Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/79509

Potentialfinder - Fostering Network Innovation by Connecting Data Owners Using Scaled Business-Relevant Pattern Recognition and Clustering

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Title:Potentialfinder - Fostering Network Innovation by Connecting Data Owners Using Scaled Business-Relevant Pattern Recognition and Clustering
Authors:Riechert, Mathias
Bhandari, Roshan
Amle, Abhijeet
Abhinav, Abhimanyu
Hubig, Nina
Keywords:Digital Innovation in a Networked World
big data
exponential patterns
innovation networks
network analysis
show 1 morepattern recognition
show less
Date Issued:04 Jan 2022
Abstract:The advancement in collection, computing and storage technologies has led to an exponential growth of available data in multiple disciplines. However, the human capacity of analyzing this data does not grow at the same rate, leaving a vast amount of potential disparate, invisible and unused. We want to enhance the capability of humans to automatically find relevant patterns in data to leverage potential in this increasing sea of data. We present an innovation network creation framework and Python library that detects exponential growth patterns from publicly available tabular data. It works as a magnifying glass to reveal the most relevant parts of the data and the processes represented by it. The extracted exponential patterns can be useful for topic or disease detection as well as for organisations such as venture capital and consulting firms to improve investment decisions. Additionally, startups and innovation units in corporates can leverage these information to base their business models on insights into sectors, markets or customer segments with exponential growth. To foster the innovation based on the revealed patterns, we connect the respective data owners that uploaded similar patterns. This paper proposes a framework for networked innovation creation including a) an algorithm to automatically detect exponential, b) approaches to scale its application to public tabular data in different sizes and formats, c) a similarity network connecting the found patterns to innovation networks, d) a clustering to group the data owners and enable co- and crowd innovation. We run experiments on large scale data for all steps to provide evidence for cost-efficiency, scalability and feasibility of the contributions.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79509
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.177
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Digital Innovation in a Networked World


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