Abstract
Information from social media can be leveraged by social scientists to support effective decision making. However, such data sources are often characterised by high volumes and noisy information, therefore data analysis should be always preceded by a data preparation phase. Designing and testing data preparation pipelines requires considering requirements on cost, time, and quality of data extraction. In this work, we aim to propose a methodology for modeling crowd-enhanced data analysis pipelines using a goal-oriented approach, including both automatic and human-related tasks, by suggesting the kind of components to include, their order, and their parameters, while balancing the trade-off between cost, time, and quality of the results.
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This work was funded by the European Commission H2020 Project Crowd4SDG, #872944.
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Cappiello, C., Pernici, B., Vitali, M. (2021). Modeling Adaptive Data Analysis Pipelines for Crowd-Enhanced Processes. In: Ghose, A., Horkoff, J., Silva Souza, V.E., Parsons, J., Evermann, J. (eds) Conceptual Modeling. ER 2021. Lecture Notes in Computer Science(), vol 13011. Springer, Cham. https://doi.org/10.1007/978-3-030-89022-3_3
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DOI: https://doi.org/10.1007/978-3-030-89022-3_3
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