1,720,957 research outputs found
Measuring Approximate Functional Dependencies: A Comparative Study
No full textApproximate functional dependencies (AFDs) are functional dependencies (FDs) that “almost” hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is unclear which measure is preferable when one needs to discover FDs in real-world data, i.e., data that only approximately satisfies the FD. In response, this paper formally and qualitatively compares AFD measures. We obtain a formal comparison through a novel presentation of measures in terms of Shannon and logical entropy. Qualitatively, we perform a sensitivity analysis w.r.t. structural properties of input relations and quantitatively study the effectiveness of AFD measures for ranking AFDs on real world data. Based on this analysis, we give clear recommendations for the AFD measures to use in practice.We thank Dan Suciu for helpful discussions. S. Vansummeren was supported by the Bijzonder Onderzoeksfonds (BOF) of Hasselt University under
Grant No. BOF20ZAP02. This research received funding from the Flemish
Government under the “Onderzoeksprogramma Artificiele Intelligentie (AI) ¨
Vlaanderen” programme. This work was supported by Research Foundation—Flanders (FWO) for ELIXIR Belgium (I002819N). The resources and
services used in this work were provided by the VSC (Flemish Supercomputer
Center), funded by the Research Foundation – Flanders (FWO) and the
Flemish Government
Measuring approximate functional dependencies: a comparative study
No full textApproximate functional dependencies (abbreviated: AFDs) are functional dependencies (FDs) that "almost" hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is unclear which measure is preferable when one needs to discover FDs in real-world data, i.e., data that only approximately satisfies the FD. In response, this paper formally and qualitatively compares AFD measures. We obtain a formal comparison through a novel presentation of measures in terms of Shannon and logical entropy. Qualitatively, we perform a sensitivity analysis w.r.t. structural properties of input relations. Quantitatively, we study the effectiveness of AFD measures for ranking linear AFDs on real world data. Based on this analysis, we give clear recommendations for the AFD measures to use in practice.We thank Dan Suciu for helpful discussions. S. Vansummeren was supported by the Bijzonder Onderzoeksfonds (BOF) of Hasselt University under Grant No. BOF20ZAP02. This research received funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme. This work was supported by Research Foundation—Flanders (FWO) for ELIXIR Belgium (I002819N). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government
Approximate functional dependencies: a comparison of measures and a relevance focused tool for discovery
No full textMany companies nowadays make use of data to optimize their processes. However, the collected data can contain various inconsistencies due to typing errors, for example. This forces the company to clean the data before deducing insights. One possible solution to discover erroneous information is finding columns that determine other columns, also called Functional Dependencies (FDs). For example, two people that live in the same city have to live in the same country. However, as FDs do not allow errors, we have to find a method to find dependencies that approximately hold in the relation, referred to as Approximate Functional Dependencies (AFDs). This thesis aims to design a relevance-focused tool for domain experts to discover AFDs.
We review the existing measures to determine the degree of approximation of an AFD by testing them on various theoretical examples. Based on the findings of these tests, we decide on a combination of measures that focuses on discovering relevant AFDs. Then, we integrate those measures and other AFD metadata into c-metric, a score representing the confidence in a particular AFD. Our extensive experimental evaluation of the c-metric shows that the metric is significantly more suitable for relevant AFD discovery than the existing approximation measures.
Finally, to assist domain experts in discovering relevant AFDs, we implement a tool that visualizes our c-metric and other AFD metadata, such as probability distributions
Approximate functional dependencies: a comparison of measures and a relevance focused tool for discovery
No full textMany companies nowadays make use of data to optimize their processes. However, the collected data can contain various inconsistencies due to typing errors, for example. This forces the company to clean the data before deducing insights. One possible solution to discover erroneous information is finding columns that determine other columns, also called Functional Dependencies (FDs). For example, two people that live in the same city have to live in the same country. However, as FDs do not allow errors, we have to find a method to find dependencies that approximately hold in the relation, referred to as Approximate Functional Dependencies (AFDs). This thesis aims to design a relevance-focused tool for domain experts to discover AFDs.
We review the existing measures to determine the degree of approximation of an AFD by testing them on various theoretical examples. Based on the findings of these tests, we decide on a combination of measures that focuses on discovering relevant AFDs. Then, we integrate those measures and other AFD metadata into c-metric, a score representing the confidence in a particular AFD. Our extensive experimental evaluation of the c-metric shows that the metric is significantly more suitable for relevant AFD discovery than the existing approximation measures.
Finally, to assist domain experts in discovering relevant AFDs, we implement a tool that visualizes our c-metric and other AFD metadata, such as probability distributions
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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