Programmers across a wide range of disciplines (e.g., bioinformatics, neuroscience, econometrics, finance, data mining, information retrieval, machine learning) write scripts to parse, transform, process, and extract insights from data. To speed up iteration times, they split their analyses into stages and write extra code to save the intermediate results of each stage to files so that those results do not have to be re-computed in every subsequent run. As they explore and refine hypotheses, their scripts often create and process lots of intermediate data files. They need to properly manage the myriad of dependencies between their code and data files, or else their analyses will produce incorrect results. To enable programmers to iterate quickly without needing to manage intermediate data files, we added a set of dynamic analyses to the programming language interpreter so that it automatically memoizes (caches) the results of long-running pure function calls to disk, manages dependencies between code and on-disk data, and later re-uses memoized results, rather than re-executing those functions, when guaranteed safe to do so. We created an implementation for Python and show how it enables programmers to iterate faster on their data analysis scripts while writing less code and not having to manage dependencies between their code and datasets. Categories and Subject Descriptors:

In his keynote speech at the 2009 RecSys conference, Francisco Martin indicated that the main challenge in recommender system industry is not to discover algorithms that provide good recommendations, but to provide users with a usable and intuitive

Yahoo! Pipes is a well-known, widely used visual programming environment for creating data mashups by aggregating, manipulating, and publishing web feeds. It provides a natural laboratory for observing a range of end-user programming (EUP) behaviors on a large scale. We have examined more than 30,000 Pipes compositions in a search for regularities that might inform the design of EUP systems and their services. Although Pipes primitives span a broad range of functionality and can be richly parameterized and composed, we find a number of patterns that govern the structure and parameterization of Pipes in the wild. Most users sample only a tiny fraction of the available design space, and simple models describe their composition behaviors. Our findings are consistent with the idea that users attempt to minimize the degrees of freedom associated with a composition as it is built and used. 1.

Abstract — The concepts of sensor recycling and re-use offer a new developmental methodology for both centralised and network-based interactive multimedia systems and multimedia art applications. This work formalises the proposed methodology, researches common issues that appear when reuse or recycling of sensing devices is introduced and discusses its application within physical interactive systems. It becomes apparent that the high system development costs typically introduced in the visual arts domain can clearly be reduced via the use of alternative recycled and reused sensing devices. A number of interactive new-media art systems case studies are presented to demonstrate its flexibility, economy and ecological advantages. This work aims to render sensor re-use a design choice that offers an alternative and inexpensive approach from the theoretical, engineering and artistic perspectives in various sensor-driven interactive multimedia systems.