ABSTRACT
The exchange of comments, opinions, and arguments in blogs, forums, social media, wikis, and review websites has transformed the Web into a modern agora, a virtual place where all types of debates take place. This wealth of information remains mostly unexploited: due to its textual form, such information is difficult to automatically process and analyse in order to validate, evaluate, compare, combine with other types of information and make it actionable. Recent research in Machine Learning, Natural Language Processing, and Computational Argumentation has provided some solutions, which still cannot fully capture important aspects of online debates, such as various forms of unsound reasoning, arguments that do not follow a standard structure, information that is not explicitly expressed, and non-logical argumentation methods. Tackling these challenges would give immense added-value, as it would allow searching for, navigating through and analyzing online opinions and arguments, obtaining a better picture of the various debates for a well-intentioned user. Ultimately, it may lead to increased participation of Web users in democratic, dialogical interchange of arguments, more informed decisions by professionals and decision-makers, as well as to an easier identification of biased, misleading, or deceptive arguments. This paper presents the vision of the Web of Debates, a more human-centered version of the Web, which aims to unlock the potential of the abundance of argumentative information that currently exists online, offering its users a new generation of argument-based web services and tools that are tailored to their real needs.
ABSTRACT
BACKGROUND: Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. NEW INFORMATION: In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data - Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu/.
