1,721,043 research outputs found
Supporting the Design of Data Preparation Pipelines
No full textThe availability of a large amount of data facilitates spreading a data-driven culture in which data are used and analyzed to support decision-making. However, data-based decisions are effective only if the considered input data sources are not affected by poor quality and biases. For this reason, the data preparation phase is crucial for guaranteeing an appropriate output quality. There is a strong evidence in the literature that dealing with data preparation is not simple: it is the most resource consuming step in data analysis and most of the times it is performed using a trial and error approach. Considering this, we aim to support users in the design of data preparation pipelines by identifying the most suitable data transformation/cleaning operations to apply and the order in which they have to be executed. In order to achieve such a goal, using different datasets and ML algorithms, we conducted a series of experiments designed to assess the impact of different types of errors on the quality of the output. The idea is to develop a framework that provides users with guidelines that recommend to address the data quality issues with the highest negative impact first. A preliminary validation has confirmed that following the system suggestions yields better results
EUCIP Guida alla certificazione per professionisti IT
No full textIl libro è una guida per la certificazione euci
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
Service identification in inter-organizational process design
No full textService identification is one of the main phases in the design of a service-oriented application. The way in which services are identified may influence the effectiveness of the SOA architecture. More specifically, the granularity of the services is very important in reaching flexibility and reusing them. Such properties are crucial in inter-organizational interactions based on collaborative business processes. In fact, collaboration is facilitated by ensuring a homogeneous description of services at the right level of granularity. In this paper, we provide a detailed description of P2S (Process-to-Services), a computer aided methodology to enable the identification of services that compose a collaborative business process. The methodology is based on metrics defined to setup service granularity, cohesion, coupling and reuse. A prototype tool based on the methodology is also described with reference to a real case scenario
Representation and Certification of Data Quality on the Web
No full textThe large majority of users accesses Web pages without considering the quality of their
contents. In the Web environment, users are not provided tools that measure the quality and
reliability of information, while Web information is often non reliable, as it is gathered from
different sources that may not be integrated and consistent with each other. In distributed systems,
such as the Internet, databases belong to different domains, are built according to different
requirements and are updated at times and with procedures that depend on the specific context.
Consequently, when information is integrated, problems concerning data consistency, accuracy,
usability, and timeliness can arise. This paper addresses these issues and proposes a tool for the
evaluation of data quality on the Web and for the management of the related certification process.
For this purpose, the paper defines a data quality representation model and a language for the
description and implementation of quality metadata by comparing different standards from the
literature
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
Toward Quality-Aware Transaction Validation in Blockchain
No full textWith blockchain-oriented software engineering, the quality of input data is often overlooked. Controlling the transaction payloads in the transaction validation phase may prevent the poor-data-quality issues that can affect the output of even correct smart contracts
Data Quality and Data Ethics: Towards a Trade-off Evaluation
No full textIn the last decades, one of the main drivers for organizational success has been data-driven decision-making: strategic decisions are based on data analysis and interpretation. In this scenario, relying on dependable results becomes imperative. Therefore we must ensure that input data have good quality and the algorithms on which the analysis is based are fair: in general, Data Quality (DQ) and Data Ethics (DE) should be guaranteed. However, maximizing DQ and DE simultaneously is non-trivial, since DQ improvement techniques can negatively affect DE and vice versa. Discovering which relationships exist between DQ and DE and thoroughly analyzing it is therefore of paramount importance. The goal of this paper is to study whether, in a given context, there is a trade-off between DQ and DE: specifically, we consider the Completeness dimension of DQ, and the Fairness dimension of DE. The results of our experiments, based on two real-world well-known datasets, provided details about this trade-off and allowed us to draw some guidelines
- …
