bluuebunny/crossref_metadata_2025 huggingface dataset:
A Static Web Immune System and Its Robustness Analysis
Authors: Gong Tao
Published: May 2010

Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing techniques. On the Static Web system, a normal model is proposed with the space property and the time property of each component, in order to identify the normal state of the system that the artificial immune system protects. Based on the normal model, the Static Web immune sytsem is modelled with three tiers, that is the innate immune tier, the adaptive immune tier and the parallel immune tier. All the three tiers are inspired from the natural immune system. On the tri-tier immune model, the self detection mechanism is proposed and programmed based on the normal model, and the non-self detection is based on the self detection. Besides, the recognition of known non-selfs and unknown non-selfs are designed and analyzed. It is showed that the Static Web immune system is effective and useful for both theory and applications.
The Interactive Computing of Web Knowledge Flow - from Web to Knowledge Web
Authors: Luo Xiangfeng , Yu Jie
Published: May 2010

Web Knowledge Flow provides a technique and theoretical support for the effective discovery of knowledge innovation, intelligent browsing, personalized recommendation, cooperative team work, and the semantic analysis of resources on Internet, which is a key issue of Web services and Knowledge Grid/ Web(Zhuge, 2007; Zhuge, 2005). In this chapter, first the authors introduce some basic concepts related to Web Knowledge Flow. Next they illustrate the concepts of interactive computing, including the Web interaction model, the implementation of interactive computing and the generation of Web Knowledge Flow. Finally, the applications of Web Knowledge Flow will be given.
Modular Rule-Based Promgramming in 2APL
Authors: Dastani Medhi
Published: May 2010

Rule markup languages will be the vehicle for using rules on the Web and in other distributed systems. They allow publishing, deploying, executing and communicating rules in a network. They may also play the role of a lingua franca for exchanging rules between different systems and tools. In a narrow sense, a rule markup language is a concrete (XML-based) rule syntax for the Web. In a broader sense, it should have an abstract syntax as a common basis for defining various concrete languages addressing different consumers. The main purposes of a rule markup language are to permit the publication, interchange and reuse of rules. This chapter introduces important requirements and design issues for general Web rule languages to fulfill these tasks. Characteristics of several important general standardization or standards-proposing efforts for (XML-based) rule markup languages including W3C RIF, RuleML, R2ML, SWRL as well as (human-readable) Semantic Web rule languages such as TRIPLE, N3, Jena, and Prova are discussed with respect to these identified issues.
Graphical Notations for Rule Modeling
Authors: Lukichev Sergey , Jarrar Mustafa
Published: May 2010

This chapter describes various graphical notations for rule modeling. Rule modeling methodologies, empowered with graphical notations, play an important role in helping business experts and rule engineers to represent business rules formally for further deployment into a rule execution system. Rules, represented graphically, can be easier understood by business people and by technicians without intensive technical learning. In this chapter we mainly focus on three graphical notations for rules: UML/OCL, URML and ORM. UML/OCL is a mainstream modeling technology in software development, which is also accommodated by some business experts when modeling a system at the semi-formal, platform independent level. URML extends UML with additional graphical symbols and the concept of a rule, which allows visualization of different rule types on top of UML class diagrams. ORM is an alternative methodology with a rich graphical notation for modeling a domain at the conceptual level. The methodological power, graphical expressivity, and verbalization capabilities of ORM have made it the most popular language within the business rules community. This chapter introduces each of these graphical notations, explain how it can be used, and compare them against each other.
ROL2
Authors: Liu Mengchi
Published: May 2010

Deductive object-oriented databases are intended to integrate the deductive and object-oriented database techniques to combine the best of two approaches and to overcome their inherent shortcomings, with a number of deductive object-oriented database languages proposed. However, most of these languages are only structurally object-oriented. Important behaviorally object-oriented features such as methods and encapsulation common in object-oriented database systems are not properly supported. This chapter presents a novel deductive object-oriented database language called ROL2 that extends structurally object-oriented database language ROL with all behaviorally object-oriented features. It supports in a rule-based framework all important object-oriented features such as object identity, complex objects, typing, information hiding, rule-based methods, encapsulation of such methods, overloading, late binding, polymorphism, class hierarchies, multiple structural and behavioral inheritance with overriding, blocking, and conflict handling. It is so far the only deductive object-oriented database language that supports all these features in a uniform rule-based framework.
Agile Development of Rule Systems
Authors: Baumeister Joachim , Seipel Dietmar , Puppe Frank
Published: May 2010

The engineering of rule-based systems was a relevant issue in the past decades and has become more attractive recently due to the emergence of intelligent applications on the web. For example, web application servers including rule engines and the rule-based description of (semantic) web services. This development allows for a natural formalization of business logic but also poses new challenges with respect to the acquisition and evolution of rule-based knowledge. Although, a lot of research has been done in the past, the practical engineering and life cycle of larger rule-based systems still remains to be a difficult and complex task. State-of-the-art tools for rule development provide extensive support for the engineering, the analysis and debugging of such systems, but a simple and easily adoptable methodology is missing. In the best case, such a methodology should be well understood even with little training and should provide techniques to ensure quality, evolutionary health, and cost control of a rule base project. We present an agile methodology for the development and evolution of rule-based systems. Intuitive concepts like the system metaphor, the planning game, and the implementation cycle make its adoption to arbitrary projects very easy. With the promotion of continuous techniques such as automated testing and refactoring we cope with evolutionary aspects of knowledge bases.
Rules Verification and Validation
Authors: Ligeza Antoni , Nalepa Grzegorz
Published: May 2010

In this chapter an introduction to verification of rules is presented. Logical models of rules, knowledge representation languages and inference rules are introduced in brief. Required characteristics of rulebased systems are analyzed and issues of formal verification techniques for rules are presented in details. The aim is to show how the quality of rules can be improved or kept at a relatively high level, even in environments with numerous active rules. This involves verification methods for rules discussing the expressive power issues of different rule representation logical languages. Moreover, verification checks for rules incorporating consistency, redundancy, completeness and minimal form are presented.
From Business Rules to Application Code
Authors: Dietrich Jens
Published: May 2010

Rules that define relationships between objects are an important part of the specifications of software systems. However, support for the explicit representation of those rules in modelling languages is still immature and there is little support to assist software engineers in implementing them. The result of this practice is hand-crafted and error-prone applications. In this chapter, we analyse some common patterns used to implement rules and discuss the shortcomings associated with those patterns. We then discuss several options to explicitly represent rules, and how to automate the generation of application code from rules.
The Agent Object Relationship Simulation as a Business Process
Authors: Pascalau Emilian , Giuca Adrian , Wagner Gerd
Published: May 2010

The use of agent-based simulation models is growing and attracted a lot of attention recently both for researchers and business management. Agent-Object Relationship (AOR) is an agent-based simulation paradigm that uses reaction rules to model agents’ behavior. The goal of this chapter, besides exemplifying the AOR concepts by means of a use case, is to investigate the use of business process modeling notation (BPMN) to model the AOR simulation process. Moreover it discusses aspects of a distributed architecture for an AOR simulation system. The chapter concludes with the fact that BPMN is well suited to model the AOR simulation process.
Developing Rule-Based Web Applications
Authors: Paptaxiarhis Vassilis , Tsetsos Vassileios , Karali Isambo , Stamotopoulos Panagiotis
Published: May 2010

Embedding rules into Web applications, and distributed applications in general, seems to constitute a significant task in order to accommodate desired expressivity features in such environments. Various methodologies and reasoning modules have been proposed to manage rules and knowledge on the Web. The main objective of the chapter is to survey related work in this area and discuss relevant theories, methodologies and tools that can be used to develop rule-based applications for the Web. The chapter deals with both ways that have been formally defined for modeling a domain of interest: the first based on standard logics while the second one stemmed from the logic programming perspective. Furthermore, a comparative study that evaluates the reasoning engines and the various knowledge representation methodologies, focusing on rules, is presented.
Sharing Ontologies and Rules Using Model Transformations
Authors: Milanovic Milan , Djuric Dragan , Gasevic Dragan , Devedzic Vladan
Published: May 2010

Web Ontology Language (OWL), Semantic Web Rule Language (SWRL) and Model-Driven Engineering (MDE) are technologies being developed in parallel, but by different communities. They have common points and issues and can be brought closer together. Many authors have so far stressed this problem and have proposed several solutions. The result of these efforts is the recent OMG’s initiative for defining an ontology development platform. However, the problem of transformation between Semantic Web ontology and rule languages and MDE-based languages has been solved using rather partial and ad hoc solutions, most often by XSLT. In this paper, we relations between the Semantic Web languages and MDE-compliant languages as separate technical spaces. In order to achieve a synergy between these technical spaces, we present ontology and rule languages in terms of MDE standards, recognize relations between the OWL and SWRL langauges and MDE-based ontology languages, and propose mapping techniques. In order to illustrate the approach, we use an MDE-defined architecture that includes the ontology and rule metamodels and ontology UML Profile. We also show how MDE techniques, such as model transformations, can be used to enable sharing rules and ontologies by using REWERSE Rule Markup Language (R2ML), a proposal for a general rule language. The main benefit of this approach is that it keeps the focus on the language concepts (i.e., languages’ abstract syntax - metamodels) rather than on technical issues caused by different concrete syntax. Yet, we also provide transformations that bridge between both languages’ concrete (XML) and abstract (MOF) syntax.
Automated Interpretation of Key Performance Indicators by Using Rules
Authors: Tomic Bojan
Published: May 2010

Business reporting is an essential task for every enterprise. In order to make appropriate decisions, decision makers need quality reports. Some recent articles suggest that reports generated by BI (Business Intelligence) systems contain mostly data (key performance indicator values) and little or no information. Data has no meaning and must be interpreted in order to become information. Information is, naturally, much more useful because it directly contributes to recipients’ knowledge and can be acted upon. The consequence is that it is left to the decision maker to manually analyze large quantities of data presented in individual reports in order to derive information. A potential solution for automated business data interpretation is presented in this chapter. It proposes using rules to capture and formalize business knowledge and then utilizing these rules to infer information from data automatically.
Mining Association Rules
Authors: Gabroveanu Mihai
Published: May 2010

During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.
Ontology Evolution
Authors: Weiss Christian , Gulla Jon Atle , Liu Jin , Brasethvik Terje , Burkhardt Felix
Published: May 2010

As semantic web technologies, including semantic search, have matured from visions to practical applications, this chapter describes a case study of (semi-) automatic construction and maintenance of ontologies and their applications to the media domain. A substantial amount of work has been done and will be done to integrate semantic search technologies into all kind of services where the underlying data is crucial for the success of automatic processing. Semantic search technologies should help both the user-to-machine and machine-to-machine communication to understand the meaning behind the data as well as to retrieve information according to user’s requests and needs. The crucial question is how to manage the semantic content (meaning) and how to deliver it in order to increase the value-chain of users’ benefits. Ontologies provide the basis for machine-based data understanding and ontology-based semantic search as they play a major role in allowing semantic access to data resources. However, the human effort needed for creating, maintaining and extending ontologies is often unreasonably high. In order to reduce effort for engineering and managing ontologies, the authors have developed a general framework for ontology learning from text. This framework has been applied in the media domain, in particular to video, music and later on to game search to offer an extended user experience in machineto machine as well as user-machine interaction.
The Power and Promise of Web 2.0 Tools
Authors: Page G. Andrew , Ali Radwan
Published: May 2010

The key idea that sets constructivism apart from other theories of cognition was launched about 60 years ago by Jean Piaget. It was the idea that what is called knowledge does not and cannot have the purpose of producing representations of an independent reality, but instead has an adaptive function (Von Glasersfeld, 1996, p.3). In this chapter, a variety of Web 2.0 applications and their affordances are presented and discussed in relation to constructivism in higher education. The aim is to explain how these applications can be used in higher education to promote interactive and engaging learning environments. Recommendations for harnessing the potential of these tools along with practical examples will assist facilitators of higher education with creative means to design their courses and thus promote Learning 2.0.
What are Gene Regulatory Networks?
Authors: de la Fuente Alberto
Published: May 2010

This book deals with algorithms for inferring and analyzing Gene Regulatory Networks using mainly gene expression data. What precisely are the Gene Regulatory Networks that are inferred by such algorithms from this type of data? There is still much confusion in the current literature and it is important to start a book about computational methods for Gene Regulatory Networks with a definition that is as unambiguous as possible. In this chapter, I provide a definition and try to clearly explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. To do the latter in a formal way, I will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed’ into the more compact description of Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, such as Transcriptional Regulatory Networks and Co-Expression Networks, will be highlighted.
Bayesian Networks for Modeling and Inferring Gene Regulatory Networks
Authors: Bauer Sebastian , Robinson Peter
Published: May 2010

Bayesian networks have become a commonly used tool for inferring structure of gene regulatory networks from gene expression data. In this framework, genes are mapped to nodes of a graph, and Bayesian techniques are used to determine a set of edges that best explain the data, that is, to infer the underlying structure of the network. This chapter begins with an explanation of the mathematical framework of Bayesian networks in the context of reverse engineering of genetic networks. The second part of this review discusses a number of variations upon the basic methodology, including analysis of discrete vs. continuous data or static vs. dynamic Bayesian networks, different methods of exploring the potentially huge search space of network structures, and the use of priors to improve the prediction performance. This review concludes with a discussion of methods for evaluating the performance of network structure inference algorithms.
Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model
Authors: Bulashevska Svetlana
Published: May 2010

This chapter describes the model of genetic regulatory interactions. The model has a Boolean logic semantics representing the cooperative influence of regulators (activators and inhibitors) on the expression of a gene. The model is a probabilistic one, hence allowing for the statistical learning to infer the genetic interactions from microarray gene expression data. Bayesian approach to model inference is employed enabling flexible definitions of a priori probability distributions of the model parameters. Markov Chain Monte Carlo (MCMC) simulation technique Gibbs sampling is used to facilitate Bayesian inference. The problem of identifying actual regulators of a gene from a high number of potential regulators is considered as a Bayesian variable selection task. Strategies for the definition of parameters reducing the parameter space and efficient MCMC sampling methods are the matter of the current research.
A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networks
Authors: Radde Nicole , Kaderali Lars
Published: May 2010

Differential equation models provide a detailed, quantitative description of transcription regulatory networks. However, due to the large number of model parameters, they are usually applicable to small networks only, with at most a few dozen genes. Moreover, they are not well suited to deal with noisy data. In this chapter, we show how to circumvent these limitations by integrating an ordinary differential equation model into a stochastic framework. The resulting model is then embedded into a Bayesian learning approach. We integrate the-biologically motivated-expectation of sparse connectivity in the network into the inference process using a specifically defined prior distribution on model parameters. The approach is evaluated on simulated data and a dataset of the transcriptional network governing the yeast cell cycle.
Computational Approaches for Modeling Intrinsic Noise and Delays in Genetic Regulatory Networks
Authors: Barrio Manuel , Burrage Kevin , Burrage Pamela , Leier André , Márquez Lago Tatiana
Published: May 2010

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).
Modeling Gene Regulatory Networks with Delayed Stochastic Dynamics
Authors: Ribeiro Andre S. , Grefenstette John J. , Kauffman Stuart A.
Published: May 2010

We present a recently developed modeling strategy of gene regulatory networks (GRN) that uses the delayed stochastic simulation algorithm to drive its dynamics. First, we present experimental evidence that led us to use this strategy. Next, we describe the stochastic simulation algorithm (SSA), and the delayed SSA, able to simulate time-delayed events. We then present a model of single gene expression. From this, we present the general modeling strategy of GRN. Specific applications of the approach are presented, beginning with the model of single gene expression which mimics a recent experimental measurement of gene expression at single-protein level, to validate our modeling strategy. We also model a toggle switch with realistic noise and delays, used in cells as differentiation pathway switches. We show that its dynamics differs from previous modeling strategies predictions. As a final example, we model the P53-Mdm2 feedback loop, whose malfunction is associated to 50% of cancers, and can induce cells apoptosis. In the end, we briefly discuss some issues in modeling the evolution of GRNs, and outline some directions for further research.
A Synthesis Method of Gene Regulatory Networks based on Gene Expression by Network Learning
Authors: Mori Yoshihiro , Kuroe Yasuaki
Published: May 2010

Investigating gene regulatory networks is important to understand mechanisms of cellular functions. Recently, the synthesis of gene regulatory networks having desired functions has become of interest to many researchers because it is a complementary approach to understanding gene regulatory networks, and it could be the first step in controlling living cells. In this chapter, we discuss a synthesis problem in gene regulatory networks by network learning. The problem is to determine parameters of a gene regulatory network such that it possesses given gene expression pattern sequences as desired properties. We also discuss a controller synthesis method of gene regulatory networks. Some experiments illustrate the performance of this method.
Complexity of the BN and the PBN Models of GRNs and Mappings for Complexity Reduction
Authors: Ivanov Ivan V.
Published: May 2010

Constructing computational models of genomic regulation faces several major challenges. While the advances in technology can help in obtaining more and better quality gene expression data, the complexity of the models that can be inferred from data is often high. This high complexity impedes the practical applications of such models, especially when one is interested in developing intervention strategies for disease control, for example, preventing tumor cells from entering a proliferative state. Thus, estimating the complexity of a model and designing strategies for complexity reduction become crucial in problems such as model selection, construction of tractable sub-network models, and control of the dynamical behavior of the model. In this chapter we discuss these issues in the setting of Boolean networks and probabilistic Boolean networks – two important classes of network models for genomic regulatory networks.
Generalized Boolean Networks
Authors: Darabos Christian , Giacobini Mario , Tomassini Marco
Published: May 2010

Random Boolean Networks (RBN) have been introduced by Kauffman more than thirty years ago as a highly simplified model of genetic regulatory networks. This extremely simple and abstract model has been studied in detail and has been shown capable of extremely interesting dynamical behavior. First of all, as some parameters are varied such as the network’s connectivity, or the probability of expressing a gene, the RBN can go through a phase transition, going from an ordered regime to a chaotic one. Kauffman’s suggestion is that cell types correspond to attractors in the RBN phase space, and only those attractors that are short and stable under perturbations will be of biological interest. Thus, according to Kauffman, RBN lying at the edge between the ordered phase and the chaotic phase can be seen as abstract models of genetic regulatory networks. The original view of Kauffman, namely that these models may be useful for understanding real-life cell regulatory networks, is still valid, provided that the model is updated to take into account present knowledge about the topology of real gene regulatory networks, and the timing of events, without loosing its attractive simplicity. According to present data, many biological networks, including genetic regulatory networks, seem, in fact, to be of the scale-free type. From the point of view of the timing of events, standard RBN update their state synchronously. This assumption is open to discussion when dealing with biologically plausible networks. In particular, for genetic regulatory networks, this is certainly not the case: genes seem to be expressed in different parts of the network at different times, according to a strict sequence, which depends on the particular network under study. The expression of a gene depends on several transcription factors, the synthesis of which appear to be neither fully synchronous nor instantaneous. Therefore, we have recently proposed a new, more biologically plausible model. It assumes a scale-free topology of the networks and we define a suitable semi-synchronous dynamics that better captures the presence of an activation sequence of genes linked to the topological properties of the network. By simulating statistical ensembles of networks, we discuss the attractors of the dynamics, showing that they are compatible with theoretical biological network models. Moreover, the model demonstrates interesting scaling abilities as the size of the networks is increased.
A Model for a Heterogeneous Genetic Network
Authors: Gonçalves Ângela T.F. , Costa Ernesto J.F.
Published: May 2010

In this chapter, we propose a new model for gene regulatory networks (GRN). The model incorporates more biological detail than other approaches, and is based on an artificial genome from which several products like genes, mRNA, miRNA, noncoding RNA, and proteins are extracted and connected, giving rise to a heterogeneous directed graph. We study the dynamics of the networks thus obtained, along with their topology (using degree distributions). Some considerations are made about the biological meaning of the outcome of the simulations.
Planning Interventions for Gene Regulatory Networks as Partially Observable Markov Decision Processes
Authors: Bryce Daniel , Kim Seungchan
Published: May 2010

In this chapter, a computational formalism for modeling and reasoning about the control of biological processes is explored. It comprises five main sections: a survey of related work, a background on methods (including discussion of the Wnt5a gene regulatory network, the coefficient of determination method for deriving gene regulatory network models, and the partially observable Markov decision process model and its role in modeling intervention planning problems), a main section on the approach taken (including algorithms for solving the intervention planning problems and techniques for representing components of the problems), an empirical evaluation of the intervention planning algorithms on synthetic and the Wnt5a gene regulatory networks, and a conclusion and future directions section. The techniques described present a promising avenue of future research in reasoning algorithms for improved scalability in planning interventions in gene regulatory networks.
Effort-Accuracy Trade-Off in Using Knowledge Management Systems
Authors: Poston Robin S. , Speier Cheri
Published: May 2010

To solve complicated problems, people often seek input from others. Knowledge management systems (KMSs) provide help in this activity by offering a computer-mediated approach to information sharing. However, if the KMS contains content that is obsolete or incomplete, those using the system may expend greater amounts of effort to detect what content is worthwhile or they risk relying on poor inputs, which may lead to less accurate solutions to their problems. As a result, most KMSs include rating schemes as part of the user interface designed to help those using the system identify high-quality content. Rating schemes depend on current users rating the quality of the existing content, guiding subsequent users in future content searches. If specific ratings are low in validity, then they may not reflect the true content quality (unintentionally or intentionally). This chapter provides a robust summary of the KMS literature and draws on the effort-accuracy trade-off framework to offer the results of a research study. The research study examines how rating validity influences how KMS users employ their limited cognitive resources to search and evaluate KMS content, with the goal of finding and using the highest-quality content. Through an experimental design, the study described herein manipulates rating validity and content quality in a replicated KMS setting and examines how users trade off search and evaluation effort. The results of the study demonstrate that rating validity differentially influences how KMS search and evaluation effort relates to decision accuracy. The chapter concludes with a discussion of the study findings and ideas for future research.
General and Specific Computer Self-Efficacy
Authors: Downey James P. , Rainer Jr. R. Kelly , Bartczak Summer E.
Published: May 2010

Computer self-efficacy is known to operate at multiple levels, from application-specific sub-domains like spreadsheets to a judgment of ability for the entire computing domain (general computer self-efficacy- GCSE). Conventional wisdom and many recent studies contend that the level of self-efficacy (specific to general) should match the level of its related constructs to maximize predictive power (Bandura, 1997; Chen, et al., 2001; Pajares, 1996). This thinking claims, for example, that GCSE should be used with a general attitude like computer anxiety (and vice versa). This study examines whether such a limitation is theoretically and empirically sound, given that SE judgments generalize across domains. Results indicate any self-efficacy judgment (specific or general) significantly relates to both general and domain-specific constructs. These results suggest that an individual’s cognitive processing of ability level is multi-faceted; that is, every SE judgment consists of general and specific components. The implication is that CSE is simultaneously generalizable and formative in nature. The results also suggest that the relationship between general and specific CSE is mediated by one’s ability level in the specific domain.
Hypertinence, Serendipity or Elicitation of Passion for Knowledge? Some Critical Elements of Online Learning by Using Web 2.0 Resources
Authors: Marchi Simona
Published: May 2010

Learning processes depend on the socio-technical and regulatory contexts in which professional practices and daily usage take place. These processes develop out of and through various systems of activities, consisting of subjects, artefacts, rules, knowledge, and roles. What happens when the rules governing these systems, the roles, artefacts and knowledge change? For instance, what happens to learning processes when the settings in which practices take place are virtual, when they occur, say, in a blog, or in a community, or on a social networking platform? In this chapter the author intends to examine in detail the specific features of learning processes taking place in these new online environments.
Web 2.0 Technologies
Authors: Torres-Coronas Teresa , Monclús-Guitart Ricard , Rodríguez-Merayo Araceli , Vidal-Blasco M. Arántzazu , Simón-Olmos M. José
Published: May 2010

Web 2.0 technologies are playing an important role in building social capital through increasing flows of information, and building on knowledge and human capacity of learning. The purpose of this chapter is to show the role that social software, a component of Web 2.0 technologies, can play in higher education and adult learning. This chapter focuses on the role of Web 2.0 technologies in promoting learning. New learning paradigms and pedagogical applications are also discussed.
Simulation Environments as Vocational and Training Tools
Authors: Abu-Taieh Evon M.O. , Abutayeh Jeihan M.O.
Published: May 2010

This paper investigates over 50 simulation packages and simulators used in vocational and course training in many fields. Accordingly, the 50 simulation packages were categorized in the following fields: Pilot Training, Chemistry, Physics, Mathematics, Environment and ecological systems, Cosmology and astrophysics, Medicine and Surgery training, Cosmetic surgery, Engineering – Civil engineering, architecture, interior design, Computer and communication networks, Stock Market Analysis, Financial Models and Marketing, Military Training and Virtual Reality. The incentive for using simulation environments as vocational and training tools is to save live, money and effort.
Agent-Based Modeling
Authors: Heath Brian L. , Hill Raymond R.
Published: May 2010

Models and simulations have been widely used as a means to predict the performance of systems. Agentbased modeling and agent distillations have recently found tremendous success particularly in analyzing ground force employment and doctrine. They have also seen wide use in the social sciences modeling a plethora of real-life scenarios. The use of these models always raises the question of whether the model is correctly encoded (verified) and accurately or faithfully represents the system of interest (validated). The topic of agent-based model verification and validation has received increased interest. This chapter traces the historical roots of agent-based modeling. This review examines the modern influences of systems thinking, cybernetics as well as chaos and complexity on the growth of agent-based modeling. The chapter then examines the philosophical foundations of simulation verification and validation. Simulation verification and validation can be viewed from two quite different perspectives: the simulation philosopher and the simulation practitioner. Personnel from either camp are typically unaware of the other camp’s view of simulation verification and validation. This chapter examines both camps while also providing a survey of the literature and efforts pertaining to the verification and validation of agent-based models. The chapter closes with insights pertaining to agent-based modeling, the verification and validation of agent-based models, and potential directions for future research.
Verification and Validation of Simulation Models
Authors: Aboud Sattar J. , Al Fayoumi Mohammad , Alnuaimi Mohamed
Published: May 2010

Unfortunately, cost and time are always restraints; the impact of simulation models to study the dynamic system performance is always rising. Also, with admiration of raising the network security models, the complexity of real model applications is rising too. As a result, the complexity of simulation models applications is also rising and the necessary demand for designing a suitable verification and validation systems to ensure the system reliability and integrality is very important. The key requirement to study the system integrity is to verify the system accuracy and to validate its legality regarding to prespecified applications causes and validly principles. This needs different plans, and application phases of simulation models to be properly identified, and the output of every part is properly documented. This chapter discusses validation and verification of simulation models. The different approaches to deciding model validity are presented; how model validation and verification relate to the model development process are discussed; various validation techniques are defined; conceptual model validity, model verification, operational validity, and data validity; superior verification and validation technique for simulation models relied on a multistage approach are described; ways to document results are given; and a recommended procedure is presented.
DEVS-Based Simulation Interoperability
Authors: Wutzler Thomas , Sarjoughian Hessam
Published: May 2010

This chapter introduces the usage of DEVS for the purpose of implementing interoperability across heterogeneous simulation models. It shows that the DEVS framework provides a simple, yet effective conceptual basis for handling simulation interoperability. It discusses the various useful properties of the DEVS framework, describes the Shared Abstract Model (SAM) approach for interoperating simulation models, and compares it to other approaches. The DEVS approach enables formal model specification with component models implemented in multiple programming languages. The simplicity of the integration of component models designed in the DEVS, DTSS, and DESS simulation formalisms and implemented in the programming languages Java and C++ is demonstrated by a basic educational example and by a real world forest carbon accounting model. The authors hope, that readers will appreciate the combination of generalness and simplicity and that readers will consider using the DEVS approach for simulation interoperability in their own projects.
Efficient Discrete Simulation of Coded Wireless Communication Systems
Authors: Sebastião Pedro J.A. , Cercas Francisco A.B. , Cartaxo Adolfo V.T.
Published: May 2010

Simulation can be a valuable tool for wireless communication system’s (WCS) designers to assess the performance of its radio interface. It is common to use the Monte Carlo simulation method (MCSM), although this is quite time inefficient, especially when it involves forward error correction (FEC) with very low bit error ratio (BER). New techniques were developed to efficiently evaluate the performance of the new class of TCH (Tomlinson, Cercas, Hughes) codes in an additive white Gaussian noise (AWGN) channel, due to their potential range of applications. These techniques were previously applied using a satellite channel model developed by Lutz with very good results. In this chapter, we present a simulation method, named accelerated simulation method (ASM), that provides a high degree of efficiency and accuracy, namely for lower BER, where the application of methods like the MCSM is prohibitive, due to high computational and time requirements. The present work generalizes the application of the ASM to a WCS modelled as a stochastic discrete channel model, considering a real channel, where there are several random effects that result in random energy fluctuations of the received symbols. The performance of the coded WCS is assessed efficiently, with soft-decision (SD) and hard-decision (HD) decoding. We show that this new method already achieves a time efficiency of two or three orders of magnitude for SD and HD, considering a BER = 1x10-4 , when compared to MCSM. The presented performance results are compared with the MCSM, to check its accuracy.
Trying Out Reflective Petri Nets on a Dynamic Workflow Case
Authors: Capra Lorenzo , Cazzola Walter
Published: May 2010

Industrial/business processes are an evident example of discrete-event systems which are subject to evolution during life-cycle. The design and management of dynamic workflows need adequate formal models and support tools to handle in sound way possible changes occurring during workflow operation. The known, well-established workflow’s models, among which Petri nets play a central role, are lacking in features for representing evolution. We propose a recent Petri net-based reflective layout, called Reflective Petri nets, as a formal model for dynamic workflows. A localized open problem is considered: how to determine what tasks should be redone and which ones do not when transferring a workflow instance from an old to a new template. The problem is efficiently but rather empirically addressed in a workflow management system. Our approach is formal, may be generalized, and is based on the preservation of classical Petri nets structural properties, which permit an efficient characterization of workflow’s soundness.
Applications of Visual Algorithm Simulation
Authors: Korhonen Ari
Published: May 2010

Understanding data structures and algorithms is an integral part of software engineering and elementary computer science education. However, people usually have difficulty in understanding abstract concepts and processes such as procedural encoding of algorithms and data structures. One way to improve their understanding is to provide visualizations to make the abstract concepts more concrete. In this chapter, we represent a novel idea to promote the interaction between the user and the algorithm visualization system called visual algorithm simulation. As a proof of concept, we represent an application framework called Matrix that encapsulates the idea of visual algorithm simulation. The framework is applied by the TRAKLA2 learning environment in which algorithm simulation is employed to produce algorithm simulation exercises. Moreover, we discuss the benefits of such exercises and applications of visual algorithm simulation in general.
Implementation of a DES Environment
Authors: Lipovszki Gyorgy , Molnar Istvan
Published: May 2010

In this chapter the authors describe a program system that implements a Discrete Event Simulation (DES) development environment. The simulation environment was created using the LabVIEW graphical programming system; a National Instruments software product. In this programming environment, the user can connect different procedures and data structures with “graphical wires” to implement a simulation model, thereby creating an executable simulation program. The connected individual objects simulate a discrete event problem. The chapter describes all simulation model objects, their attributes and methods. Another important element of the discrete event simulator is the task list, which has also been created using task type objects. The simulation system uses the “next event simulation” technique and refreshes the actual state (attribute values of all model objects) at every event. The state changes are determined by the entity objects, their input, current content, and output. Every model object can access (read) all and modify (write) a selected number of object attribute values. This property of the simulation system provides the possibility to build a complex discrete event system using predefined discrete event model objects.
An Integrated Data Mining and Simulation Solution
Authors: Alnoukari Mouhib , El Sheikh Asim , Alzoabi Zaidoun
Published: May 2010

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education
The State of Computer Simulation Applications in Construction
Authors: Marzouk Mohamed
Published: May 2010

Construction operations are performed under different working conditions including (but not limited to) unexpected weather conditions, equipment breakdown, delays in procurement, etc. As such, computer simulation is considered an appropriate technique for modeling the randomness of construction operations. Several construction processes and operations have been modeled utilizing computer simulation such as earthmoving, construction of bridges and tunnels, concrete placement operations, paving processes, and coordination of cranes operations. This chapter presents an overview of computer simulation efforts that have been performed in the area of construction engineering and management. Also, it presents two computer simulation applications in construction; earthmoving and construction of bridges’ decks. Comprehensive case studies are worked out to illustrate the practicality of using computer simulation in scheduling construction projects, taking into account the associated uncertainties inherited in construction operations.
An Ontological Approach to Online Instructional Design
Authors: Zheng Robert Z. , Dahl Laura B.
Published: May 2010

This chapter introduces the ontological instructional design as an alternative to the traditional instructional design in teaching and learning. By comparing the differences between traditional instructional design and e-Learning, the authors suggest that instructional design in e-Learning require a different model than the existing traditional models due to the idiosyncratic nature of e-Learning in terms of population, environment, and resources. An ontological instructional design model is proposed with a focus on the sharability, reusability and interoperability of ontological entities and design components within the ontological entities, which provides a holistic approach to online instructional design compared to the segmented, linear design approach in traditional instructional design models. A case study is included to illustrate the use and application of the ontological instructional design model in an online business course. Finally, guidelines for implementing the model are made with suggestions for future research.