Search

This paper describes the work done in the project MoseForAgrofood (Ms4A) whose goal is to take advantage of mobile technologies to develop augmented Web services for the agrofood domain. The use of personal devices, such as smart phones and tablets, brings in specific issues such as the need to consider quality of protection, quality of services, quality of data at any stage of the service life cycle. In this paper we propose a conceptual model that aims to support the service development and provisioning addressing the above issues by managing suitable non-functional properties.

The paper discusses two concepts that have been associated with various approaches to data and information, namely capacity and value, focusing on data base architectures, and on two types of technologies diffusely used in integration projects, namely data integration, in the area of Enterprise Information Integration, and publish & subscribe, in the area of Enterprise Application Integration. Furthermore, the paper proposes and discusses a unifying model for information capacity and value, that considers also quality constraints and run time costs of the data base architecture.

The eighth international workshop on Quality in Database was held in Singapore, on September 13th, 2010 and co-located with the 36th Conference on Very Large DataBase (VLDB). The main objective of the workshop was to address the challenge to detect data anomalies and assess, monitor, improve, and maintain the quality of information. The keynote speech, titled 'SOLOMON: Seeking the Truth Via Copying Detection' was delivered by Xin Luna Dong, AT&T. The author presented the SOLOMON tool that supports the discovery of copying relationships between structured data source to improve data integration features. The goal of 'Deriving Effectiveness Measures for Data Quality Rules' by Lei Jiang, Alex Borgida, Daniele Barone, John Mylopoulos is the evaluation of data quality rules. Peter Yeh, and Colin Puri show in 'Discovering Conditional Functional Dependencies to Detect Data Inconsistencies' an approach that exploits conditional functional dependencies for detecting inconsistencies in data and hence improves data quality.

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data qualityassessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of suchmethodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.

Measuring and improving data quality in an organisation or in a group of interacting organisations is a complex task. Several methodologies have been developed in the past, providing a basis for the definition of a data quality programme that guarantees high data quality levels. Since the main limitation of existing approaches is their specialisation on specific issues or contexts, this paper presents a Comprehensive Data Quality (CDQ) methodology. The main aim of the CDQ methodology is the integration and enhancement of the phases, techniques and tools proposed by previous approaches. In particular, the CDQ methodology is conceived to be at the same time complete, flexible and simple to apply. Completeness is achieved by considering an existing techniques and tools and integrating them in a framework that can work in any organisation. The methodology is flexible, since it supports the user in the selection of the most suitable techniques and tools within each phase and in any context. Finally, CDQ is simple, since it is organised in phases and each phase is characterised by a specific goal and a set of techniques to apply. The methodology is explained by means of a running example and significant cases of its application are reported

Measuring and improving data quality in an organization or in a group of interacting organizations is a complex task. Several methodologies have been developed in the past providing a basis for the definition of a complete data quality program applying assessment and improvement techniques in order to guarantee high data quality levels. Since the main limitation of existing approaches is their specialization on specific issues or contexts, this paper presents the Comprehensive Data Quality (CDQ) methodology that aims at integrating and enhancing the phases, techniques and tools proposed by previous approaches. CDQ methodology is conceived to be at the same time complete, flexible and simple to apply. Completeness is achieved by considering existing techniques and tools and integrating them in a framework that can work in both intra and inter organizational contexts, and can be applied to all types of data. The methodology is flexible since it supports the user in the selection of the most suitable techniques and tools within each phase and in any context. Finally, CDQ is simple since It is organized in phases and each phase is char-acterized by a specific goal and techniques to apply. The methodology is explained by means of a running example. © 2006 IEEE.

The goal of the MAIS system is to provide support for flexible and adaptive execution of applications in a distributed, multichannel, mobile information system. In such a system, a fundamental requirement is an ability to describe the continuously evolving execution environment and user characteristics. Service requests are therefore satisfied by considering both the request itself and its provisioning environment. The first part of this chapter presents the general architecture of the MAIS system. The MAIS architecture allows us to define a set of “pluggable” modules which can be composed to provide adaptivity at different levels in the MAIS system. The main architectural components are introduced in Sect. 2.2; more details of the components are provided in the rest of the book. The MAIS reference framework, illustrated in the second part of this chapter, provides the essential basis for all of the adaptive mechanisms that are illustrated in the book. The MAIS reference framework defines a common understanding of the elements of a mobile information system that are used to enable communication among the various modules of a MAIS system during information exchange and service provisioning. The reference framework is composed of a set of models: the functional model, the architectural model, and the context model. These are described in the second part of this chapter.

This paper presents a first attempt to realize a methodological framework supporting the most relevant phases of the design of a value-added service. A value-added service is defined as a functionality of an adaptive and multi-channel information system obtained by composing services offered by different providers. The framework has been developed as part of the MAIS project. The MAIS framework focuses on the following phases of service life cycle: requirements analysis, design, deployment, run time use and negotiation. In the first phase, the designer elicits, validates and negotiates service requirements according to social and business goals. The design phase is in charge of modelling services with an enhanced version of UML, augmented with new features developed within the MAIS project. The deployment phase considers the network infrastructure and, in particular, provides an approach to implement and coordinate the execution of services from different providers. In the run time use and negotiation phase, the MAIS methodology provides support to the optimal selection and quality renegotiation of services and to the dynamic evaluation of management costs. The paper describes the MAIS methodological tools available for different phases of service life cycle and discusses the main guidelines driving the implementation of a service management architecture, called reflective architecture, that complies with the MAIS methodological approach