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1st International Workshop on Algorithms & Architectures for Distributed Data Analytics


In the past few years, Big Data Analytics is new ubiquitous term involving multiple areas such as engineering, applied mathematics, medicine, computational biology, healthcare, social networks, finance, business, government, education, transportation and telecommunications. It is a rapidly expanding research area of computer science for understanding and solving complex problems in these diverse fields.


To solve the challenges of Data Analytics which generates huge volumes and variety of data with great velocity the need for parallel & distributed algorithms and architectures arises. The goal of the International Workshop on Algorithms & Architectures for Distributed Data Analytics (AADDA) is an open forum for the exchange of knowledge on distributed algorithms & architectures for big data analytics.


Topics of interest include, but are not limited to:


- Distributed Graph Analytics
- Architectures for Distributed Data Analytics
- Parallel and Distributed Computing Algorithms for Big Data
- Distributed algorithms for Big Data Search
- Distributed and Shared Memory Models for Data Analytics
- Shared and Transactional Memory for Big Data
- Programming Models for Distributed Data Analytics
- Distributed Algorithms for Data Visualization
- Distributed Signal Processing for Data Analytics
- Distributed Data analytics across Distributed Clouds


Submission Guidelines:


Papers describing original ideas should be written in English and in PDF format.Submissions should include: a title, the names of the authors, their affiliations and the contact author's email. Each submission must not exceed six double-column pages (excluding references) which will describe the results. Additional necessary details may be included in a clearly marked appendix which will be read at the discretion of the program committee. Shorter submissions are welcome. The submissions will be judged based on the merit of the ideas rather than the length. Submissions must be made through the online submission site. The accepted papers will be published on ACM digital library.

Authors are also welcome submit work currently in progress while not exceeding six page limit. The authors can later submit the completed and full version at other venues. In this case, final papers will be available to participants electronically at the meeting, but to facilitate resubmission to more formal venues, no archival proceedings will be published, and papers will not be sent to the ACM Digital Library. The authors must clearly indicate that they are submitting work in progress on the title page. But these papers will also be treated as normal from a review perspective. They will be subject to same rigor of review as regular papers.


At least one author of each accepted paper must register for the conference and present the paper. In case of no-shows of accepted papers at the workshop will result in those papers NOT being included in the proceedings and will also not be made electronically available.


Submission Instructions:


Papers can be submitted here: https://easychair.org/conferences/?conf=aadda01


Important Dates


Deadline for Submission: 
Acceptance Notification: 
Camera Ready Due: 
Workshop Date: 

  October 21, 2016
  November 10, 2016
  November 23, 2016
  January 04, 2017



Keynote Speaker

Dr. Naveen Sivadasan

Naveen Sivadasan is a senior scientist at TCS Innovation Labs, Hyderabad. Naveen did his PhD in the area of algorithms from Max-Planck Institute for Computer Science, Germany. His research interests include algorithms for large data, graph theory, high performance computing and computational genomics. Prior to joining TCS Innovation labs, he was an assistant professor in the Department of Computer Science and Engineering at Indian Institute of Technology Hyderabad for about five years.


Topic: Computing on Genome-scale Data

Genomics is witnessing an unprecedented explosion of data. Second generation and third generation high throughput sequencing technologies produce massive amounts of raw data that require sophisticated techniques for efficient processing and downstream analyses. Several ongoing projects such as 1000 genomes, million plant and animal genomes and million micro-ecosystem genomes pose additional challenges with regard to efficient storage, distribution and analysis. Genome analysis is undergoing a paradigm shift from single genome reference to pan genome reference and consequently adopting richer structures such as graphs for succinct representation and efficient processing. In this talk, we will discuss some of these aspects and related computational challenges.



Steering Committee Chairs


R K Shyamasundar, Indian Institute of Technology, Bombay, India
Sajal K. Das, Missouri University of Science and Technology, USA (Co-chair)
Sukumar Ghosh, University of Iowa, USA (Co-chair)


General/TPC Chairs


Sathya Peri, IIT Hyderabad, India


Program Committee (to be updated shortly)


- Naveen Sivadasan, TCS
- Neeraj Mittal, University of Texas at Dallas, USA
- Kishore Kothapalli, IIIT Hyderabad, India
- Ajoy Datta, University of Nevada at Las Vegas, USA
- M V Panduranga Rao, IIT Hyderabad, India
- Smruti Ranjan Sarangi, IIT Delhi, India
- Sandeep Kulkarni, Michigan State University, USA
- Subrahmanyam Kalyanasundaram, IIT Hyderabad, India
- Arnab Bhattacharya, IIT Kanpur, India
- Manohar Kaul, IIT Hyderabad, India
- Sobhan Babu, IIT Hyderabad, India
- Sameep Mehta, IBM Research, Delhi, India
- Srikanta B Jagannath, IBM Research, Delhi, India
- Reiji Suda, University of Tokyo, Tokyo
- Madhavan Mukund, Chennai Mathematical Institute, India