Jeremy.Gibbons | 13 Oct 21:08 2013

Call for Talk Proposals: Data-Centric Programming, San Diego, Jan 2014

ACM SIGPLAN Workshop on Data-Centric Programming 2014

Colocated with POPL, January 25, 2014 | San Diego, USA
Submission: November 18, 2013
Notification: December 2, 2013

We're very pleased to announce DCP 2014, an exciting workshop which builds on the success of the
Data-Driven Functional Programming (DDFP) workshop at POPL 2013. This workshop is for anyone who loves
the application of functional programming (and indeed other programming paradigms as well) to
data-rich domains. Please consider submitting to the workshop - whatever your flavor of data, whatever
your flavor of data-centric programming. We want this to be a great event that opens up opportunities at
the intersection of data and programming.

Functional programming techniques are increasingly important in data-centric programming: languages
like Haskell, Scala, and C# draw heavily on a range of functional techniques and find application in
numerous data-driven domains; paradigms like map/reduce and its extensions lie at the core of modern
scalable data processing; and "information-rich" languages like Ur, F#, and Gosu use meta-programming
to integrate type-safe queries, web-based APIs, and scalable data sources - along with associated
semantically-rich metadata - into the programming language. In principle, the expressiveness, strong
typing, and core functional paradigm of these languages make them an ideal choice for expressing robust
and scalable data-centric programming. 

On the other end, the web of data is growing at an enormous pace, with few dedicated software applications
capable of dealing efficiently in information-rich spaces. Reasons for that include one (or more) of the
following research issues: lack of integrated development environments (IDEs, such as Visual Studio
and Eclipse), poor programming language support, lack of standard testbeds and/or benchmarks,
inadequate training, and perhaps the need for curriculum revision. Properly addressing these issues
requires interdisciplinary skills, and the collaboration between academia and industry. 
(Continue reading)