Pernici Mobile Information Systems

Infrastructure and Design for Adaptivity and Flexibility
1. Auflage 2006
ISBN: 978-3-540-31008-2
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 357 Seiten

This book presents a framework for mobile information systems, focusing on quality of service and adaptability at all architectural levels. These levels range from adaptive applications to e-services, middleware, and infrastructural elements, as developed in the 'Multichannel Adaptive Information Systems' (MAIS) project. The design models, methods, and tools developed in the project allow the realization of adaptive mobile information systems in a variety of different architectures.

Barbara Pernici is full professor of Computer Engineering at Politecnico di Milano. Her research interests include cooperative information systems, workflow management systems, information systems modeling and design, mobile information systems, temporal databases, applications of database technology. She holds a Dr. Eng. Degree from Politecnico di Milano and a Master of Science in Computer Science from Stanford University. She has published around 35 papers in international journals, including IEEE and ACM Transactions, co-edited 10 books, and published about 120 papers at international level. She is an editor of the Requirements Engineering Journal. She is chief scientist of the Italian FIRB MAIS (Multichannel Adaptive Information Systems) Project, 2002-2005. She is chair of Working Group 8.1 on Design and Evaluation of Information Systems of IFIP (International Federation for Information Processing).

Autoren/Hrsg.

Pernici, Barbara

Inhaltsverzeichnis

1;Preface;7
2;List of Contributors;11
3;Contents;15
4;Part I Core Technologies for Mobile Information Systems;17
4.1;1 Basic Concepts;19
4.1.1;1.1 Introduction;19
4.1.2;1.2 Novel Aspects of Mobile Information Systems;21
4.1.2.1;1.2.1 Multichannel Access and Mobile Devices;21
4.1.2.2;1.2.2 Context-Awareness in Mobile Information Systems;24
4.1.2.3;1.2.3 Wireless Infrastructures;27
4.1.2.4;1.2.4 Service-Oriented Systems and Cooperative Mobile Information Systems;28
4.1.3;1.3 Applications of Mobile Information Systems;32
4.1.3.1;1.3.1 Mobile and Multichannel Tourist Services;34
4.1.3.2;1.3.2 Mobile-Team Support;34
4.1.3.3;1.3.3 E-Learning;36
4.1.4;1.4 Introduction to the MAIS Framework;36
4.2;2 Reference Architecture and Framework;41
4.2.1;2.1 Introduction;41
4.2.2;2.2 The MAIS Architecture;41
4.2.2.1;2.2.1 MAIS Front-End Environment;42
4.2.2.2;2.2.2 MAIS Back-End Environment;44
4.2.3;2.3 The MAIS Framework;45
4.2.3.1;2.3.1 Functional Model: E-Service General Model;46
4.2.3.1.1;Service Provisioning Model;46
4.2.3.1.2;Service Request Model;49
4.2.3.2;2.3.2 Architectural Model;50
4.2.3.2.1;Base Reflective Layer;51
4.2.3.2.2;Quality-of-Service Representation;51
4.2.3.2.3;An Example: Computational Components;53
4.2.3.3;2.3.3 Context Model;53
4.2.3.3.1;Channel Model;55
4.2.3.3.2;Location and Time;58
4.2.3.3.3;User Profile;59
4.2.3.4;2.3.4 Quality of Service Dimensions;61
4.3;3 E-Services;63
4.3.1;3.1 MAIS Flexible E-Services;63
4.3.2;3.2 The MAIS Service Model;65
4.3.2.1;MAIS Service Description Language (MAIS-SDL);65
4.3.2.2;MAIS Process Language (MAIS-PL);67
4.3.3;3.3 Web Service Publication and Selection;69
4.3.4;3.4 MAIS-P;73
4.3.4.1;3.4.1 Invocation Mechanisms;73
4.3.4.2;3.4.2 Concretization and Optimization;75
4.3.4.3;3.4.3 Behavioral Compatibility of Services and Building New Services;78
4.3.4.4;3.4.4 Orchestrator;81
4.3.5;3.5 Service Cost and Price Models;86
4.3.5.1;A Model of Infrastructural Costs;86
4.3.5.2;Pricing Models;88
4.3.6;3.6 Micro-MAIS: Flexible Services for Ad Hoc Networks;91
4.3.6.1;3.6.1 Partitioning Process;92
4.3.6.2;3.6.2 Adaptive Work.ows;95
4.4;4 Middleware and Architectural Reflection;97
4.4.1;4.1 Introduction;97
4.4.2;4.2 Base Re.ective Layer;100
4.4.2.1;4.2.1 Basic Concepts;100
4.4.2.2;4.2.2 Observation and Control;102
4.4.2.3;4.2.3 Deployment;106
4.4.2.4;4.2.4 Network Base Layer;108
4.4.2.4.1;Network Objects;108
4.4.2.4.2;Network QoS Implementation;109
4.4.3;4.3 Extended Re.ective Layer;111
4.4.3.1;4.3.1 QoS Extension Pattern;111
4.4.3.2;4.3.2 Abstracting the QoS;112
4.4.4;4.4 A Middleware Architecture for Software Replication;114
4.4.4.1;4.4.1 Fault Tolerance Versus High Availability;117
4.4.4.2;Two-Tier Architectures for Software Replication;117
4.4.4.3;A Three-Tier Architecture for Active Software Replication;118
4.4.4.4;Software Replication in MAIS;120
4.4.4.5;4.4.2 Interoperable Replication Logic (IRL);121
4.4.4.6;Architectural Overview of IRL;121
4.4.5;4.5 Examples;125
4.4.5.1;4.5.1 University Education Management;125
4.4.5.2;Overview;125
4.4.5.3;Architecture of Adaptive PDBudget;125
4.4.5.4;Adaptive Functionalities: Examples;126
4.4.5.5;4.5.2 Supporting Nurses in Caregiving Process;127
4.4.5.6;Overview;127
4.4.5.7;MAIS Point of View;127
4.4.5.8;Rei.cation of Requests from the Point of View of the Nurse;129
4.4.5.9;Rei.cation of User Information;129
4.4.5.10;Rei.cation of Request from the Point of View of the Patient;129
4.4.5.11;Rei.cation of Location;130
5;Part II Enabling Technologies;131
5.1;5 Adaptive Networks;133
5.1.1;5.1 Introduction;133
5.1.2;5.2 Physical Layer;135
5.1.2.1;5.2.1 Flexibility, Adaptivity, and Recon.gurability in Modern Wireless Transceivers;135
5.1.2.2;5.2.2 An Innovative Approach to Flexible Modem Design: the Supervisor;136
5.1.2.3;5.2.3 Introduction of the Supervisor into the WiMAX Physical Layer;138
5.1.2.4;5.2.4 Performance Analysis and Results;139
5.1.3;5.3 MAC Layer;140
5.1.3.1;5.3.1 Layer-2 Adaptivity: Architectures and Requirements;141
5.1.3.2;5.3.2 Adaptive MAC within the WiMAX MAC Layer;143
5.1.3.3;5.3.3 Demonstration of Mobile Ad Hoc Network (MANET);146
5.1.4;5.4 On the quality-of-service of IP Telephony over Vehicular Ad Hoc Networks;149
5.1.4.1;5.4.1 Related Work;151
5.1.4.1.1;MAC Protocols for Ad Hoc Networks;151
5.1.4.1.2;Routing Protocols for Mobile Ad Hoc Networks;152
5.1.4.2;5.4.2 System Architecture;153
5.1.4.2.1;The VMAC Protocol;153
5.1.4.2.2;Routing Protocol;154
5.1.4.2.3;Transport and Application Protocols;155
5.1.4.3;5.4.3 Application Scenario;156
5.1.4.3.1;Performance Parameters;156
5.1.4.4;5.4.4 Performance Results;157
5.1.5;5.5 Advanced Interdomain Routing Protocols;161
5.1.5.1;5.5.1 BGPlay: a Platform for Visualizing the Behavior of;163
5.1.5.2;Interdomain;163
5.1.5.3;5.5.2 Commercial Relationships Between Autonomous Systems;165
5.1.5.4;5.5.3 Active BGP Probing;166
5.2;6 Data Management;171
5.2.1;6.1 Architectural Features for Data Management;172
5.2.1.1;6.1.1 Memory Types;173
5.2.1.2;6.1.2 Endurance, Power Consumption and Performance;174
5.2.2;6.2 DBMSs for Small Devices;175
5.2.2.1;6.2.1 Commercial Tools;175
5.2.2.1.1;Oracle Database Lite 10g;175
5.2.2.1.2;IAnywhere UltraLite Database;175
5.2.2.1.3;IBM DB2 Everyplace;176
5.2.2.1.4;Microsoft SQL Server Mobile Edition;176
5.2.2.2;6.2.2 PoLiDBMS: System Features;176
5.2.3;6.3 Design of Very Small Databases for Mobility;181
5.2.3.1;6.3.1 VSDB Design Methodology;182
5.2.3.2;6.3.2 Data Synchronization and Transactions;186
5.2.3.2.1;Distributed Commit Protocols;186
5.2.3.2.2;Semantic Synchronization;189
5.3;7 Low-Power Architectures for Mobile Systems;193
5.3.1;7.1 Exploiting Multiple Levels of Parallelism;196
5.3.1.1;7.1.1 Multithreaded Extension of Multicluster VLIW Processors for Embedded Applications;196
5.3.1.2;7.1.2 Multilevel Computing Architecture: Microarchitecture Design Issues;202
5.3.2;7.2 Power Modeling of Wireless Protocols;208
5.3.2.1;7.2.1 State of the Art;210
5.3.2.2;7.2.2 Methodology;211
5.3.3;7.3 Algorithms and Architectures for Information Security;216
5.3.4;7.4 Concluding remarks;222
6;Part III Design of Mobile Information Systems;223
6.1;8 Methods and Tools for the Development of Adaptive Applications;225
6.1.1;8.1 Introduction;225
6.1.2;8.2 Design Methodologies for Multichannel Adaptive;226
6.1.3;8.2 Design Methodologies for Multichannel Adaptive Information Systems;226
6.1.3.1;8.2.1 Modeling the Requirements of Multichannel Applications Information Systems;226
6.1.3.2;8.2.2 Design of Multichannel Adaptive Web-Based Applications;230
6.1.3.3;8.2.3 Multimodal Deployment of Adaptive Applications;236
6.1.3.3.1;Context-Awareness;236
6.1.3.3.2;The M3L Multimodal Framework;238
6.1.3.3.3;An Example of Adaptive Presentation;241
6.1.4;8.3 Adaptive Interaction in Web Information Systems;242
6.1.4.1;8.3.1 Modeling User Interaction;242
6.1.4.1.1;Formalizing the Interaction;242
6.1.4.1.2;The Set of AIUs;243
6.1.4.1.3;AIUs at Work;245
6.1.4.2;8.3.2 Context-Aware Adaptation Tools;246
6.1.4.3;8.3.3 Adaptive Presentation by Matching User Pro.les;249
6.1.4.3.1;Matchmaking Techniques in IM3;251
6.1.4.3.2;Architecture of the System;251
6.1.5;8.4 Usability and Accessibility of Adaptive Applications;252
6.1.5.1;8.4.1 Guidelines and Principles for Accessibility and Usability;252
6.1.5.1.1;Limitations of the Mobile Context;252
6.1.5.1.2;Principles and Guidelines for Accessibility;253
6.1.5.1.3;Usability Principles for Mobile Computing;254
6.1.5.2;8.4.2 Heuristic Evaluation of Usability and Accessibility in Mobile;256
6.1.5.2.1;Computing;256
6.1.5.2.2;Background;257
6.1.5.2.3;Expert-Based Evaluation in Mobile Computing;257
6.1.5.3;8.4.3 User Studies on Mobile Computing;258
6.1.5.3.1;User-Based Methods in Mobile Computing;259
6.1.5.3.2;Some Parameters of User Studies in Mobile Computing;260
6.1.5.3.3;Usability Evaluation: Summary;263
6.2;9 Development of Services for Mobile Information Systems;265
6.2.1;9.1 The MAIS Methodological Framework for Service Design;265
6.2.2;9.2 Guidelines for Analyzing and Designing Web Services;267
6.2.3;9.3 Service Speci.cation and Compatibility Analysis;268
6.2.3.1;9.3.1 Service Analysis and Design;269
6.2.3.2;9.3.2 High-Level Redesign;272
6.2.3.3;9.3.3 Customization;272
6.2.4;9.4 Broker–Provider Negotiation and Dynamic Evaluation of Management Costs;274
6.2.5;9.5 Process Partitioning;278
6.2.5.1;9.5.1 Work.ow Controllers in Distributed Environments;278
6.2.5.2;9.5.2 The Partitioning Tool;279
6.3;10 Knowledge-Based Tools for E-Service Profiling and Mining;281
6.3.1;10.1 Introduction;281
6.3.1.1;10.1.1 Recommender Systems: a Literature Review;282
6.3.1.2;10.1.2 Main Technological Characteristics of Recommender Systems;283
6.3.2;10.2 Applying Knowledge about Users to the Selection of E-Services;286
6.3.2.1;10.2.1 User Knowledge Supporting Service Personalization;286
6.3.2.2;10.2.2 Recommendation Environment Architecture;289
6.3.3;10.3 Algorithms Used for Evaluating Similarities Between Users and the E-Services;291
6.3.3.1;10.3.1 Description and Semantic Annotation of Services;292
6.3.3.2;10.3.2 User Profile Representation;294
6.3.3.3;10.3.3 Semantic Matching;295
6.3.4;10.4 The Data-Mining Profile Module as a User Knowledge Manager;299
6.3.4.1;10.4.1 Behavioral Pro.ling;299
6.3.4.2;10.4.2 Profile Construction Process;302
6.3.4.3;10.4.3 The Proposed Tool: the Data-Mining Profiling Module;305
6.3.4.4;10.4.4 Architecture;306
6.3.4.5;10.4.5 Implementation using Weka;307
6.3.4.6;10.4.6 Data Mining Profiling Module GUI;307
6.4;11 Applications. MultiLezi: Implementation of an Adaptive Multichannel Learning Environment;309
6.4.1;11.1 Introduction;309
6.4.2;11.2 A Conceptual Model of the Environment and of the User Profile;310
6.4.2.1;11.2.1 PocketLezi;313
6.4.2.2;11.2.2 MultimodalLezi;313
6.4.2.3;11.2.3 VocalLezi;315
6.4.2.4;11.2.4 AacLezi;317
6.4.2.5;11.2.5 MultiACC: a Pro.le-Based Channel Selector;318
6.4.3;11.3 Conclusions;320
7;Part IV Appendices;321
7.1;A The Quality Registry;323
7.1.1;A.1 Architectural Model;323
7.1.2;A.2 Channel Model;324
7.1.3;A.3 Functional Model;330
7.1.4;A.4 Context Model;331
7.2;B Functional Interaction Abilities: Conceptual Model of the User Context;335
7.2.1;B.1 Attributes of ICF-Related Classes: Individuation of the Minimal Set;335
8;References;343
9;Index;367

Leseproben

10 Knowledge-Based Tools for E-Service Profiling and Mining (p.265-266)

A. Corallo, G. Lorenzo, G. Solazzo, and D. Arnone


10.1 Introduction

It has emerged from the previous chapters that MAIS was conceived to fulfil user needs through adaptation to the context and personalization. Services that adapt and permit personalization can be conceived such that they take into account different levels of user needs and preferences, such as those relating to devices, quality of service, and visualization. Services can also take into account the context of use and the specific properties of business, in order to enable adoption in service-oriented business environments. Service-oriented architectures are, at the moment, the most promising paradigm for business middleware. This trend is confirmed by the interest of many organizations and standardization bodies involved in the promotion of diffusion such as OASIS, UN/CEFACT and the Value Chain Group. In order to create seamless and fluid business environments, in which people can conduct business as they normally do in the business context, it is necessary to develop systems that enable strong business personalization, capable of delivering business services to users in accordance with their user profile.

In this chapter, we shall define a software component, called the recommendation environment, that extends service personalization by enabling a matchmaking process on nonfunctional, semantically rich user and service descriptions, and describe the modeling and design of it. The recommendation environment adds value to the MAIS platform, adding a business dimension with which it is possible to describe e-services, and that at the same time enables reasoning, knowledge extraction, and management. The recommendation environment is placed in the back-end architecture of the MAIS platform (see Fig. 2.3); its role is to recommend, once the functional selection has been carried out, the most suitable concrete e-service with respect to a user profile.

Starting from a set of functionally equivalent concrete e-services, the recommendation environment will state which is the service closest to the behavioral description of a user profile. The user profile contains properties that extend the technological description of an e-service by defining, for example, the characteristics of the real world products and services delivered by the company that manages this specific e-service. In order for the recommendation environment to perform its tasks, it is necessary to provide the environment with a back-ofice tool for data mining capable of analyzing and extracting knowledge from business events generated by the MAIS platform.

Starting from basic definitions, in the rest of this section we introduce the concept of a recommender system and describe how recommender systems can support service-oriented architectures in emerging e-business models. In Section 10.2, we describe the architecture of the recommendation environment, specifying its role in the MAIS architecture and its interaction with other components. In Section 10.3, we show how a recommendation is performed, and describe the approach followed for description of users and services, and the algorithm used to evaluate the degree of afinity between a user profile and a concrete e-service. Finally, in Section 10.4, we provide some details about data mining and behavioral profiling, which supports the creation and management of profiles.