---------------
CALL FOR PAPERS
---------------
BDL 2022

Workshop on Big Data & Deep Learning in
High Performance Computing

Bordeaux, France, November 2-5, 2022

https://www.dcc.fc.up.pt/bdl2022/

----------------------
Aims and scope of BDL
----------------------

The number of very large data repositories (big data) is increasing in
a rapid pace. Analysis of such repositories using the traditional
sequential implementations of Machine Learning (ML) and emerging
techniques, like deep learning, that model high-level abstractions in
data by using multiple processing layers, requires expensive
computational resources and long running times.

Parallel or distributed computing are possible approaches that can
make analysis of very large repositories and exploration of high-level
representations feasible. Taking advantage of a parallel or a
distributed execution of a ML/statistical system may: i) increase its
speed; ii) learn hidden representations; iii) search a larger space
and reach a better solution or; iv) increase the range of applications
where it can be used (because it can process more data, for
example). Parallel and distributed computing is therefore of high
importance to extract knowledge from massive amounts of data and learn
hidden representations.

The workshop will be concerned with the exchange of experience among
academics, researchers and the industry whose work in big data and
deep learning require high performance computing to achieve
goals. Participants will present recently developed
algorithms/systems, on going work and applications taking advantage of
such parallel or distributed environments.

------
Topics
------

BDL 2022 invites papers on all topics in novel data-intensive
computing techniques, data storage and integration schemes, and
algorithms for cutting-edge high performance computing architectures
which targets Big Data and Deep Learning are of interest to the
workshop. Examples of topics include but not limited to:

* parallel algorithms for data-intensive applications;
* scalable data and text mining and information retrieval;
* using Hadoop, MapReduce, Spark, Storm, Streaming to analyze Big Data;
* energy-efficient data-intensive computing;
* deep-learning with massive-scale datasets;
* querying and visualization of large network datasets;
* processing large-scale datasets on clusters of multicore and
manycore processors, and accelerators;
* heterogeneous computing for Big Data architectures;
* Big Data in the Cloud;
* processing and analyzing high-resolution images using
high-performance computing;
* using hybrid infrastructures for Big Data analysis;
* new algorithms for parallel/distributed execution of ML systems;
* applications of big data and deep learning to real-life problems.

------------------
Program Chairs
------------------

João Gama, University of Porto, Portugal
Carlos Ferreira, Polytechnic Institute of Porto, Portugal
Miguel Areias, University of Porto, Portugal

-----------------
Program Committee
-----------------

TBA

----------------
Important dates
----------------

Submission deadline: July 30, 2022(AoE)
Author notification: September 2, 2022
Camera-ready: September 12, 2022
Registration deadline: September 14, 2022

----------------
Paper submission
----------------

Papers submitted to BDL 2022 must describe original research results
and must not have been published or simultaneously submitted anywhere
else.

Manuscripts must follow the IEEE conference formatting guidelines and
submitted via the EasyChair Conference Management System as one pdf
file. The strict page limit for initial submission and camera-ready
version is 8 pages in the aforementioned format.

Each paper will receive a minimum of three reviews by members of the
international technical program committee. Papers will be selected
based on their originality, relevance, technical clarity and quality
of presentation. At least one author of each accepted paper must
register for the BDL 2022 workshop and present the paper.

-----------
Proceedings
-----------

All accepted papers will be published at IEEE Xplore.