Inference for big data assisted by small area methods: an application to OBEC (on-line based enterprise characteristics)

Nowadays, the availability of a huge amount of data produced by a wide range of new technologies, so-called big data, is increasing. However, data obtain- able from big data sources are often the result of a non-probability sampling process and adjusting for the selection bias is an important practical problem. In this paper, we propose a novel method of reducing the selection bias associated with the big data source in the context of Small Area Estimation (SAE). Our approach is based on data integration and the combination of a big data sample and a probability sam- ple. An application on OBEC (on-line based enterprise characteristics) combining Istat sampling survey and web scraping data has been proposed

Design and Implementation of a Peer-to-Peer Data Quality Broker

Abstract Data quality is becoming an increasingly important issue in environments characterized by extensive data replication. Among such environments, this paper focuses on Cooperative Information Systems (CISs), for which it is very important to declare and access quality of data. Indeed, a system in the CIS will not easily exchange data with another system without a knowledge on its quality, and cooperation becomes dicult without data exchanges. Also, when poor quality data are exchanged, there is a progressive deterioration of the quality of data stored in the whole CIS. In this paper, we describe the detailed design and implementation of a peer-to-peer service for exchanging and improving data quality in CISs. Such a service allows to access data and related quality distributed in the CIS and improves quality of data by comparing dierent copies of the same data. Some experiments on real data will show the eectiveness of the service and the performance behavior

The impact of Covid-19 healthcare emergency on the psychological well-being of health professionals: a review of literature

Introduction. The Coronavirus pandemic (Covid-19) was first identified in December 2019 in the city of Wuhan, China, and later caused a severe health crisis, causing massive disruptions to most healthcare systems worldwide. The Covid-19 health emergency has seen healthcare workers in the front line facing all the difficulties related to the care burden. One of the most significant and probably underinvestigated aspects is the psychological stress of the healthcare staff managing the emergency. The aim of the paper is to analyze the literature on the impact of the Covid-19 crisis on the psychological well-being of health professionals.Methodology. We conducted a systematic review of articles published on this topic during the months from January 2020 to December 2020, searching on Pub Med, Scopus and Web of Science databases.Results. Most of the issues can be summarized into five conceptual categories: Stress, Depression and Infection Anxiety, Anguish, Insomnia, Post Traumatic Stress Disorder, and Suicide. The literature identifies many factors contributing to the onset of anxiety, depression, and stress, like the fear of contracting the disease and transmitting it to family members and friends, stressful shifts, and little rest among several others. The literature highlights the needs for adequate measures, including proper psychological support.Conclusion. The conducted review suggests that the behaviours of healthcare professionals during the emergency phase of the Covid-19 pandemic show psychological disorders that can compromise mental health. Therefore, there is a call for those in chief like hospital managers and policymakers to take action, promoting measures like surveillance, monitoring, and psychological support among others, to increase the resilience of healthcare workers, limiting stress and anxiety and allowing them to keep their performance at work

The impact of Covid-19 healthcare emergency on the psychological well-being of health professionals: a review of literature

Introduction. The Coronavirus pandemic (Covid-19) was first identified in December 2019 in the city of Wuhan, China, and later caused a severe health crisis, causing massive disruptions to most healthcare systems worldwide. The Covid-19 health emergency has seen healthcare workers in the front line facing all the difficulties related to the care burden. One of the most significant and probably underinvestigated aspects is the psychological stress of the healthcare staff managing the emergency. The aim of the paper is to analyze the literature on the impact of the Covid-19 crisis on the psychological well-being of health professionals. Methodology. We conducted a systematic review of articles published on this topic during the months from January 2020 to December 2020, searching on Pub Med, Scopus and Web of Science databases. Results. Most of the issues can be summarized into five conceptual categories: Stress, Depression and Infection Anxiety, Anguish, Insomnia, Post Traumatic Stress Disorder, and Suicide. The literature identifies many factors contributing to the onset of anxiety, depression, and stress, like the fear of contracting the disease and transmitting it to family members and friends, stressful shifts, and little rest among several others. The literature highlights the needs for adequate measures, including proper psychological support. Conclusion. The conducted review suggests that the behaviours of healthcare professionals during the emergency phase of the Covid-19 pandemic show psychological disorders that can compromise mental health. Therefore, there is a call for those in chief like hospital managers and policymakers to take action, promoting measures like surveillance, monitoring, and psychological support among others, to increase the resilience of healthcare workers, limiting stress and anxiety and allowing them to keep their performance at work

From Data Quality to Big Data Quality

This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review

Viewpoints on emergent semantics

Authors include:Philippe Cudr´e-Mauroux, and Karl Aberer (editors), Alia I. Abdelmoty, Tiziana Catarci, Ernesto Damiani, Arantxa Illaramendi, Robert Meersman, Erich J. Neuhold, Christine Parent, Kai-Uwe Sattler, Monica Scannapieco, Stefano Spaccapietra, Peter Spyns, and Guy De Tr´eWe introduce a novel view on how to deal with the problems of semantic interoperability in distributed systems. This view is based on the concept of emergent semantics, which sees both the representation of semantics and the discovery of the proper interpretation of symbols as the result of a self-organizing process performed by distributed agents exchanging symbols and having utilities dependent on the proper interpretation of the symbols. This is a complex systems perspective on the problem of dealing with semantics. We highlight some of the distinctive features of our vision and point out preliminary examples of its applicatio

Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized before being released, it is still possible for an adversary to reconstruct the original data using additional information thus resulting in privacy violations. To date, however, a systematic approach to quantify such risks is not available. In this paper we develop a framework, based on statistical decision theory, that assesses the relationship between the disclosed data and the resulting privacy risk. We model the problem of deciding which data to disclose, in terms of deciding which disclosure rule to apply to a database. We assess the privacy risk by taking into account both the entity identification and the sensitivity of the disclosed information. Furthermore, we prove that, under some conditions, the estimated privacy risk is an upper bound on the true privacy risk. Finally, we relate our framework with the k-anonymity disclosure method. The proposed framework makes the assumptions behind k-anonymity explicit, quantifies them, and extends them in several natural directions