Upscaling Peatland Science Through Collaborative Big Data

Big Data Science Needs Big Data Middleware

There has been a “Cambrian explosion” of big data systems proposed and evaluated in the last eight years, but relatively little understanding of how these systems or the ideas they represent compare and complement one another. In enterprise and science situations, “one size is unlikely to fit all”: we see analytics teams running multiple systems simultaneously. However, the highest level of abs...

Supporting Collaborative Science through a Knowledge and Data Management Portal

This paper discusses a portal used by geoscientists and human-environment relations researchers to capture and share the evolution of concepts and the emergence of agreement through collaboration. A concept graph interface to the portal encodes relationships among people, concepts, data, tools, tasks, times, and places. By linking resources in flexible and reusable structures, we attempt to sit...

Big Data, Data Science, and Civil Rights

Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much ...

Data science, big data and granular mining

Data science, big data and granular mining

With the evolution of various modern technologies, huge amount f data is being constantly generated and collected around us. We re in the midst of what is popularly called information revolution nd are living in a so-called world of knowledge. Intentionally and/or ccidentally, generation of these data is inevitable. As a result, large ata, broadly characterised by three Vs – large volume, veloc...