Paper Info
Title
Dependency Visualization In Data Stream Profiling
Abstract
Data stream profiling concerns the automatic extraction of metadata from a data stream, without having the possibility to store it. Among the metadata of interest, functional dependencies (Fds), and their extensions relaxed functional dependencies (RFds), represent an important semantic property of data. Nowadays, there are many algorithms for automatically discovering them from static datasets, and some are being proposed for data streams. However, one of the main problems is that the stream nature of data requires a different paradigm of monitoring, since the "big" number of (R)Fds that might hold on a given dataset continuously change as new data are read from the stream. In this paper, we present a tool for visualizing RFds discovered from a data stream. The tool permits to explore results for different types of RFds, and uses quantitative measures to monitor how discovery results evolve. Moreover, the tool enables the comparison among RFds discovered across several executions, also proving visual manipulation operators to dynamically compose and filter results. A user study has been conducted to assess the effectiveness of the proposed visualization tool. (C) 2021 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.bdr.2021.100240
BIG DATA RESEARCH
Keywords
DocType
Volume
Data stream profiling, Big data visualization, Metadata visualization, Continuous discovery, Relaxed functional dependencies
Journal
25
ISSN
Citations 
PageRank 
2214-5796
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Bernardo Breve102.70
Loredana Caruccio24812.92
Stefano Cirillo302.37
Vincenzo Deufemia444940.96
Giuseppe Polese526338.68