1,720,963 research outputs found
Data fusion with source authority and multiple truth
No full textThe abundance of data available on the Web makes more and more probable the case of finding that different sources contain (partially or completely) different values for the same item. Data Fusion is the relevant problem of discovering the true values of a data item when two entities representing it have been found and their values are different. Recent studies have shown that when, for finding the true value of an object, we rely only on majority voting, results may be wrong for up to 30% of the data items, since false values are spread very easily because data sources frequently copy from one another. Therefore, the problem must be solved by assessing the quality of the sources and giving more importance to the values coming from trusted sources. State-of-the-art Data Fusion systems define source trustworthiness on the basis of the accuracy of the provided values and on the dependence on other sources. In this paper we propose an improved algorithm for Data Fusion, that extends existing methods based on accuracy and correlation between sources by taking into account also source authority, defined on the basis of the knowledge of which sources copy from which ones. Our method has been designed to work well also in the multi-truth case, that is, when a data item can also have multiple true values. Preliminary experimental results on a multi-truth real-world dataset show that our algorithm outperforms previous state-of-the-art approaches
A Minimum Metadataset for Data Lakes Supporting Healthcare Research
No full textWhile data lakes have emerged as a solution for storing vast amounts of heterogeneous and often unstructured data, responding to the growing need for flexible data storage, integration, and analytics in different domains, the digital transformation of healthcare processes has led to an exponential increase in various types of health records, necessitating efficient data management solutions and making this domain an ideal arena for experimenting data lake efficacy. In data lakes, effective metadata extraction and management are crucial for describing raw data, establishing connections, and ensuring interoperability among datasets ingested into the lake. To address this, we propose a minimum set of metadata tailored for clinical research, which includes relevant information common to significant branches of healthcare. Our metadataset not only streamlines data ingestion processes but also enhances the accessibility and usability of healthcare datasets for research purposes. By standardizing the collected metadata within the clinical research domain, we also facilitate data integration, analysis, and exploration, facilitating comprehensive data description and management within the data lake environment
Enhancing domain-aware multi-truth data fusion using copy-based source authority and value similarity
No full textData fusion, within the data integration pipeline, addresses the problem of discovering the true values of a data item when multiple sources provide different values for it. An important contribution to the solution of the problem can be given by assessing the quality of the involved sources and relying more on the values coming from trusted sources. State-of-the-art data fusion systems define source trustworthiness on the basis of the accuracy of the provided values and on the dependence on other sources, and recently it has been also recognized that the trustworthiness of the same source may vary with the domain of interest. In this paper we propose STORM, a novel domain-aware algorithm for data fusion designed for the multi-truth case, that is, when a data item can also have multiple true values. Like many other data-fusion techniques, STORM relies on Bayesian inference. However, differently from the other Bayesian approaches to the problem, it determines the trustworthiness of sources by taking into account their authority: Here, we define authoritative sources as those that have been copied by many other ones, assuming that, when source administrators decide to copy data from other sources, they choose the ones they perceive as the most reliable. To group together the values that have been recognized as variants representing the same real-world entity, STORM provides also a value-reconciliation step, thus reducing the possibility of making mistakes in the remaining part of the algorithm. The experimental results on multi-truth synthetic and real-world datasets show that STORM represents a solid step forward in data-fusion research
Development of Data Ingestion Pipelines for the Federated Use of Biomedical Data in Research: The Health Big Data Project
No full textThe secondary use of health data represents a great opportunity to advance pathophysiological knowledge and improve patients' care. However, the absence of standard data formats and information structuring schemas severely hinders this potential, preventing the efficient sharing of data collected in different hospitals and affecting the quality of multicentric studies. The 10-year Health Big Data (HBD) project aims to address these issues to foster the collaboration of 51 Italian research hospitals (IRCCSs). To address the seven main challenges identified for health data sharing, seven Working Groups (WGs) were created, with the WG2 being responsible for the definition of standardization and harmonization pipelines for signals, bioimages, and omics data. The present paper focuses on two ongoing works of the WG2, namely the implementation of a pipeline to extract and map information from electrocardiographic (ECG) signals into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and the development of a harmonization pipeline to reduce the center effect in multicentric Magnetic Resonance Imaging (MRI) studies. We show interesting results and insights concerning the implementation of both pipelines. Besides, we highlight the main difficulties we encountered on our path toward health data sharing and suggest possible solutions
Extraction of medical concepts from Italian natural language descriptions
No full textIn this paper we present a Natural Language Processing (NLP) pipeline to automatically extract medical concepts from a free text written in a language other than English. To do so, we use common NLP techniques and the metathesaurus of Unified Medical Language System (UMLS). Specifically, our goal is to automatically extract ontological concepts representing which part of the human body is injured and what is the nature of the injury, given an Italian textual description of a work accident. We start by partitioning the text into tokens and assigning to each token its part-of-speech, and then use an appropriate tool to extract relevant concepts to be searched within UMLS. We tested our system on a public large repository containing textual descriptions of work accidents produced by INAIL. Experimental results confirm that our system is able to correctly extract relevant medical concepts from texts written in Italian
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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