| CCA: An ML Pipeline for Cloud Anomaly Troubleshooting. | 0 | 0.34 | 2022 |
| An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems. | 1 | 0.35 | 2020 |
| Additive Explanations for Anomalies Detected from Multivariate Temporal Data | 1 | 0.35 | 2019 |
| Explainable Failure Predictions with RNN Classifiers based on Time Series Data. | 0 | 0.34 | 2019 |
| Mtex-Cnn: Multivariate Time Series Explanations For Predictions With Convolutional Neural Networks | 0 | 0.34 | 2019 |
| "Memory loss" in commodity hardware?: predicting DIMM failures with machine learning. | 0 | 0.34 | 2017 |
| Predicting DRAM reliability in the field with machine learning. | 5 | 0.44 | 2017 |
| On the adoption and impact of predictive analytics for server incident reduction. | 1 | 0.35 | 2017 |
| Predicting Disk Replacement towards Reliable Data Centers | 21 | 0.85 | 2016 |
| Predictive Analytics for Server Incident Reduction. | 0 | 0.34 | 2015 |
| Comprehensible Models for Reconfiguring Enterprise Relational Databases to Avoid Incidents | 2 | 0.37 | 2015 |
| Do you know how to configure your enterprise relational database to reduce incidents? | 1 | 0.35 | 2015 |
| Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching | 6 | 0.48 | 2015 |
| Dynamic software deployment from clouds to mobile devices | 22 | 0.84 | 2012 |
| Enabling efficient placement of virtual infrastructures in the cloud | 14 | 0.66 | 2012 |
| Understanding performance modeling for modular mobile-cloud applications | 2 | 0.38 | 2012 |
| Semanta --- Semantic Email in Action | 0 | 0.34 | 2009 |
| Calling the cloud: enabling mobile phones as interfaces to cloud applications | 137 | 6.30 | 2009 |
| ProICET: a cost-sensitive system for prostate cancer data. | 0 | 0.34 | 2008 |
