Web Service Discovery Based on Past User Experience

Web service technology provides a way for simplifying interoperability among different organizations. A piece of functionality available as a web service can be involved in a new business process. Given the steadily growing number of available web services, it is hard for developers to find services appropriate for their needs. The main research efforts in this area are oriented on developing a mechanism for semantic web service description and matching. In this paper, we present an alternative approach for supporting users in web service discovery. Our system implements the implicit culture approach for recommending web services to developers based on the history of decisions made by other developers with similar needs. We explain the main ideas underlying our approach and report on experimental results

Forum Session at the First International Conference on Service Oriented Computing (ICSOC03)

The First International Conference on Service Oriented Computing (ICSOC) was held in Trento, December 15-18, 2003. The focus of the conference ---Service Oriented Computing (SOC)--- is the new emerging paradigm for distributed computing and e-business processing that has evolved from object-oriented and component computing to enable building agile networks of collaborating business applications distributed within and across organizational boundaries. Of the 181 papers submitted to the ICSOC conference, 10 were selected for the forum session which took place on December the 16th, 2003. The papers were chosen based on their technical quality, originality, relevance to SOC and for their nature of being best suited for a poster presentation or a demonstration. This technical report contains the 10 papers presented during the forum session at the ICSOC conference. In particular, the last two papers in the report ere submitted as industrial papers

IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems

Recommendation systems have proven to be useful in various application domains. However, current solutions are usually ad-hoc systems which are tightly-coupled with the application domain. We present the IC-Service, a recommendation service that can be included in any system in a loosely coupled way. The implementation follows the principles of service oriented computing and provides a solution to various problems arising in recommendation systems, e.g. to the problem of meta-recommendation systems development. Moreover, when properly configured, the IC-Service can be used by different applications (clients), and several independent instances of the IC-Service can collaborate to produce better recommendations. Service architecture and communication protocols are presented. The paper describes also ongoing work and applications based on the IC-Service

Can you see the commons? The Sense Weaver: visually-driven sense-making

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An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a 'black box' system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples

A hybrid calibration-free/artificial neural networks approach to the quantitative analysis of LIBS spectra

A 'hybrid' method is proposed for the quantitative analysis of materials by LIBS, combining the precision of the calibration-free LIBS (CF-LIBS) algorithm with the quickness of artificial neural networks. The method allows the precise determination of the samples' composition even in the presence of relatively large laser fluctuations and matrix effects. To show the strength and robustness of this approach, a number of synthetic LIBS spectra of Cu-Ni binary alloys with different composition were computer-simulated, in correspondence of different plasma temperatures, electron number densities and ablated mass. The CFLIBS/ANN approach here proposed demonstrated to be capable, after appropriate training, of 'learning' the basic physical relations between the experimentally measured line intensities and the plasma parameters. Because of that the composition of the sample can be correctly determined, as in CF-LIBS measurements, but in a much shorter time

designing badges for a civic media platform reputation and named levels

Badges are gaining momentum on the social web but known HCI design experiences are very few. In this paper we present a design experience related to badges and named levels for representing reputation within the context of a Civic Media Platform called timu. We describe our design methodology and the exercise we have devised for designing named levels together with users. We then describe our design vision which unites contextual details about timu together with the rhetoric of progression provided by the language proficiency system. In the conclusion we generalise our design results

Aiding compliance governance in service-based business processes

Assessing whether a company's business practices conform to laws and regulations and follow standards and SLAs, i.e., compliance management, is a complex and costly task. Few software tools aiding compliance management exist; yet, they typically do not address the needs of who is actually in charge of assessing and understanding compliance. We advocate the use of a compliance governance dashboard and suitable root cause analysis techniques that are specifically tailored to the needs of compliance experts and auditors. The design and implementation of these instruments are challenging for at least three reasons: (1) it is fundamental to identify the right level of abstraction for the information to be shown; (2) it is not trivial to visualize different analysis perspectives; and (3) it is difficult to manage and analyze the large amount of involved concepts, instruments, and data. This chapter shows how to address these issues, which concepts and models underlie the problem, and, eventually, how IT can effectively support compliance analysis in Service-Oriented Architectures (SOAs). © 2012, IGI Global