A Survey of RDF Data Management Systems

RDF is increasingly being used to encode data for the semantic web and for data exchange. There have been a large number of works that address RDF data management. In this paper we provide an overview of these works

The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research

Distributed Data Management in 2020?

Work on distributed data management commenced shortly after the introduction of the relational model in the mid-1970's. 1970's and 1980's were very active periods for the development of distributed relational database technology, and claims were made that in the following ten years centralized databases will be an “antique curiosity” and most organizations will move toward distributed database managers [1]. That prediction has certainly become true, and all commercial DBMSs today are distributed

White Paper: Measuring Research Outputs Through Bibliometrics

The suggested citation for this white paper is: University of Waterloo Working Group on Bibliometrics, Winter 2016. White Paper: Measuring Research Outputs through Bibliometrics, Waterloo, Ontario: University of Waterloo.This White Paper provides a high-level review of issues relevant to understanding bibliometrics, and practical recommendations for how to appropriately use these measures. This is not a policy paper; instead, it defines and summarizes evidence that addresses appropriate use of bibliometric analysis at the University of Waterloo. Issues identified and recommendations will generally apply to other academic institutions. Understanding the types of bibliometric measures and their limitations makes it possible to identify both appropriate uses and crucial limitations of bibliometric analysis. Recommendations offered at the end of this paper provide a range of opportunities for how researchers and administrators at Waterloo and beyond can integrate bibliometric analysis into their practice