Enhanced Publication Management Systems

Bardi, Alessia; Manghi, Paolo

doi:10.1145/2740908.2742026

Cited by 2 publications

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

ILastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data

Dimou

Haesendonck

Vanbrabant

et al. 2018

Semantics, Analytics, Visualization

View full text Add to dashboard Cite

Enriching scholarly data with metadata enhances the publications' meaning. Unfortunately, different publishers of overlapping or complementary scholarly data neglect general-purpose solutions for metadata and instead use their own ad-hoc solutions. This leads to duplicate efforts and entails non-negligible implementation and maintenance costs. In this paper, we propose a reusable Linked Data publishing workflow that can be easily adjusted by different data owners to (i) generate and publish Linked Data, and (ii) align scholarly data repositories with enrichments over the publications' content. As a proof-of-concept, the proposed workflow was applied to the iMinds research institute data warehouse, which was aligned with publications' content derived from Ghent University's digital repository. Moreover, we developed a user interface to help lay users with the exploration of the iLastic Linked Data set. Our proposed approach relies on a general-purpose workflow. This way, we manage to reduce the development and maintenance costs and increase the quality of the resulting Linked Data.

show abstract

ILastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data

Dimou

Haesendonck

Vanbrabant

et al. 2018

Semantics, Analytics, Visualization

View full text Add to dashboard Cite

show abstract

NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research

et al. 2020

View full text Add to dashboard Cite

The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Although the goals and methodologies are commonly similar, we lack initiatives to categorize the data, methods, and findings systematically. In this paper, we present Network Traffic Analysis Research Curation (NTARC), a data model to store key information about network traffic analysis research. We additionally use NTARC to perform a critical review of the field of research conducted in the last two decades. The collection of descriptive research summaries enables the easy retrieval of relevant information and a better reuse of past studies by the application of quantitative analysis. Among others benefits, it enables the critical review of methodologies, the detection of common flaws, the obtaining of baselines, and the consolidation of best practices. Furthermore, it provides a basis to achieve reproducibility, a key requirement that has long been undervalued in the area of traffic analysis. Thus, besides reading hard copies of papers, with NTARC, researchers can make use of a digital environment that facilitates queries and reviews over a comprehensive field corpus.

show abstract

Enhanced Publication Management Systems

Cited by 2 publications

References 3 publications

ILastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data

ILastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data

NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research

Contact Info

Product

Resources

About