Mobile Information Systems
Description
Mobile devices allow users to access information resources and services over many different distribution channels - anywhere, anytime, anyhow. Technical and usage characteristics of mobile systems are highly variable with respect to user capabilities and context characteristics, therefore an immense level of flexibility is required.
Barbara Pernici - with contributions by the research groups involved in the project - presents here a framework for mobile information systems, focussing on quality of service and adaptability at all architectural levels, ranging from adaptive applications to e-services, middleware, and infrastructural elements, as it was 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.
The book is divided into three parts: core technologies for mobile information systems (e.g., adaptive middleware and flexible e-services), enabling technologies (like data management on small devices or adaptive low-power hardware architectures or wireless networks), and methodological aspects of mobile information systems design (such as service profiling or user interface and e-service design for context-aware applications). It provides researchers in academia and industry with a comprehensive vision on innovative aspects which can be used as a basis for the development of new frameworks and applications.
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Person
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).
Content
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.
