Publication
DEBS 2016
Conference paper

Spreadsheets for stream processing with unbounded windows and partitions

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Abstract

Stream processing is a computational paradigm that allows the analysis of live data streams as they are produced. This paper describes a programming model, based on enhancements to spreadsheets, that enables users with limited programming experience to participate directly in the development of complex streaming applications. The programming model augments a conventional spreadsheet with streaming features that permit operating over unbounded data sets despite the finite interface provided by the spreadsheet. The new constructs include time-based windows and partitioning. We introduce a spreadsheet compiler that generates C++ code to achieve integration with existing stream processing systems. Our experimental study illustrates the expressivity of the new features and finds that our implementation is between 8x slower and 2x faster than hand-written stream programs.

Date

13 Jun 2016

Publication

DEBS 2016

Authors

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