Seminar - Jean-Daniel Fekete, INRIA

Progressive Data Analysis: a new computation paradigm for scalability in exploratory data analysis

Thursday 13th April

14:00 - 15:30

A130 (College Building, First Floor)

In his talk, Jean-Daniel will talk about a novel computation paradigm called Progressive Data Analysis that brings at the programming language level the low-latency guarantee by performing computations in a progressive fashion.

Jean-Daniel suggests that such novel mechanisms are highly critical in exploratory data analysis which requires a short feedback loop due to human cognitive capabilities and limitations. During the talk, he will describe this new paradigm, report on novel experiments showing that human can cope effectively with progressive systems, and show demos using a prototype implementation called ProgressiVis, explain the requirements it implies through exemplar applications.

Jean-Daniel Fekete (http://www.aviz.fr/~fekete) is Senior Research Scientist (DR1) at INRIA, Scientific Leader of the INRIA Project Team AVIZ that he founded in 2007. His main Research areas are Visual Analytics, Information Visualization and Human Computer Interaction. He published more than 150 articles in multiple conferences and journals, including the most prestigious in visualization (TVCG, InfoVis, EuroVis, PacificVis) and Human-Computer Interaction (CHI, UIST) ( some of which are available on the AVIZ pages).

He is the chair of the IEEE Information Visualization Conference Steering Committee, member of the IEEE VIS Executive Committee, member of the Eurographics EuroVis Steering Committee, and member of the Eurographics publication board. He is also an ACM Distinguished Speaker.

In 2015, he was on Sabbatical at the Visualization and Computer Graphics group at NYU-Poly, and at the Visual Computing Group at Harvard.

Full Abstract of the Talk: 

Exploring data requires a short feedback loop, with a latency of at most 10 seconds because of human cognitive capabilities and limitations. When data becomes large or analyses become complex, sequential computations can no longer be completed in a few seconds and interactive exploration is severely hampered. This talk will describe a novel computation paradigm called Progressive Data Analysis that brings at the programming language level the low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems — including visual analytics — from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory analytics systems. I will describe the new paradigm, report on novel experiments showing that human can cope effectively with progressive systems, and show demos using a prototype implementation called ProgressiVis, explain the requirements it implies through exemplar applications, and present opportunities and challenges ahead, in the domains of visualization, visual analytics, machine-learning, and databases.