Massimiliano Giacalone

Innovative Methods for the Development of a Notoriety System

The role of internet in our society is growing day by day and is becoming more and more the only way for getting information, exchange opinions and for improving our personal culture. So, an huge mole of data, in all fields, is today easily accessible and everybody can express and exchange ideas. This represents the greatness of the web. But at the same time, to this huge amount of data does not always correspond an appropriate quality of information that we are reading, and nowadays this represents the biggest weakness of the web. Aim this the work is to analyze the approach based on marked chain used by Pagliarani et al. as we explain in the introduction, showing the relation of this method with BigData analysis.

Big Data and forensics

Nowadays, it is easy to trace a large amount of information on the web, to access documents and produce a digital storage.The current work is submitted as an introduction to an innovative system for the investigation about notoriety of web data which is based on the evaluation of judicial sentences and it is implemented to reduce the duration of all processes.This research also aims to open some new conjoint debates about the study and application of statistical and computational methods to web data on new forensics topics: text mining techniques enable us to obtain information which may be helpful to establish a statistical index in order to describe the quality and the efficiency in terms of law. It is also possible to develop an intelligent system about facts and judgments.

The Sentiment of the Infosphere: A Sentiment Analysis Approach for the Big Conversation on the Net

In the Network Society the use of hashtags has become a daily routine for the participation on the Big Conversation Iorio and Ruoto (Nessun tempo, 2015). Designated by a ‘hash’ symbol (#), a hashtag is a keyword assigned to information that describes it and aides in searching. Hashtags are now central to organize information on Social Networks. Hashtags organize discussion around specific topics or events and they are becoming an integrated part of the Infosphere, the whole informational environment constituted by all informational entities. The sentiment analysis of Hashtags shared on the Big Conversation can return a possible snapshot about the sentiment shared by users. Scope of this work is to present an application of sentiment analysis on the Italian hashtags of mainly social networks as part of the ‘Infosphere’. This analysis returns a semantic sentiment report about the hashtags shared by the users of the social networks, that can produce a semantic sentiment trend about users. This approach could be applied to every language simply changing the sentiment thesaurus used.

A Generalized Error Distribution-Based Method for Conditional Value-at-Risk Evaluation

One of the most important issues in finance is to correctly measure the risk profile of a portfolio, which is fundamental to take optimal decisions on the capital allocation. In this paper, we deal with the evaluation of portfolio’s Conditional Value-at-Risk (CVaR) using a modified Gaussian Copula, where the correlation coefficient is replaced by a generalization of it, obtained as the correlation parameter of a bivariate Generalized Error Distribution (G.E.D.). We present an algorithm with the aim of verifying the performance of the G.E.D. method over the classical RiskMetrics one, resulting in higher performance of the G.E.D. method.

Big Data Compliance for Innovative Clinical Models

Abstract In the healthcare sector, information is the most important aspect, and the human body in particular is the major source of data production: as a result, the new challenge for world healthcare is to take advantage of these huge amounts of data de-structured among themselves. In order to benefit from this advantage, technology offers a solution called Big Data Analysis that allows the management of large amounts of data of a different nature and coming from different sources of a “computerized” healthcare, as there are considerable changes made by the input of digital technology in all major health areas. Clinical intelligence consists of all the analytical methods made possible through the use of computer tools, in all the processes and disciplines of extraction and transformation of crude clinical data into significant insights, new purposes and knowledge that provide greater clinical efficacy and best health pronouncements about past performance, current operations and future events. It can therefore be stated that clinical intelligence, through patient data analysis, will become a standard operating procedure that will address all aspects of care delivery. The purpose of this paper is to present clinical intelligence approaches through Data Mining and Process Mining, showing the differences between these two methodologies applied to perform “real process” extraction to be compared with the procedures in the corporate compliance template (the so called “Model 231”) by “conformance checking”.

Bonferroni-Holm and permutation tests to compare health data: methodological and applicative issues

BackgroundStatistical methodology is a powerful tool in the health research; however, there is wide accord that statistical methodologies are not usually used properly. In particular when multiple comparisons are needed, it is necessary to check the rate of false positive results and the potential inflation of type I errors. In this case, permutation testing methods are useful to check the simultaneous significance level and identify the most significant factors.MethodsIn this paper an application of permutation tests, in the medical context of Inflammatory Bowel Diseases, is performed. The main goal is to assess the existence of significant differences between Crohn’s Disease (CD) and Ulcerative Colitis (UC). The Sequentially Rejective Multiple Test (Bonferroni-Holm procedure) is used to find which of the partial tests are effectively significant and solve the problem of the multiplicity control.ResultsApplying Non-Parametric Combination (NPC) Test for partial and combined tests we conclude that Crohn’s Disease patients and Ulcerative Colitis patients differ between them for most examined variables. UC patients compared with the CD patients, have a higher diagnosis age, not show smoking status, proportion of patients treated with immunosuppressants or with biological drugs is lower than the CD patients, even if the duration of such therapies is longer. CD patients have a higher rate of re-hospitalization. Diabetes is more present in the sub-population of UC patients. Analyzing the Charlson score we can highlight that UC patients have a more severe clinical situation than CD patients. Finally, CD patients are more frequently subject to surgery compared to UC. Appling of the Bonferroni Holm procedure, which provided adjusted p-values, we note that only nine of the examined variables are statistically significant: Smoking habit, Immunosuppressive therapy, Surgery, Biological Drug, Diabetes, Adverse Events, Re-hospitalization, Gender and Duration of Immunosoppressive Therapy. Therefore, we can conclude that these are the specific variables that can discriminate effectively the Crohn’s Disease and Ulcerative Colitis groups.ConclusionsWe identified significant variables that discriminate the two groups, satisfying the multiplicity problem, in fact we can affirm that Smoking habit, Immunosuppressive therapy, Surgery, Biological Drug, Diabetes, Adverse Events, Hospitalization, Gender and Duration of Immunosoppressive Therapy are the effectively significant variables.