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CrowdCorrect: A Curation Pipeline for Social Data Cleansing and Curation

  • Amin Beheshti
  • Kushal Vaghani
  • Boualem Benatallah
  • Alireza Tabebordbar
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 317)

Abstract

Process and data are equally important for business process management. Data-driven approaches in process analytics aims to value decisions that can be backed up with verifiable private and open data. Over the last few years, data-driven analysis of how knowledge workers and customers interact in social contexts, often with data obtained from social networking services such as Twitter and Facebook, have become a vital asset for organizations. For example, governments started to extract knowledge and derive insights from vastly growing open data to improve their services. A key challenge in analyzing social data is to understand the raw data generated by social actors and prepare it for analytic tasks. In this context, it is important to transform the raw data into a contextualized data and knowledge. This task, known as data curation, involves identifying relevant data sources, extracting data and knowledge, cleansing, maintaining, merging, enriching and linking data and knowledge. In this paper we present CrowdCorrect, a data curation pipeline to enable analysts cleansing and curating social data and preparing it for reliable business data analytics. The first step offers automatic feature extraction, correction and enrichment. Next, we design micro-tasks and use the knowledge of the crowd to identify and correct information items that could not be corrected in the first step. Finally, we offer a domain-model mediated method to use the knowledge of domain experts to identify and correct items that could not be corrected in previous steps. We adopt a typical scenario for analyzing Urban Social Issues from Twitter as it relates to the Government Budget, to highlight how CrowdCorrect significantly improves the quality of extracted knowledge compared to the classical curation pipeline and in the absence of knowledge of the crowd and domain experts.

Notes

Acknowledgements

We Acknowledge the Data to Decisions CRC (D2D CRC) and the Cooperative Research Centres Program for funding this research.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Amin Beheshti
    • 1
    • 2
  • Kushal Vaghani
    • 1
  • Boualem Benatallah
    • 1
  • Alireza Tabebordbar
    • 1
  1. 1.University of New South WalesSydneyAustralia
  2. 2.Macquarie UniversitySydneyAustralia

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