving each processor an equal amount of input data. This would work ifeach data item is equally expensive to process and each process has the same performance orproduction rate but it is not generally true for all applications and environments. For manyapplications each data item does not take equal time to process and the performance of eachprocessor on a network is typically not equal. Thus, a load balancing strategy needs to beemployed to maximize the performance of the whole distributed computing system. Oneapproach for load balancing on data distribution is to divide the data into many more piecesthan the number of processors, and allow the processors to get themselves a new piece of datawhen they are ready. This approach is sometimes called a processor farm and was, for exam-ple, implemented in Barry and Vacharasintopchai (2001a). Load balancing for a pipelined,algorithmic decomposition of a problem is more difficult than its data decomposition coun-terpart because we cannot usually fine tune the workload of each processor by adjusting theboundaries between the tasks that they perform. In this case, the hybrid methods discussedearlier may be employed to achieve better load balancing among processors.3.3 Applications in Scientific Computing With the advent of numerical methods, analyses of natural phenomena have reliedheavily on the use of digital computers. Procedures for such analyses involve discrete mod-els that depend on sizes of the problems and desired accuracy of the solutions, rather thanin closed forms usually found in the past. In some applications, as sizes of the problems 16
  25. 25. grow centralized computing cannot be used to solve the problems efficiently. Researchersand practitioners have then turned to distributed computing as a means to solve the problemsin a more natural and efficient way. Up to now, scientific computing has been a predom-inantly application-driven area for distributed computing. In this section, two importantachievements in this area, namely, the SETI@home project and grid computing, will besummarized.3.3.1 SETI@home Project SETI@home Project (Anderson et al., 2002), a famous project for its application ofpublic-resource computing, is a project in a research area called the Search for Extraterres-trial Intelligence (SETI). SETI is an area whose goal is to detect intelligent life outside theEarth. SETI@home project is based on the radio SETI approach which uses radio telescopesto listen for narrow-bandwidth radio signals from space. The signals are not known to occurnaturrally, so a detection would provide evidence of extraterrestrial technology. In contrastto radio SETI projects in the past that used special-purpose supercomputers located at thetelescope to perform data analysis, the project uses a virtual supercomputer composed of alarge number of personal computers connected to the Internet. During the design of the SETI@home project, the designers were aware of the poten-tially high network bandwidth associated with the analysis. Network bandwidth consump-tion increases as the frequency range and the resolution of the search is increased. Therefore,they limited the frequency range and resolution of the search to the ones that are just enoughto capture a significant sign of intelligence. It was reported that, compared to other radioSETI projects, SETI@home covers a narrower frequency range but does a more thoroughsearch in that range. The computational strategy of SETI@home can be described as follows. At the cen-tral server complex, the radio signal data is divided into work units of the same sizes. Thesework units are distributed by a multithreaded data/result server to a client program runningon the participants’ computers via the Internet using a HTTP-based protocol. The reasonfor a HTTP-based protocol is to facilitate the clients whose Internet connections are behindfirewalls. The client program, downloadable from SETI@home web site, computes a result,which is a set of candidate signals, returns it to the server for post-processing, and gets an-other work unit. All clients work on their own without any communication among them andneed Internet connections only while downloading the source data from the server or viceversa. The client program can be configured to compute only when the host is idle or to com-pute constantly at a low priority. The program periodically writes its state to a disk file andreads the file on startup so that progress is made even if the host is frequently turned off. Theproject also does redundant calculation by assigning each work unit to be processed multi-ple times. By employing an approximate consensus policy at the central server to choose acanonical result for a particular work unit, results from faulty processors and from malicioususers can be identified and discarded. A relational database management system is employedto manage information about source data, work units, results, users, and other aspect of theproject. SETI@home has proven to be a socially successful distributed computing project.The project began in 1998 with 400,000 participants and the number of participants hadgrown to over 3.8 millions by July 2002. Between July 2001 and July 2002 SETI@homeparticipants processed 221 million work units in 12 months and the average throughput was27.36 Tera FLOPS or 27.36 × 1012 floating point operations per second. 17
  26. 26. 3.3.2 Grid Computing Grid computing is a recent field in distributed computing. The term grid was intro-duced in the 1990s to denote a proposed distributed computing infrastructure for advancedscience and engineering (Foster et al., 2001). The grid is a new class of computing infras-tructure built on the Internet and the World Wide Web. It provides scalable, secure andhigh-performance mechanisms for discovering and negotiating access to remote comput-ing resources (Foster, 2002a). Through resource sharing among geographically distributedgroups, it is possible for scientific communities to collaborate on a very large scale. Foster(2002b) gave a definition of the grid as a hardware and software infrastructure that providesdependable, consistent, pervasive, and inexpensive access to high-end computational capa-bilities. In Foster et al. (2001), the definition was refined to address social and policy issues.It was stated that grid computing is concerned with the coordinated resource sharing andproblem solving in dynamic, multi-institutional virtual organizations and that the sharing isnot primarily in file exchange but rather is the direct access to computers, software, data,and other resources, as required by a range of collaborative problem-solving and resource-brokering strategies. Many distributed computing technologies have emerged over the past decade as aresult of the prosperity of the Internet. Currently these technologies are of great intereststo the commerial and the scientific communities and some of them have been termed grids.To help distinguish grid computing technologies from the rest, Foster (2002b) proposed anidentification checklist and explained that a grid computing technology must possess thefollowing properties: 1. Coordination of resources that are not subjected to centralized control: A grid must integrate and coordinate resources and users that live in different control domains, e.g. different administrative units of the same company, different companies, or different countries. 2. Uses of standard, open, general-purpose protocols and interfaces: A grid must be built from multi-purpose protocols and interfaces that address fundamental issues such as authentication, authorization, resource discovery and resource access. It is important that these protocols and interfaces be standard and open to prevent application-specific systems. 3. Delivery of nontrivial qualities of service: A grid must allow its constituent resources to be used in a coordinated fashion to deliver various qualities of service, relating for example to response time, throughput, availability, and security, including co- allocation of multiple resource types to meet complex user demands and result in the synergy of the combined system. A list of major projects in grid computing can be found in Foster (2003a) and Foster(2003b). Two large-scale grid deployments that are worth-mentioning and are being under-taken within the scientific community are NASA’s Information Power Grid (IPG, 2003) andthe TeraGrid (Catlett, 2002). The Information Power Grid is a project funded by the Com-puting, Information, and Communications Technology (CICT) program at NASA Ames Re-search Center to link the resources between NASA Ames Research Center, NASA GlennResearch Center, National Science Foundation (NSF) Partnerships for Advanced Computa-tional Infrastructure (PACI) program at the National Center for Supercomputing Applica-tions, the NSF PACI program at the San Diego Supercomputing Center, Argonne National 18
  27. 27. Figure 3.5: Model of the NASA X-38 Crew Return Vehicle (Barnard et al., 1999) Figure 3.6: Mach Contours for the X-38 Crew Return Vehicle (Barnard et al., 1999)Laboratory, and Information Sciences Institute in the United States. The TeraGrid is a projectbeing constructed to link major academic sites in the U.S. which include California Instituteof Technology (Caltech) for data collection analysis facilities, Argonne National Labora-tory (ANL) for visualization facilities, San Diego Supercomputing Center (SDSC) for datastorage facilities, National Center for Supercomputing Applications (NCSA) and PittsburgSupercomputing Center (PSC) for computational facilities. The work described in Barnardet al. (1999) is an example of computational fluid dynamics (CFD) experiments performedon the Information Power Grid. In this work, a virtual machine comprised of parallel su-percomputers, linked by a grid infrastructure, was chosen to execute a CFD application in-volving the accurate prediction of high-speed viscous flow around a geometrically-complexthree-dimensional body shown in Figures 3.5 and 3.6. Problems of this nature challenge thecapabilities of the most advanced single-processor platforms available. Large-scale multi-processor computer systems offer a powerful tool to solve large and complex problems; butthey may still not suffice, and gaining exclusive access to them is difficult in practice. Most major grid projects utilize the community-based, open-source Globus Toolkit(Foster, 2002a), which provides the basic infrastructure for grid operations. The GlobusToolkit has now become the de facto standard for grid computing (Globus, 2003a). Spon-sored by the U.S. Department of Defense, Department of Energy, National Science Foun-dation, NASA, IBM, Microsoft and Cisco Systems Corporation (Globus, 2003b; Ungaro,2003), the project has been announced for support by at least 12 companies. The grid com- 19
  28. 28. munity has also formed their own organization called the Global Grid Forum. Currentlywith more than 5,000 members world-wide, the Global Grid Forum is a significant body forsetting standards and development in the field (GGF, 2003). 20
  29. 29. CHAPTER 4 WEB SERVICES4.1 The Web Services Architecture Web Services (W3C, 2003d) is a new distributed computing architecture that uses theInternet as the medium for communication. The fundamental concept of Web Services is tobuild computer software by making use of Remote Procedure Calls (RPC) to objects or sub-routines over the Internet or a network. It differs from other previous distributed computingtechnologies in the use of platform-independent standards such as the Hypertext TransferProtocol (HTTP) (Fielding et al., 1999) and the eXtensible Markup Language (XML) (Brayet al., 2000; Fallside, 2001; Thompson et al., 2001; Biron and Malhotra, 2001) which allowservice providers to completely hide the implementation details from the clients. The clientsneed to know the Unified Resource Locator (URL) (Berners-Lee et al., 1994) of the serviceand the data types used for the method1 calls but does not need to know how the service isimplemented in order to make use of it (Basha et al., 2002). The architecture of Web Servicesas described in (Basha et al., 2002) is presented as follows. Web Services architecture makeextensive use of the XML language. The readers are referred to standard textbooks on XMLsuch as Harold (2001) for detailed information. A typical model of Web Services architecture is illustrated in Figure 4.1. Threeroles, namely, service provider, service consumer and service registry, and three operations,namely, publishing, finding and binding, are involved in the Web Services model. Descrip-tions of the roles are as follows:Service Provider – A service provider is an entity that creates the Web Service. Typically, the service provider exposes certain functionality in their organization as a Web Ser- vice for any organization to invoke. To reach full potential of a Web Service, the service provider needs to do two tasks. First, it needs to describe the Web Service in a standard format understandable by all organizations that will be using that Web Service. Next, it needs to publish the details about its Web Service in a central registry that is publicly available to everyone.Service Consumer – A service consumer is any organization that uses the Web Service provided by a service provider. The service consumer can know the functionality of a Web Service from the description made available by the service provider. To retrieve these details, the service consumer makes a search in the registry where the service provider had published its Web Service description. The service consumer is able to get from the service description the description of the mechanism to bind to the service provider’s Web Service and in turn to invoke that Web Service.Service Registry – A service registry is a central location where the service provider can list its Web Services, and where a service consumer can search for Web Services. Service providers usually publish their Web Service capabilities in the service registry for service consumers to find and then bind to their Web Service. Information such as organization details, the Web Services that it provides, and the brief details about each Web Service including technical details is typically stored in the service registry. As mentioned above, three operations fundamental to Web Services architecture are“finding”, “binding”, and “publishing”. The architecture aims to achieve inter-application 1A subroutine in object-oriented programming paradigm 21
  30. 30. Find Service Registry Publish Service Bind Service Provider Consumer Web Service Description Figure 4.1: Conceptual Web Services Model (Basha et al., 2002)communication irrespective of the programming language the application is written in, theplatform the application is running on, etc. To make this happen, the standards for eachof these three operations and a standard way for a service provider to describe their WebService irrespective of the programming languge used are needed. These standards are listedas follows: • A standard way to describe Web Services – The Web Service Description Language (WSDL) (W3C, 2001) is a standard that uses XML format to describe Web Services. The WSDL document for a Web Service defines the methods that are present in the Web Service, the input/output parameters for each of the methods, the data types, the network transport protocol used and URL of the end point at which the Web Service will be hosted. • A standard protocol to publish or find Web Services – The Universal Description, Discovery, and Integration (UDDI) standard (OASIS, 2002) provides a way for service providers to publish details about their organization and the Web Services that they provide to a central registry. It also provides a standard for service consumers to find service providers and details about their Web Services. Publication of the details is the “description” part of the UDDI and finding of such details is the “discovery” part of it. • A standard protocol for applications to bind to Web Services – The Simple Object Access Protocol (SOAP) (W3C, 2000) is a lightweight2 XML mechanism used to exchange information between applications regardless of the operating systems, pro- gramming languages, or object models employed in developments of the applications. The roles of SOAP, WSDL and UDDI within the context of Web Services architec-ture is presented in Figure 4.2. Each of the layered blocks in the figure builds upon the blockbeneath it. The labels shown on the left identify the concepts in the architecture and thoseon the right identify actual technologies being used in the implementations. The TransportNetwork layer is responsible for making Web Services accessible by using any of the trans-port protocols available such as the Hypertext Transfer Protocol (HTTP) and the Simple Mail 2 with small amount of overhead instructions 22
  31. 31. Service Publication/Discovery UDDI Service Description WSDL XML Messaging SOAP HTTP, SMTP, FTP or HTTPS Transport Network TCP/IPFigure 4.2: Roles of SOAP, WSDL and UDDI in Web Services Architecture (Basha et al.,2002)Transfer Protocol (SMTP) (Klensin, 2001). The XML Messaging layer defines the messageformat that is used for application communication, with SOAP as the standard commonlyused by Web Services. The Service Description layer provides a mechanism for a serviceprovider to describe the functionality that a Web Service provides. The Service Publication TMand Discovery layer acts like a Yellow Pages where service providers publish the links totheir WSDL documents describing the Web Services they provide and the instructions to TMmake use of them. Service consumers, on the contrary, use these Yellow Pages to searchfor Web Services suitable for their needs and make use of them according to the instructionsgiven by service providers.4.2 Applications of Web Services in Scientific Computing Web Services has been involved in many area of scientific computing ranging fromcomputational infrastructure developments (Chiu et al., 2002; van Engelen, 2003) to finiteelement analysis a coupled fluid, thermal, and mechanical fracture problem (Chew et al.,2003). Chiu et al. (2002) investigated the limitations of SOAP for high-performance scien-tific computing and presented an improved implementation of the SOAP specification moresuitable to applications in this area. van Engelen (2003) investigated the usability, interoper-ability, and performance issues of SOAP/XML-based Web Services for scientific computingand addressed key issues important for deployment of high-performance and mission-criticalservices. It was reported that a successful deployment of Web Services in scientific comput-ing may be achieved by limiting the communication overhead of XML encoding throughdefining optimized XML data representations and by applying message chunking, compres-sion, routing, and streaming techniques in the communication between services. In Chewet al. (2003), crack propagation of a rocket engine segment subjected to high Reynolds num-ber, chemically reacting gas flow was studied. One of the most significant efforts in this areais the standardization attempt by the Global Grid Forum to create the Open Grid ServiceArchitecture specification (OGSA) (Foster and Gannon, 2003), which is the global standardfor interoperations among the grid community. According to Foster and Gannon (2003), 23
  32. 32. SOAP and WSDL, two important components of Web Services, are adopted in this standard.Since there are already a significant number of scientific computing projects that rely ongrid computing infrastructures, with NASA’s Information Power Grid and the TeraGrid askey projects in the area, imposition of such a standard implies a significant increase in thenumber of scientific computing applications that rely on Web Services technology. 24
  33. 33. CHAPTER 5 THE SEMANTIC WEB5.1 General The World Wide Web has turned into a hyper-library where accesses to the very largecollection of information are ubiquitous, in both academic and non-academic worlds. Thesuccess of the Web may be reflected by the ever-increasing electronic versions of documentssuch as books, magazines, and journals. Since its invention in 1992 (Berners-Lee et al.,1992), HyperText Markup Language (HTML) (W3C, 1999a) has been the standard for pub-lication of documents on the Web. HTML is a collection of tags that are used to specifyhow a document is to be displayed on web browsers. Examples of HTML tags are thosepresented in Figure 5.1, such as <title>, <b>, and <table> that tell web browsers to dis-play the title of a web page, a text in bold typeface, and a table with specified numbers ofrows and columns, respectively. One drawback of using HTML as the sole representation ofdocuments is that, from the point of view of computers, no semantics1 can be extracted fromsuch documents. Pieces of information embedded inside a document can be extracted by hu-mans reading them on web browsers but, to computers, the document itself does not providemuch information other than a stream of characters with extra specifications on how they areto be rendered on web browsers. The HTML code in Figure 5.1 would be rendered as a tableof mechanical properties for steel, aluminum, and copper as shown in Figure 5.2. Humanswould have no trouble understanding that the modulus of elasticity of steel, aluminum, andcopper, are 200 GPa, 70 GPa, and 120 GPa, respectively. With some basic background in en-gineering, human readers would also understand that these moduli of elasticity are specificproperties of materials, which are used to relate stresses to strains developed inside them.Computers, on the other hand, would have no clue what the contents of this table mean and,as a consequence, could not make any use of them unless programmers explicitly specifyhow information from a particular HTML code of a particular web site can be extracted andused. In the example in Figure 5.1, to extract the modulus of elasticity of aluminum from thetable, the computer may be programmed to use a pattern matching technique to get the character string (not a number) that lies between the second pair of <td>, </td> tags located inside the pair of <tr>, </tr> tags whose first pair of <td>, </td> tags contains the string aluminum. The example above is for the case that involves only one document on the Web. Inthe real world, searching for information on the Web often involves multiple documents anddata sources. This means that, in a non-automated way, humans would have to read andexamine these web pages and extract the embedded information, or, in an automated way,programmers would have to examine various patterns of HTML codes and provide hard-coded routines to extract information from various web pages. These tasks are tedious ifthey involve ten to twenty documents from restricted searches on web sites and are nextto impossible on unrestricted searches over the Internet, where a search for “mechanical TMproperties” on Google returns hundreds thousands of web pages, with a potential that theHTML code of each one is constantly being changed due to the decentralized architecture ofthe Web. The Semantic Web (Berners-Lee et al., 2001), a vision for the next generation of theWorld Wide Web, is the Web in which information presented are useful not only for humans 1 the relationship between words or symbols and their intended meanings (Microsoft, 1997) 25
  34. 34. <html> <head> <title>Material Properties</title> </head> 5 <body> <b>Mechanical properties</b> of materials are presented as follows: <br/> <table> <tr>10 <td>Material</td> <td>Young’s Modulus (GPa)</td> <td>Yield Stress (MPa)</td> </tr> <tr>15 <td>Steel</td> <td>200</td> <td>240</td> </tr> <tr>20 <td>Aluminum</td> <td>70</td> <td>60</td> </tr> <tr>25 <td>Copper</td> <td>120</td> <td>260</td> </tr> </table>30 </body> </html> Figure 5.1: Example of an HTML Document $ Mechanical properties of materials are presented as follows: Material Youngs Modulus (GPa) Yield Stress (MPa) Steel 200 240 Aluminum 70 60 Copper 120 260 & % Figure 5.2: HTML Document in Figure 5.1 as Rendered on a Web Browser 26
  35. 35. <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"> <rdf:Description about="http://www.computings.org"> <Creator>Glenn Smith</Creator>5 </rdf:Description> </rdf:RDF> Figure 5.3: Example of an RDF Description but also machines. According to an article by Tim Berners-Lee, the inventor of the Web, and his colleagues (Berners-Lee et al., 2001), by augmenting Web pages with data targeted at computers and by adding documents solely for computers, we would be able to transform the Web into the Semantic Web where computers find the meaning of semantic data on a web page by following hyperlinks to definitions of key terms and rules for reasoning about them logically. Computers will be able to understand pieces of information on web pages rather than merely presenting them to users, and will be able to manipulate such information on their own. Explicit representation of the semantics underlying data, programs, pages, and other web resources, will enable a knowledge-based web that provides a qualitatively new level of service. Automated services will improve in their capacity to assist us in achieving our goals by understanding more of the content on the Web and thus provide more accurate filtering, categorization, and search of information sources (Ding et al., 2002). Eventually, computers could then be able to help us make better use the enormous information scattered on the Internet in a more efficient and less tedious way. According to (Berners-Lee et al., 2001), for the Semantic Web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. In artificial intelligence, such collections are called knowledge representation systems. Enabling such systems on the Web involves syntactic and semantic descriptions of data. Syntactic description is achieved by making use of the eXtensible Markup Langauge (XML) which allows users to add arbitrary structure to their documents by using tags, the hidden labels such as <youngs modulus> or <yield stress> that annotate web pages or sections of text on a page. Computer programs can make use of these tags in many ways but programmers would also have to know in advance the intended meaning of each tag, which is created, adopted, or used by document writers, as XML does not say anything about these meanings. Semantic description, the meaning description of an XML tag, is expressed by on- tology representation languages such as the Resource Description Framework (RDF) (W3C, 1999c) and DAML+OIL (DAML, 2001). Ontologies are encoded in sets of triples in a gram- matical form like the subject, verb and object of an elementary sentence. The triples can be written using XML tags. For example, in RDF, a document makes assertions that a particular thing (such as a person) has properties (such as “is the author of”) with certain values (such as a Web page). An example of RDF descriptions using XML tags is shown in Figure 5.3. In the following sections, RDF and DAML+OIL ontology representation languages as well as the mechanisms to make inferences on ontologies will be presented. 27
  36. 36. 5.2 Resource Description Framework (RDF)5.2.1 Introduction The Resource Description Framework (RDF) is an XML application2 for representinginformation about resources on the World Wide Web. RDF was first developed for represent-ing metadata about Web resources, such as the title, author, and modification date of a Webpage, but, by generalizing the concept of a Web resource, RDF can also be used to representinformation about things that can be identified on the Web, even if they cannot be directlyretrieved on the Web. Intended for situations in which information needs to be processed byapplications rather than being displayed to people, RDF provides a common framework forexpressing the information such that it can be exchanged without loss of meaning. In thefollowing, concepts about RDF, as described in W3C (2003b), will be presented.5.2.2 An RDF Statement RDF is based on the idea that the things being described have properties which havevalues, and that resources can be described by making statements that specify those prop-erties and values. For a particular statement, the part that identifies the thing the statementis about is called the subject. The part that identifies the property of charateristic of thesubject that the statement specifies is called the predicate. The part that identifies the valueof that property is called the object. RDF statements may be represented in both graphicaland non-graphical ways. Figure 5.3 presented earlier is an RDF statement about the au-thor of a Web site encoded in RDF/XML, which is the XML version of RDF representationand is the machine-processable way to represent an RDF statement. The English statementcorresponding to the figure is “http://www.computings.org has a Creator whose valueis Glenn Smith.” The subject for this statement is http://www.computings.org. Thepredicate is Creator and the object is Glenn Smith.5.2.3 Identification of Resources RDF uses Uniform Resource Identifiers (URIs) (Berners-Lee et al., 1998), the gen-eralization of the Uniform Resource Locators (URLs) (Berners-Lee et al., 1994) commonlyused on web browsers, as the basis of its mechanism to uniquely identify subjects, predi-cates, and objects in statements. To be precise, RDF uses URI references (URIref), whichis a URI with an optional fragment identifier separated by the symbol #. For example, theURI reference http://www.computings.org/peoples.html#85740 consists of the URIhttp://www.computings.org/peoples.html, which is a web page that contains informa-tion about many people, and the fragment identifier 85740, which specifically identifies theinformation about people whose identification number is 85740. RDF defines a resource asanything that is identifiable by a URI reference. According to the specification (Berners-Leeet al., 1998) URIs and URIrefs can be used to identify things that can be accessed onlineas well as the ones that cannot. Thus, using URIrefs allows RDF to describe practicallyanything, and to state relationships between them as well.5.2.4 The RDF Model Although RDF may be represented in graphical and non-graphical ways, RDF state-ments are fundamentally modeled as graphs. RDF models statements as nodes and arcs ina graph. An RDF statement is represented by (1) a node for the subject, (2) a node for the 2 A markup language defined by XML. XML is a meta-markup language or the language that is used todefine markup languages (Harold, 2001). 28
  37. 37. http://www.computings.org/index.html http://purl.org/dc/elements/1.1/creator http://www.computings.org/staffid/85740 Figure 5.4: A Simple RDF Statement (adapted from W3C, 2003b)object, and (3) an arc for the predicate, directed from the subject node to the object node.Groups of statements are represented by corresponding groups of nodes and arcs. Figure 5.4 shows an example of a simple RDF statement. Figure 5.5 shows a groupof RDF statements that modify the statement in Figure 5.4 with the following statements: http://www.computings.org/index.html has a creation-date whose value is August 16, 1999. http://www.computings.org/index.html has a language whose value is English. The name of the staff specified by http://www.computings.org/staffid/ 85740 is Glenn Smith. He is 27 years old. Objects in RDF statements may be either URIrefs or literals, which are constant val-ues of character strings to represent property values. Literals may not be used as subjects orpredicates in RDF statements. In RDF graphs, nodes that are URIrefs are shown as ellipseswhereas nodes that are literals are shown as boxes. URIrefs are used in Figures 5.4 and 5.5 to explicitly specify, for example, that thepredicate creator is to be strictly interpreted by the definition in http://purl.org/dc/elements/1.1/creator, and the object is strictly the staff of Computings.org whose iden-tification number is 85740. Using URIrefs as subjects, predicates, and objects in RDF state-ments supports the development and use of a shared vocabulary on the Web, since peoplecan discover and begin using vocabularies already used by others to describe things, thusreflecting a shared understanding of concepts.5.2.5 Defining RDF Vocabularies RDF provides a way to express statements about resources using named propertiesand values. However, it lacks the capability to define terms or vocabularies to describespecific classes of resources nor the properties to be used specifically on them. Such classesand properties can be described as an RDF vocabulary by using RDF Schema (RDFS) (W3C,2003c). RDF Schema provides the facilities needed to describe classes and properties, andto indicate which classes and properties are expected to be used together. It provides a typesystem for RDF, which is similar in some aspects to the type systems in object-orientedprogramming languages. RDF Schema allows resources to be defined as instances of one ormore classes and allows classes to be organized in a hierarchical fashion. 29
  38. 38. http://www.computings.org/index.html http://purl.org/dc/elements/1.1/creatorhttp://www.computings.org/terms/creation-date http://www.computings.org/terms/language August 16, 1999 http://www.computings.org/staffid/85740 English http://www.computings.org/terms/name http://www.computings.org/terms/age Glenn Smith 27 Figure 5.5: Several RDF Statements about Resources (adapted from W3C, 2003b)Classes RDF Schema uses classes to refer to the kinds of things to be described. A class inRDF Schema corresponds to the generic concept of a type or category, or a class in object-oriented programming languages. RDF classes can be used to represent any category ofthings, ranging from people, Web pages, document types, to abstract concepts. Classesare described by using the RDF Schema resources rdfs:Class and rdfs:Resource, and theproperties rdf:type and rdfs:subClassOf. The resources that belong to a class are called theinstances to that class. As an illustration, a hierarchy of classes in RDF Schema in graphnotation and the corresponding RDF/XML encoding are presented in Figures 5.6 and 5.7.An RDF/XML encoding that represents an instance of a class defined in the figures is alsoshown in Figure 5.8.Properties RDF Schema uses the RDF class rdf:Property, and the RDF Schema properties rdfs:-domain, rdfs:range, and rdfs:subPropertyOf to specifically describe properties that character-ize classes of things. All properties in RDF are described as instances of class rdf:Property.RDF Schema also provides vocabulary for describing how properties and classes are in-tended to be used together in RDF data, with the RDF Schema properties rdfs:range andrdfs:domain as the most important information to describe application-specific properties. The rdfs:range property is used to restrict that the values of a particular property areinstances of a specified class or given by a specific type of literals. The rdfs:domain prop-erty is used to indicate that a particular property applies to a specified class. Data typesof literals specified in rdfs:range are defined externally to RDF and RDF Schema. Datatypes may be defined by XML Schema data typing mechanisms (Biron and Malhotra, 2001)and are referred to in RDF statements by their URIrefs. Statements with rdfs:range serveto document the existence of data types and to indicate explicitly that they are to be usedin the schemas. Similar to classes, RDF Schema also provides a way to make specializa-tion on properties. The specialization relationship between two properties is described by 30

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