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Mobile Information Systems Infrastructure and Design for Adaptivity and Flexibility

:	Barbara Pernici
:	Barbara Pernici
:	Mobile Information Systems Infrastructure and Design for Adaptivity and Flexibility
:	Springer-Verlag
:	9783540310082
:	1
:	CHF 85.50
:

:	Datenkommunikation, Netzwerke
:	English

:	357
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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).

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.

	Preface	7
	List of Contributors	11
	Contents	15
	Part I Core Technologies for Mobile Information Systems	17
	1 Basic Concepts	19
	1.1 Introduction	19
	1.2 Novel Aspects of Mobile Information Systems	21
	1.2.1 Multichannel Access and Mobile Devices	21
	1.2.2 Context-Awareness in Mobile Information Systems	24
	1.2.3 Wireless Infrastructures	27
	1.2.4 Service-Oriented Systems and Cooperative Mobile Information Systems	28
	1.3 Applications of Mobile Information Systems	32
	1.3.1 Mobile and Multichannel Tourist Services	34
	1.3.2 Mobile-Team Support	34
	1.3.3 E-Learning	36
	1.4 Introduction to the MAIS Framework	36
	2 Reference Architecture and Framework	41
	2.1 Introduction	41
	2.2 The MAIS Architecture	41
	2.2.1 MAIS Front-End Environment	42
	2.2.2 MAIS Back-End Environment	44
	2.3 The MAIS Framework	45
	2.3.1 Functional Model: E-Service General Model	46
	Service Provisioning Model	46
	Service Request Model	49
	2.3.2 Architectural Model	50
	Base Reflective Layer	51
	Quality-of-Service Representation	51
	An Example: Computational Components	53
	2.3.3 Context Model	53
	Channel Model	55
	Location and Time	58
	User Profile	59
	2.3.4 Quality of Service Dimensions	61
	3 E-Services	63
	3.1 MAIS Flexible E-Services	63
	3.2 The MAIS Service Model	65
	MAIS Service Description Language (MAIS-SDL)	65
	MAIS Process Language (MAIS-PL)	67
	3.3 Web Service Publication and Selection	69
	3.4 MAIS-P	73
	3.4.1 Invocation Mechanisms	73
	3.4.2 Concretization and Optimization	75
	3.4.3 Behavioral Compatibility of Services and Building New Services	78
	3.4.4 Orchestrator	81
	3.5 Service Cost and Price Models	86
	A Model of Infrastructural Costs	86
	Pricing Models	88
	3.6 Micro-MAIS: Flexible Services for Ad Hoc Networks	91
	3.6.1 Partitioning Process	92
	3.6.2 Adaptive Work.ows	95
	4 Middleware and Architectural Reflection	97
	4.1 Introduction	97
	4.2 Base Re.ective Layer	100
	4.2.1 Basic Concepts	100<