Exploring Highly Structured Data: A Comparative Study of Stardinates and Parallel Coordinates

Author(s):  
M. Lanzenberger ◽  
S. Miksch ◽  
M. Pohl
2015 ◽  
Vol 34 (3) ◽  
pp. 261-270 ◽  
Author(s):  
Rassadarie Kanjanabose ◽  
Alfie Abdul-Rahman ◽  
Min Chen

2021 ◽  
Vol 72 ◽  
pp. 943-1027
Author(s):  
Giannis Nikolentzos ◽  
Giannis Siglidis ◽  
Michalis Vazirgiannis

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.


Author(s):  
Grégory Cobéna ◽  
Talel Abdessalem

Change detection is an important part of version management for databases and document archives. The success of XML has recently renewed interest in change detection on trees and semi-structured data, and various algorithms have been proposed. We study different algorithms and representations of changes based on their formal definition and on experiments conducted over XML data from the Web. Our goal is to provide an evaluation of the quality of the results, the performance of the tools and, based on this, guide the users in choosing the appropriate solution for their applications.


2018 ◽  
Vol 7 (2.27) ◽  
pp. 1
Author(s):  
Rapinder Kaur ◽  
Vaishali Chauhan ◽  
Urvashi Mittal

Immoderate amount of data is being generated everyday across the world via miscellaneous sources or fields which create issues to the users. Due to this rapid growth, the crucial issue is to analyse the big data with the help of traditional data processing tactics. Structured data is not the peerless but moreover unstructured data and semi-structured data charge up the supplementary consequences to handle this voluminous data. As in this gigantic bulk of data highly advantageous information is hidden which can be good for what ails the individual, group or organization and for adding up to more sophisticated or valuable decisions. So in order to deal with this many new tools and techniques have been excogitated. These tools can analyse the large volume of data being generated at unprecedented speed. This paper shows the comparative study of some of the data analytics techniques which can untangle the big data analytics issues by examining it in more précised manner. The contrast study of Hadoop, Hive and Pig has been illustrated which covers the working of these techniques.


2020 ◽  
Author(s):  
Bruno Oliveira Ferreira de Souza ◽  
Éve‐Marie Frigon ◽  
Robert Tremblay‐Laliberté ◽  
Christian Casanova ◽  
Denis Boire

2001 ◽  
Vol 268 (6) ◽  
pp. 1739-1748
Author(s):  
Aitor Hierro ◽  
Jesus M. Arizmendi ◽  
Javier De Las Rivas ◽  
M. Angeles Urbaneja ◽  
Adelina Prado ◽  
...  

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