Overview
We are seeing exponential growth in data generated from
various platforms like social media, multimedia, enterprises,
internet of things etc. It is becoming increasingly difficult to
manage, analyze, visualize, model, store, search big data systems.
However, we also witness growth in the complexity, diversity,
number of deployments and capabilities of big data processing
systems such as Map-Reduce, Spark, HBase, Hive, Cassandra, Big
Table, Pregel and Mongo DB. The big data system may use new
operating system designs, advanced data processing algorithms,
parallelization of application, high performance computing
architectures such as GPUs etc. and clusters to improve the
performance. Traditional systems are also upgrading themselves to
co-locate with popular big data technologies.
The workshop on performance analysis of big data systems
(PABS) aims at providing a platform for scientific researchers,
academicians and practitioners to discuss techniques, models,
benchmarks, tools, case studies and experiences while dealing with
performance issues in traditional and big data systems. The
primary objective is to discuss performance bottlenecks and
improvements during big data analysis using different paradigms,
architectures and big data technologies. We propose to use this
platform as an opportunity to discuss systems, architectures,
tools, and optimization algorithms that are parallel in nature and
hence make use of advancements to improve the system performance.
This workshop shall focus on the performance challenges imposed by
big data systems and on the different state-of-the-art solutions
proposed to overcome these challenges. The accepted papers shall
be published in ACM proceedings and digital library.
TOPICS
All novel performance analysis or prediction techniques,
benchmarks, architectures, models and tools for data-intensive
computing system for optimizing application performance on
cutting-edge high performance solutions are of interest to the
workshop. Examples of topics include but not limited to:
- Performance analysis and optimization of Big data systems
and technologies
- Big data analytics using machine learning
- In-memory analysis of big data
- Performance Assured migration of traditional systems to
Big data platforms
- Deployment of Big Data technology/application on High
performance computing architectures.
- Case studies/ Benchmarks to optimize/evaluate performance
of Big data applications/systems and Big data workload
characterizations.
- Tools or models to identify performance bottlenecks and
/or predict performance metrics in Big data
- Performance analysis while querying, visualization and
processing of large network datasets on clusters of multicore,
many core processors, and accelerators.
- Performance issues in heterogeneous computing for Big
data architectures.
- Analysis of Big data applications in science,
engineering, finance, business, healthcare and telecommunication
etc.
- Data structure and algorithms for performance
optimizations in Big data systems.
- Data intensive computing
- Tools for big data analytics and management
SUBMISSIONS
Submissions describing original, unpublished recent results
related to the workshop theme, upto 6 pages in standard ACM format
can be submitted through the easychair conference system,
following this link:
EasyChair
In case of any difficulty please contact d dot chahal at tcs dot
com or rekha dot singhal at tcs dot com . All Submissions
must be in pdf format. Accepted technical papers will be included
in the ACM Digital Library