Measuring mashup similarity in open data innovation contests
Abstract
Contests have become an important instrument for fostering the development of novel open data mash-ups, in short open data innovations. Literature calls for new methods for measuring the similarity of open data mash-ups in order to identify code cloning and creative re-use of components of applications. Theoretically grounded computationally methods for identifying the similarity of open data contests are lacking. This study explores the similarity measurement of data-based mashups in the context of an open data innovation contest. Three different dimensions of mashup similarity are defined: code similarity, functional feature similarity, and visualized feature similarity. The results from the contest, including the source code, the running project and the descriptive documents, are collected as the research data for this study. Data analysis is based on the design and development of computational approaches to measure technology and functional similarity. The findings of this study will be helpful in better understanding the similarity of solutions in an open data innovation contest. This study contributes to the theoretical and practical approaches for similarity measurement, especially in the field of mashup development.
Degree
M.S.
Advisors
Brunswicker, Purdue University.
Subject Area
Information Technology
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