Nik Bessis and Fatos Xhafa (Eds.)

Next Generation Da ta Technologies for Collective Computational Intelligence

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Vol. 352. Nik B essis and Fatos Xhafa (Eds.)

Next Generation Data Technologies for Collective

Computational Intelligence, 2011

ISBN 978-3-642-20343-5

NikBessisandFatosXhafa(Eds.)

Ne xt Ge nerat i on Data

Technolog ies for Colle ctive

Computational Intelligence

123

Professor Nik B essis

School of Computing & Maths

University of Derby

Derby, DE22 1GB

United Kingdom (UK)

E-mail: n.bessis@derby.ac.uk

Dr. Fatos Xhafa

Profess or Titular d’Universitat

Dept de Llenguatges i Sistemes Informàtics

Universitat Politècnica de Catalunya

Barcelona, Spain

E-mail: [email protected]pc.edu

ISBN 978-3-642-20343-5 e-ISBN 978-3-642-20344-2

DOI 10.1007/978-3-642-20344-2

Studies in Computational Intelligence ISSN 1860-949X

Library of Congress Control Number: 2011925383

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Foreword

It is a great honor to me to write a foreword for this book on "Next Generation

Data Technologies for Collective Computational Intelligence". With the rapid

development of the Internet, the volume of data being created and digitized is

growing at an unprecedented rate, which if combined and analyzed through a

collective and computational intelligence manner will make a difference in the

organizational settings and their user communities.

The focus of this book is on next generation data technologies in support of col-

lective and computational intelligence. The book distinguish itself from others in

that it brings various next generation data technologies together to capture, inte-

grate, analyze, mine, annotate and visualize distributed data – made available from

various community users – in a meaningful and collaborative for the organization

manner.

This book offers a unique perspective on collective computational intelligence,

embracing both theory and strategies fundamentals such as data clustering, graph

partitioning, collaborative decision making, self-adaptive ant colony, swarm and

evolutionary agents. It also covers emerging and next generation technologies in

support of collective computational intelligence such as Web 2.0 enabled social

networks, semantic web for data annotation, knowledge representation and infer-

ence, data privacy and security, and enabling distributed and collaborative para-

digms such as P2P computing, grid computing, cloud computing due to the nature

that data is usually geographically dispersed and distributed in the Internet envi-

ronment.

This book will be of great interest and help to those who are broadly involved

in the domains of computer science, computer engineering, applied informatics,

business or management information systems. The reader group might include re-

searchers or senior graduates working in academia; academics, instructors and

senior students in colleges and universities, and software developers.

Dr. Maozhen Li

Brunel University, UK

Preface

Introduction

The use of collaborative decision and management support systems has evolved

over the years through developments in distributed computational science in a

manner, which provides applicable intelligence in decision-making. The rapid de-

velopments in networking and resource integration domains have resulted in the

emergence and in some instances to the maturation of distributed and collabora-

tive paradigms such as Web Services, P2P, Grid and Cloud computing, Data

Mashups and Web 2.0. Recent implementations in these areas demonstrate the ap-

plicability of the aforementioned next generation technologies in a manner, which

seems the panacea for solving very complex problems and grand challenges. A

broad range of issues are currently being addressed; however, most of these de-

velopments are focused on developing the platforms and the communication and

networking infrastructures for solving these very complex problems, which in

most instances are well-known challenges. The enabling nature of these technolo-

gies allows us to visualize their collaborative and synergetic use in a less conven-

tional manner, which are currently problem focused.

In this book, the focus is on the viewpoints of the organizational setting as well

as on the user communities, which those organizations cater to. The book appreci-

ates that in many real-world situations an understanding – using computational

techniques – of the organization and the user community needs is a computational

intelligence itself. Specifically, current Web and Web 2.0 implementations and fu-

ture manifestations will store and continuously produce a vast amount of distrib-

uted data, which if combined and analyzed through a collective and computational

intelligence manner using next generation data technologies will make a differ-

ence in the organizational settings and their user communities. Thus, the focus of

this book is about the methods and technologies which bring various next genera-

tion data technologies together to capture, integrate, analyze, mine, annotate and

visualize distributed data – made available from various community users – in a

meaningful and collaborative for the organization manner.

In brief, the overall objective of this book is to encapsulate works incorporating

various next generation distributed and other emergent collaborative data tech-

nologies for collective and computational intelligence, which are also applicable

in various organizational settings. Thus, the book aims to cover in a comprehen-

sive manner the combinatorial effort of utilizing and integrating various next

generation collaborative and distributed data technologies for computational intel-

ligence in various scenarios. The book also distinguishes itself by focusing on

VIII Preface

assessing whether utilization and integration of next generation data technologies

can assist in the identification of new opportunities, which may also be strategi-

cally fit for purpose.

Who Should Read the Book?

The content of the book offers state-of-the-art information and references for work

undertaken in the challenging area of collective computational intelligence using

emerging distributed computing paradigms. Thus, the book should be of particular

interest for:

Researchers and doctoral students working in the area of distributed data

technologies, collective intelligence and computational intelligence, primarily as a

reference publication. The book should be also a very useful reference for all re-

searchers and doctoral students working in the broader fields of data technologies,

distributed computing, collaborative technologies, agent intelligence, artificial in-

telligence and data mining.

Academics and students engaging in research informed teaching and/or

learning in the above fields. The view here is that the book can serve as a good

reference offering a solid understanding of the subject area.

Professionals including computing specialists, practitioners, managers and

consultants who may be interested in identifying ways and thus, applying a num-

ber of well defined and/or applicable cutting edge techniques and processes within

the domain area.

Book Organization and Overview

The book contains 22 self-contained chapters that were very carefully selected

based on peer review by at least two expert and independent reviewers. The book

is organized into four parts according to the thematic topic of each chapter.

Part I: Foundations and Principles

The part focuses on presenting state-of-the-art reviews on the foundations, princi-

ples, methods and techniques for collective and computational intelligence. In

particular:

Chapter 1 illustrates the space-based computing paradigm aiming to support and

facilitate software developers in their efforts to control complexity regarding con-

cerns of interaction in software systems.

Chapter 2 presents a state-of-the-art review on ant colony optimization and data

mining techniques and focus on their use for data classification and clustering.

They briefly present related applications and examples and outline possible future

trends of this promising collaborative use of techniques.

Preface IX

Chapter 3 offers a high-level introduction to the open semantic enterprise architec-

ture. Because of its open nature it is free to adopt and extend, yet retains a root

commonality to ensure all participating agents can agree on a common under-

standing without ambiguity, regardless of the underlying ontology or logic system

used.

Chapter 4 discusses and evaluates techniques for automatically classifying and co-

ordinating tags extracted from one or more folksonomies, with the aim of building

collective tag intelligence, which can then be exploited to improve the conven-

tional searching functionalities provided by tagging systems.

Chapter 5 provides an overview of the current landscape of computational models

of trust and reputation, and it presents an experimental study case in the domain of

social search, where it is shown how trust techniques can be applied to enhance

the quality of social search engine predictions.

Part II: Advanced Models and Practices

The part focuses on presenting theoretical models and state-of-the-art practices on

the area of collective and computational intelligence. These include but not limited

to the application of formal concept analysis; classifiers and expression trees;

swarm intelligence; channel prediction and message request; time costs and user

interfaces. In particular:

Chapter 6 presents the formal concept analysis; a proposed data technology that

complements collective intelligence such as that identified in the semantic web.

The work demonstrates the discovery of these novel semantics through open

source software development and visualizes data’s inherent semantics.

Chapter 7 focuses on constructing high quality classifiers through applying collec-

tive computational techniques to the field of machine learning. Experiment results

confirm gene expression programming and cellular evolutionary algorithms when

applied to the field of machine learning, can offer an advantage that can be attrib-

uted to their collaborative and synergetic features.

Chapter 8 deals with the load-balancing problem by using a self-organizing

approach. In this work, a generic architectural pattern has been presented, which

allows the exchanging of different algorithms through plugging. Although it pos-

sesses self-organizing properties by itself, a significant contribution to self-

organization is given by the application of swarm based algorithms, especially bee

algorithms that are modified, adapted and applied for the first time in solving the

load balancing problem.

Chapter 9 presents a new scheme for channel prediction in multicarrier frequency

hopping spread spectrum system. The technique adaptively estimates the channel

conditions and eliminates the need for the system to transmit a request message

prior to transmit the packet data.

X Preface

Chapter 10 discusses a theory on process for decision making under time stress,

which is common among two or bilateral decision makers. The work also pro-

poses a formula on strategic points for minimizing the cost of time for a certain

process.

Chapter 11 presents a model for amplifying human intelligence, utilizing agents

technology for task-oriented contexts. It uses domain ontology and task scripts for

handling formal and semiformal knowledge bases, thereby helping to systemati-

cally explore the range of alternatives; interpret the problem and the context and

finally, maintain awareness of the problem.

Part III: Advanced Applications

The part focuses on presenting cutting-edge applications with a specific focus on

social networks; cloud computing; computer games and trust. In particular:

Chapter 12 investigates the use of a proposed architecture for continuous analytics

for massively multi-play online games, to support the analytics part of the relevant

social networks. The work presents the design and implementation of the plat-

form, with a focus on the cloud-related benefits and challenges.

Chapter 13 studies feature extraction and pattern classification methods in two

medical areas, Stabilometry and Electroencephalography. An adaptive fuzzy infer-

ence neural network has been applied by using a hybrid supervised/unsupervised

clustering scheme while its final fuzzy rule base is optimized through competitive

learning. The proposed system is based on a method for generating reference mod-

els from a set of time series.

Chapter 14 analyzes a service oriented architecture based next generation mobility

management model. In this work, a practical case, e.g., a “mobile messaging” ap-

plication showing how to apply the proposed approach is presented.

Chapter 15 creates a set of metrics for measuring entertainment in computer

games. Specifically, the work here uses evolutionary algorithm to generate new

and entertaining games using the proposed entertainment metrics as the fitness

function. A human user survey and experiment using the controller learning ability

is also included.

Chapter 16 investigates the problem of knowledge extraction from social media.

Specifically, the work here presents three methods that use Flickr data to extract

different types of knowledge namely, the community structure of tag-networks,

the emerging trends and events in users tag activity, and the associations between

image regions and tags in user tagged images.

Chapter 17 presents an anonymity model to protect privacy in large survey rating

data. Extensive experiments on two real-life data sets show that the proposed slic-

ing technique is fast and scalable with data size and much more efficient in terms

of execution time and space overhead than the heuristic pair-wise method.