WSC-07: Evolving the Web Services Challenge

Service-oriented architecture (SOA) is an evolving architectural paradigm where businesses can expose their capabilities as modular, network-accessible software services. By decomposing capabilities into modular services, organizations can share their offerings at multiple levels of granularity while also creating unique access points for their peer organizations. The true impact of SOA will be realized when 3rd party organizations can obtain a variety of services, on-demand, and create higher-order composite business processes. The Web Services Challenge (WSC) is a forum where academic and industry researchers can share experiences of developing tools that automate the integration of web services. In the third year (i.e. WSC-07) of the Web Services Challenge, software platforms will address several new composition challenges. Requests and results will be transmitted within SOAP messages. In addition, semantic representations will be both represented in the eXtensible Markup Language (XML) and in the Web Ontology Language (OWL). Finally, composite processes will have both sequential and concurrent branches

A framework for cots software evaluation and selection for COTS mismatches handling and non-functional requirements

The decision to purchase Commercial Off-The-Shelf (COTS) software needs systematic guidelines so that the appropriate COTS software can be selected in order to provide a viable and effective solution to the organizations. However, the existing COTS software evaluation and selection frameworks focus more on functional aspects and do not give adequate attention to accommodate the mismatch between user requirements and COTS software specification, and also integration with non functional requirements of COTS software. Studies have identified that these two criteria are important in COTS software evaluation and selection. Therefore, this study aims to develop a new framework of COTS software evaluation and selection that focuses on handling COTS software mismatches and integrating the nonfunctional requirements. The study is conducted using mixed-mode methodology which involves survey and interview. The study is conducted in four main phases: a survey and interview of 63 organizations to identify COTS software evaluation criteria, development of COTS software evaluation and selection framework using Evaluation Theory, development of a new decision making technique by integrating Analytical Hierarchy Process and Gap Analysis to handle COTS software mismatches, and validation of the practicality and reliability of the proposed COTS software Evaluation and Selection Framework (COTS-ESF) using experts’ review, case studies and yardstick validation. This study has developed the COTS-ESF which consists of five categories of evaluation criteria: Quality, Domain, Architecture, Operational Environment and Vendor Reputation. It also provides a decision making technique and a complete process for performing the evaluation and selection of COTS software. The result of this study shows that the evaluated aspects of the framework are feasible and demonstrate their potential and practicality to be applied in the real environment. The contribution of this study straddles both the research and practical perspectives of software evaluation by improving decision making and providing a systematic guidelines for handling issue in purchasing viable COTS software

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

A methodology for transactional risk assessment and decision making in e-business interactions

The development and advancement of technologies have enabled users to complete their tasks efficiently. They have also provided them with various options and alternatives to complete and achieve their tasks. In such an environment, it is imperative for a user to make informed decisions that would ensure that its aims or objectives are achieved and its interaction experience is maximized. In the literature, various approaches for decision making have been proposed. But among those approaches, the notion of risk has been considerably ignored in the domain of e-business, despite its having been acknowledged as an important concept related to decision-making in any domain. In this paper, we propose a methodology by which users in an e-business domain can assess and analyze the level of transactional risk in the domain of e-business and then take it into consideration when making an informed interaction-based decision

Highlighting the issues in making an informed interaction-based decision in industrial ecosystems

In the literature, the notion of trust has been widely discussed and various approaches have been proposed to assess and quantify it, in order to take it into consideration when making an interaction-based decision in industrial ecosystems. At the same time, the notion and importance of risk have been widely ignored and not been acknowledged as an important factor to consider, along with trust; while making an interaction-based decision. Although there are approaches in the literature which attempt to quantify risk in the domain of a business interaction in industrial ecosystems, none of them proposes a methodology by which the initiating agent of the interaction can ascertain and determine all the various concepts that are expressed by risk in that domain, and then utilize it to make an informed interaction-based decision. In this paper we present an evaluation of the approaches from the literature and highlight the issues which need to be addressed in order to assist the initiating agent of the interaction in making an informed interaction-based decision with another agent in Industrial ecosystems

Past, present and future of mobile payments research: A literature review

The mobile payment services markets are currently under transition with a history of numerous tried and failed solutions, and a future of promising but yet uncertain possibilities with potential new technology innovations. At this point of the development, we take a look at the current state of the mobile payment services market from a literature review perspective. We review prior literature on mobile payments, analyze the various factors that impact mobile payment services markets, and suggest directions for future research in this still emerging field. To facilitate the analysis of literature, we propose a framework of four contingency and five competitive force factors, and organize the mobile payment research under the proposed framework. Consumer perspective of mobile payments as well as technical security and trust are best covered by contemporary research. The impacts of social and cultural factors on mobile payments, as well as comparisons between mobile and traditional payment services are entirely uninvestigated issues. Most of the factors outlined by the framework have been addressed by exploratory and early phase studies. </p

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde