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Jump to: 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013
Number of items: 14.

2019

Nadeau, D., Ezzati-Jivan, N. & Dagenais, M. R. (2019). Efficient large-scale heterogeneous debugging using dynamic tracing. Journal of Systems Architecture, 98, p. 346-360.

Reumont-Locke, F., Ezzati-Jivan, N. & Dagenais, M. R. (2019). Efficient methods for trace analysis parallelization. International Journal of Parallel Programming, 47(5-6), p. 951-972.

Abbasi, H., Ezzati-Jivan, N., Bellaïche, M., Talhi, C. & Dagenais, M. R. (2019). Machine learning-based EDoS attack detection technique using execution trace analysis. Journal of Hardware and Systems Security, 3(2), p. 164-176.

2018

Ezzati-Jivan, N., Bastien, G. & Dagenais, M. R. (2018, April). High latency cause detection using multilevel dynamic analysis. Paper presented at Annual IEEE International Systems Conference (SysCon 2018), Vancouver, Canada (8 pages).

Bationo, Y. J., Ezzati-Jivan, N. & Dagenais, M. R. (2018, January). Efficient cloud tracing: From very high level to very low level. Paper presented at IEEE International Conference on Consumer Electronics (ICCE 2018), Las Vegas, NV, USA (6 pages).

2017

Vergé, A., Ezzati-Jivan, N. & Dagenais, M. R. (2017). Hardware-assisted software event tracing. Concurrency and Computation: Practice and Experience, 29(10), p. 1-9.

Ezzati-Jivan, N. & Dagenais, M. R. (2017). Multi-scale navigation of large trace data: A survey. Concurrency and Computation: Practice and Experience, 29(10), p. 1-20.

Wininger, F., Ezzati-Jivan, N. & Dagenais, M. R. (2017). A declarative framework for stateful analysis of execution traces. Software Quality Journal, 25(1), p. 201-229.

2016

Biancheri, C., Ezzati-Jivan, N. & Dagenais, M. R. (2016, August). Multilayer virtualized systems analysis with kernel tracing. Paper presented at 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW 2016), Vienna, Austria (7 pages).

Prieur-Drevon, L., Beamonte, R., Ezzati-Jivan, N. & Dagenais, M. R. (2016, June). Enhanced state history tree (eSHT): a stateful data structure for analysis of highly parallel system traces. Paper presented at IEEE International Congress on Big Data (BigData Congress 2016), San Francisco, CA, USA (8 pages).

2015

Kouamé, K. G., Ezzati-Jivan, N. & Dagenais, M. R. (2015, June). A flexible data-driven approach for execution trace filtering. Paper presented at IEEE International Congress on Big Data (BigData Congress 2015), New York, NY, USA (6 pages).

Ezzati-Jivan, N. & Dagenais, M. R. (2015). Cube data model for multilevel statistics computation of live execution traces. Concurrency and Computation: Practice and Experience, 27(5), p. 1069-1091.

2014

Montplaisir-Gonçalves, A., Ezzati-Jivan, N., Wininger, F. & Dagenais, M. R. (2013, September). State history tree: an incremental disk-based data structure for very large interval data. Paper presented at International Conference on Social Computing (SocialCom 2013), Alexandria, VA, USA (9 pages).

2013

Ezzati-Jivan, N. & Dagenais, M. R. (2013). A framework to compute statistics of system parameters from very large trace files. ACM SIGOPS Operating Systems Review, 47(1), p. 43-54.

This list was generated on Thu Jun 4 01:35:12 2020 EDT.