Freek Bomhof, MSc
- data analysis
- artificial intelligence
- big data
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The application of machine learning in big data has attracted a lot of attention, both in science and in business. Society and business increasingly depend on data analytics as a basis for decision-making. While almost every practitioner is familiar with the ‘garbage in = garbage out’ adage, there are many sources of uncertainties that are not always taken into account, not widely understood, and especially are not studied in relation to each other. Sources can be inaccurate, noisy or (temporarily) unavailable data; poor or faulty preprocessing and filtering; models that are trained on non-representative or biased datasets; or inadequate visualizations and interpretations of the output.
The goal of this workshop is to create insights in the relations between all sources of uncertainty in big data processing pipelines, to create a more comprehensive approach that enables business to deploy reliable, accurate processing. The workshop aims to create an overview of the state of the art in handling uncertainties in big data, and to identify the areas that need more attention in order to create reliable, useful and acceptable Big Data applications.
Contributions are expected from research that provides original and new insights into the effects of combined uncertainties in big data processing pipelines, including, but not limited to, research topics like:
The workshop will take place during the afternoon session on the first day of the 14th IEEE eScience 2018 event.
Are you a scientist, or are you interested in improving your business or product? Make sure to join our workshop!