Identifying Informative System Metrics: Predicting the predictability of time series using entropy

Building predictive models using cloud metrics for a task like incident prediction in the cloud is becoming ubiquitous in cloud monitoring. For such a forecasting task, if we know beforehand which system metrics are predictable then we can easily build good models. Quantifying the predictability of cloud metrics can help us rank the available...

Implementing the Decomposition of Soundness Proofs of Abstract Interpreters in Coq

Abstract interpretation is a way of approximating the semantics of a computer program, in which we derive properties of those programs without actually performing the necessary computations for running the program, through the use of an abstract interpreter. To be able to trust the result of the abstract interpretation, we would to able to prove...

Capelin: Fast Data-Driven Capacity Planning for Cloud Datacenters

Cloud datacenters provide a backbone to our digital society. Crucial to meeting increasing demand while maintaining efficient operation is the activity of capacity planning. Inaccurate capacity planning for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption....

Which error detection tool to choose?

The amount of data being collected is growing exponentially, both in academics as well as in business. Unfortunately, the quality of that data can be poor, leading to poor decisions and increasing costs. Data cleaning, the process of detecting and correcting errors from a dataset, could be the solution to improve bad data.<br/>This research...

On the fairness of crowdsourced training data and Machine Learning models for the prediction of subjective properties. The case of sentence toxicity: To be or not to be #$@&%*! toxic? To be or not to be fair?

Training machine learning (ML) models for natural language processing usually requires lots of data that is often acquired through crowdsourcing. In crowdsourcing, crowd workers annotate data samples according to one or more properties, such as the sentiment of a sentence, the violence of a video segment, the aesthetics of an image, ... To...

A plug-in infrastructure for the CodeFeedr project

CodeFeedr is a research project at the software engineering division of the Delft University of Technology in collaboration with the Software Improvement Group. The research focuses on a software infrastructure which serves software practitioners in utilizing data-driven decision making. Currently, frameworks like Apache Flink are capable of...