MyImage

Djamel Edine YAGOUBI


Data Scientist & Data Engineer

Short Bio.

I am Data Scientist & Data Engineer, software Development is my biggest passion. I am always looking for something new to learn, for a new way to improve, for the next challenge.

I did my PhD in Computer Science at Inria, in the Zenith team at Montpellier, in the area of Big Data Analytics. I hold an M.Sc in Computer Science from University of Oran 1, Algeria, in 2014. The title of my M.Sc thesis was "The management of energy consumption in Clouds Computing".

A selection of main publications.

Djamel Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas: Massively Distributed Time Series Indexing and Querying. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018

Djamel Edine Yagoubi, Reza Akbarinia, Boyan Kolev, Oleksandra Levchenko, Florent Masseglia, Patrick Valduriez, Dennis Shasha: ParCorr: Efficient Parallel Methods to Identify Similar Time Series Pairs across Sliding Windows. Data Mining and Knowledge Discovery, pp 1-27, 2018

Djamel Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas: DPiSAX: Massively Distributed Partitioned iSAX. IEEE International Conference on Data Mining (ICDM), 2017

Djamel Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Dennis Shasha: RadiusSketch: Massively Distributed Indexing of Time Series. IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017

Education.

P.hD. in computer science from the University of Montpellier, France (March 2018) : Massive distribution for indexing and mining time series.

Masters of Science from University of Oran 1, Algeria (Summer 2014): Management of energy consumption in Clouds Computing. Master's thesis (in french). Slides (in french).

Baccalaureate diploma in experimental science, Oran, Algeria (Summer 2009).

Projects.

A list for some of the projects that I have completed during the last few years: DPiSAX: Massively Distributed Partitioned iSAX. RadiusSketch: Massively Distributed Indexing of Time Series. ParCorr: efficient parallel methods to identify similar time series pairs across sliding window.

Code Repos.

Here is where you can find some of my code : @djamelinfo On GitHub. @yagoubi On GitLab.

Contact.

yagoubi.djamel@gmail.com

@ydjameledine

© 2018 Djamel Edine YAGOUBI Some Rights Reserved.