Publications
Evaluation of stream processing frameworks Ghent University
Acquisition modeling in the nonprofit sector based on Facebook data Ghent University
This research investigates whether Facebook data can be used to predict donation behavior. On the basis of individual specific Facebook data (i.e. Facebook groups, pages, and page categories), we build an acquisition model that predicts an individual’s likelihood to become a donor. Three different classification algorithms are being compared, namely Bagging, Random Forest and Adaboost. Furthermore, by constructing different models, we assess the ...
Latency measurement of fine-grained operations in benchmarking distributed stream processing frameworks Ghent University
This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmarking of fine-grained individual stages of a processing pipeline. By determining the latency of distinct common operations in the processing flow instead of the end-to-end latency, we can form guidelines for efficient processing pipeline design. Additionally, we address the issue of defining time in distributed systems by capturing time on one ...
Evaluating the importance of different communication types in romantic tie prediction on social media Ghent University
The added value of social media data in B2B customer acquisition systems : a real-life experiment Ghent University
Identifying soccer players on Facebook through predictive analytics Ghent University
Predicting consumer load profiles using commercial and open data Ghent University
Automated Metering Infrastructure (AMI) has gradually become commonplace within the utilities industry and has brought with it numerous improvements in all related fields. Specifically in tariff setting and demand response models, classification of smart meter readings into load profiles helps in finding the right segments to target. This paper addresses the issue of assigning new customers, for whom no AMI readings are available, to one of ...