Streamlining the management and analysis of preference data.
Preferences are orders among a collection of items attributed to a population of judges. Preference data comes in a variety of forms, such as ranked lists and pairwise comparisons, and is ubiquitous in a plethora of applications across different domains. Over the past decade, there has been a sharp increase in the volume of preference data, in the diversity of applications that use it, and in the richness of preference data analysis methods. Examples of applications include rank aggregation in genomic data analysis, management of votes in elections and recommendation systems in e-commerce.More about the project
Graphs are used to represent a plethora of phenomena, from the Web and social networks, to biological pathways, to semantic knowledge bases. Arguably the most interesting and important questions one can ask about graphs have to do with their evolution. Which Web pages are showing an increasing popularity trend? How does influence propagate in social networks? How does knowledge evolve?
Much research and engineering effort today goes into developing sophisticated graph analytics and their efficient implementations, both stand-alone and in scope of data processing platforms. Yet, systematic support for scalable querying and analytics over evolving graphs still lacks. In this project we aim to build a system that fills this gap.
If not used responsibly, big data technology can propel economic inequality, destabilize global markets and affirm systemic bias. While the potential opportunity of big data techniques is well accepted, the necessity of using these techniques responsibly should be discussed. In the society we envision, data and data analysis are fair, transparent and available equally to all.