Enabling Cross Constraint Satisfaction in RDF-Based Heterogeneous Database Integration

Abstract The problem of database integration has been widely tackled through different approaches. While data transformation based systems, such as Data Warehouses, reached the acceptation of the industry during the 80's, in the last decade query translation based approaches have gained popularity given their adequacy to dynamic domains

Decentralized Control and Adaptation in Distributed Applications via Web and Semantic Web Technologies

The presented work provides an approach and an implementation for enabling decentralized control in distributed applications composed of heterogeneous components by benefiting from the interoperability provided by the Web stack and relying on semantic technologies for enabling data integration. In particular, the concept of Smart Components enables adaptability at runtime through an adaptation layer and is complemented by a reference architecture as well as a prototypical implementation

Metrics Thermostat

The explosion of information and information technology has led many firms to evolve a dispersed product development process with people and organizations spread throughout the world. To coordinate such dispersed processes managers attempt to establish a culture that implicitly rewards product development teams based on their ability to perform against a set of strategic metrics such as customer satisfaction, time to market, defect reduction, or platform reuse. Many papers have focused on selecting the right metrics and establishing the culture. In this paper we focus on a practical method to fine-tune a firm's relative emphasis on the metrics that they have chosen. In particular, we seek to advise a firm whether to increase or decrease their emphasis on each metric such that the change in emphasis improves profits. Using a thermostat analogy we apply an adaptive control feedback mechanism in which we estimate the incremental improvements in priorities that will increase profits. Iterations of adaptive control seek to maximize profits even if the environment is changing. We demonstrate the metric thermostat’s use in an application to a firm with over $20 billion in revenue. In developing the metric thermostat we recognize that there are hundreds of detailed actions, such as the use of the house of quality and the use of robust design, among which the product development team must choose. We also recognize that they will act in their own best interests to choose the actions that maximize their own implicit rewards as determined by the metrics. Management need not observe or dictate these detailed actions, but rather control the process by establishing the culture that sets the implicit weights on the metrics. The thermostat works by changing those implicit weights. We define the problem, introduce the adaptive control mechanism, modify “agency” theory to deal with incremental changes about an operating point, and derive methods that are practical and robust in light of the data that firms have available. Our methods include statistical estimation and internal surveys. The mathematics identify the critical few parameters that need be determined and highlight how to estimate them. Both the measures and the estimation are illustrated in our initial application to a large officeequipment firm. The metrics thermostat suggests that this firm has about the right emphasis on timeto- market, but has overshot on platform reuse and has lost its focus on customer satisfaction. We describe how the firm reacted to the recommendations and changed its organization. We describe additional ongoing applications with the US Air Force, the US Navy, and a major automobile and truck manufacturer.This research was funded by the Center for Innovation in Product Development (CIPD) and the International Center for Research on the Management of Technology (ICRMOT), M.I.T

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks