- Sponsor:
- sigplan
Functional programming techniques are becoming 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; functional 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 metaprogramming 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.
However, many challenges remain. We live in an information rich world. Programming languages are not designed for this. On the programming side, as the world moves into ubiquitous computing with devices and services, it is vital that developers can productively integrate information at internet-scale into their everyday programming environment. On the information side, as the web continues to grow with linked data and open data resources being made available more broadly by government agencies (e.g. World Bank) and communities (e.g. Freebase) it becomes even more important to have the tools and mechanisms to make sense of the data.
The goal of the Data Driven Functional Programming Workshop 2013 is to discuss, promote, and advance the use of functional programming in information-rich data spaces --- including the development of new programming and data-manipulation systems and the extension of existing ones. By devising methods for handling data from the programming level, we can promote the research and development of better functional programming technologies as a whole, as well as facilitate the shift towards both principled and effective data-centric computing.
The papers included in the proceedings explore the area of data-centric programming in the light of today's data challenges, particularly with a view to the application of functional programming and metaprogramming techniques. Research projects and short papers explore the following topics:
Formal systems that capture the essential theoretical elements of data-centric programming
Experimental systems that demonstrate novel data-centric programming techniques
Technology that demonstrate correctness, scalability, productivity, robustness or maintainability of data-centric programs
Schema evolution, schema-type mapping, query languages, probabilistic programming, networkconnected programming or semi-structured data
Programming-related aspects of knowledge representation techniques including the database theory, ontology techniques and linked data.
New views and opinions, coming from the functional programming and semantic web communities, address some of the following questions:
How to effectively explore, navigate, understand, analyze and present data to the developer
What are the necessary tools and language features needed to help streamline the developer experience
How to easily access and query linked data
How to seamlessly integrate and manipulate diverse datasets
How to help humans make sense of semantically-rich data
How to address schema evolution, schema-type mapping, query languages, probabilistic programming?
In addition, the program includes a keynote, a tutorial on Information-rich Programming and a panel on Next Generation Data-Driven Programming. We hope that these proceedings will help promote the discussion among the data-driven research and the functional programming communities.
Proceeding Downloads
Themes in information-rich functional programming for internet-scale data sources
The F# language includes a feature called "F# 3.0 Type Providers" to support the integration of internet-scale information sources into a strongly typed functional-first programming environment. In this position paper we describe the key themes in ...
Algebraic data types for language-integrated queries
The seamless integration of relational databases and programming languages remains a major challenge. Mapping rich data types featured in general-purpose programming languages to the relational data model is one aspect of this challenge. We present a ...
Incompleteness-aware programming with RDF data
It is becoming common to publish data on theWeb by using semantic technologies like RDF as a flexible formalism for structured data representation. In particular, industrial and governmental authorities have started to publish open data in RDF; see, for ...
Streaming big data with self-adjusting computation
Many big data computations involve processing data that changes incrementally or dynamically over time. Using existing techniques, such computations quickly become impractical. For example, computing the frequency of words in the first ten thousand ...
Visualizing data on the web
We present a language-integrated technique that can be applied to enlist web-based data visualization libraries in the type-safe discipline of F#, and to use them with various data access mechanisms to visualize data on the web quickly and effectively ...
Cited By
- Jiu M and Sahbi H Deep kernel map networks for image annotation 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (1571-1575)
- Jiu M and Sahbi H Laplacian deep kernel learning for image annotation 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (1551-1555)
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Dongel T and Cevikalp H (2014). Semi automatic annotation tool 2014 22nd Signal Processing and Communications Applications Conference (SIU), 10.1109/SIU.2014.6830417, 978-1-4799-4874-1, (1067-1070)
- Proceedings of the 2013 workshop on Data driven functional programming