1,721,116 research outputs found
Reasoning under Uncertainty in On-Line Auditing.
No full textWe propose a Bayesian approach to reasoning under uncertainty in on-line auditing of Statistical Databases. A Bayesian network addresses disclosures based on probabilistic inferences that can be drawn from released data. In particular, we deal with on-line max and min auditing. Moreover, we show how our model is able to deal with the implicit delivery of information that derives from denying the answer to a query and to manage user prior-knowledge
Towards reengineering in reuse reengineering processes
No full textReuse of existing software has been regarded in recent years as a feasible solution to software quality and productivity improvement problems. Various reference paradigms for setting up a reuse reengineering process have been proposed. With reference to the RE/sup 2/ (Reverse Engineering and Reuse Reengineering) paradigm, this paper addresses the problems of the election phase. In particular, by describing an approach to the reuse reengineering of COBOL programs, it tackles the transformation of a set of candidate components into a set of actually reusable modules. This involves the identification of a module template that allows the COBOL code components to be easily reused, and the definition of reverse engineering and reengineering techniques to package the components into the template
A Bayesian Approach for On-Line Sum/Count/Max/Min Auditing on Boolean Data
No full textWe consider the problem of auditing databases that support statistical sum/count/max/min queries to protect the privacy of sensitive information. We study the case in which the domain of the sensitive information is the boolean set. Principles and techniques developed for
the privacy of statistical databases in the case of continuous attributes do not always apply here. We provide a probabilistic framework for the on-line auditing and we show that sum/count/min/max queries can be audited by means of a Bayesian network
An empirical comparison of methods to support QoS-aware service selection
No full textRun-time binding is an important and useful feature of Service Oriented Architectures (SOA), which aims at selecting, among functionally equivalent services, the ones that optimize some QoS objective of the overall application. To this aim, it is particularly relevant to forecast the QoS a service will likely exhibit in future invocations.
This paper presents an empirical study aimed at comparing different approaches for QoS forecasting, namely the use of average and current values, linear models, and models based on time series. The study is performed on QoS data obtained by monitoring the execution of 10 real services for 4 months.
Results show that, overall, the use of time series forecasting has the best compromise in ensuring a good prediction error, being sensible to outliers, and being able to predict likely violations of QoS constraints
Reasoning under Uncertainty and Multi-CriteriaDecision Making in Data Privacy
No full textBy means of an integration of decision theory and probabilistic models, we explore and develop methods for improving data privacy. Our work encompasses disclosure control tools in statistical databases and privacy requirements prioritization.
Several fields in the social sciences, economics and engineering will benet from the advances in this research area: e-voting, e-government, e-commerce,
e-banking, e-health, cloud computing and risk management are a few examples of applications for the fundings of this research
Acknowledgements
No full textI’d like to thank prof. Aniello Cimitle and Gerardo Canfora for their helpful leading along the three years of my study, explaining me how to study, how to realize research, how to evaluate my work. I hope they give me the opportunity to follow their steps forever. I’d like to thank my father and my mother for staying at my side along all the way and giving me the suggestions and the strength to reach my final goal; they teach me to reject the fear to fall down, but to have ever the will to stand up and go on. I’d like to thank also Gioia for her understanding, patience and help during the harder moments of weakness. Without her smile nothing could be done. I’d like to thank also Gloria for being every time I needed to discuss with her. I’d like to thank prof. Mario Piattini, Felix Garcia, Marcela Genero for their fruitful collaboration. I appreciate them both on the human and on the professional side. The deep relationship linking me to them is based on pure esteem and respect. I’d like to thank prof. Giuseppe di Lucca and Emilio Bellini for their collaboration in the research
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