Optimizing BDDs for Time-Series dataset manipulation

Abstract

In this work we advocate the adoption of Binary Decision Diagrams (BDDs) for storing and manipulating Time-Series datasets. We first propose a generic BDD transformation which identifies and removes 50% of all BDD edges without any loss of information. Following, we optimize the core operation for adding samples to a dataset and characterize its complexity. We identify time-range queries as one of the core operations executed on time-series datasets, and describe explicit Boolean function constructions that aid in efficiently executing them directly on BDDs. We exhibit significant space and performance gains when applying our algorithms on synthetic and real-life biosensor time-series datasets collected from field trials.