@ARTICLE{boracchi16TNNLS, 
author={C. Alippi and G. Boracchi and M. Roveri}, 
journal={IEEE Transactions on Neural Networks and Learning Systems}, 
title={Hierarchical Change-Detection Tests}, 
year={2016}, 
volume={PP}, 
number={99}, 
pages={1-13}, 
abstract={We present hierarchical change-detection tests (HCDTs), as effective online algorithms for detecting changes in datastreams. HCDTs are characterized by a hierarchical architecture composed of a detection layer and a validation layer. The detection layer steadily analyzes the input datastream by means of an online, sequential CDT, which operates as a low-complexity trigger that promptly detects possible changes in the process generating the data. The validation layer is activated when the detection one reveals a change, and performs an offline, more sophisticated analysis on recently acquired data to reduce false alarms. Our experiments show that, when the process generating the datastream is unknown, as it is mostly the case in the real world, HCDTs achieve a far more advantageous tradeoff between false-positive rate and detection delay than their single-layered, more traditional counterpart. Moreover, the successful interplay between the two layers permits HCDTs to automatically reconfigure after having detected and validated a change. Thus, HCDTs are able to reveal further departures from the postchange state of the data-generating process.}, 
keywords={Algorithm design and analysis;Big data;Change detection algorithms;Computer architecture;Delays;Learning systems;Monitoring;Big data analytics;change-detection tests (CDTs);cognition-inspired systems;cognitive fault detection;hierarchical architectures;stream data analytics.}, 
doi={10.1109/TNNLS.2015.2512714}, 
ISSN={2162-237X}, 
month={},}