Searching and mining trillions of time series subsequences under Dynamic Time Warping

Searching and mining trillions of time series subsequences under dynamic time warping - Rakthanmanon et al. SIGKDD 2012 What an astonishing paper this is! By 2012, Dynamic Time Warping had been shown to be the time series similarity measure that generally performs the best for matching, but because of its computational complexity researchers and practitioners … Continue reading Searching and mining trillions of time series subsequences under Dynamic Time Warping

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics - Venkataraman et al. 2016 With cloud computing environments such as Amazon EC2, users typically have a large number of choices in terms of the instance types and number of instances they can run their jobs on. Not surprisingly, the amount of memory per core, storage media, … Continue reading Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics

MacroBase: Analytic Monitoring for the Internet of Things

MacroBase: Analytic Monitoring for the Internet of Things - Bailis et al. 2016 It looks like Peter Alvaro is not the only one to be doing some industrial collaboration recently! MacroBase is the result of Peter Bailis' collaboration with Cambridge Mobile Telematics (CMT), an IoT company. The topic at hand is analytic monitoring - detecting … Continue reading MacroBase: Analytic Monitoring for the Internet of Things

Arabesque: A System for Distributed Graph Mining

Arabesque: A System For Distributed Graph Mining - Teixeira et al. 2015 We've studied graph computation systems before in The Morning Paper: systems such as Pregel, Giraph and GraphLab that provide vertex-centric programming models ('think like a vertex') on top of a Bulk Synchronous Parallel compute model. We've also seen some of the limitations of … Continue reading Arabesque: A System for Distributed Graph Mining