For Tutorial Participation Registration Form

DaSAA 2017 invite proposals for tutorials that are aimed at researchers, students and working professionals. This pre-conference tutorial will be in conjunction with the International Conference on Data Science Analytics and Applications and is scheduled on January 4, 2017.

The topics for the tutorial shall cover the emerging research areas in the field of “Data Science Analytics and Applications” and allied areas. Tutorials on interdisciplinary directions, novel and fast growing fields, and significant applications are highly encouraged.

Tutorial notes will be given to the attendees of the Tutorial.

Tutorial proposal shall be submitted to not exceeding 3 pages by September 1st 2016 with the subject line containing “Proposal for DaSAA Tutorial”. The proposal shall contain :
♣ Title
♣ Expected duration (90 minutes / 3 hours)
♣ Name, Affiliation, Mail Id of the speaker(s)
♣ Objectives
♣ Abstract
♣ Outline of the tutorial including its tentative schedule, not exceeding 1 page
♣ Target audience expected (Novice / Intermediate / Expert)
♣ Audio – visual requirements
♣ References including the presenters papers in the proposed topic
♣ Instructor’s experience in the proposed area
♣ Bio-data of all instructors of the tutorial
♣ Type of support materials to be supplied to attendees

The proposals will be evaluated by the programme committee considering the relevance, theme, expertise and experience in the subject.

Dr. S.D Madhu Kumar
Associate Professor
Department of Computer Science and Engineering
NIT Calicut


Dr. S.D Madhu Kumar is an Associate Professor in the Department of Computer Science and Engineering at NIT Calicut. He obtained his PhD in Computer Science from IIT Bombay. His research specialization includes Distributed Computing, Cloud Computing, Big Data & Cloud Database and software Engineering.

Topic :

Cloud Storage- Advances, Challenges and Opportunities


The primary use of cloud storage today is for storage of unstructured data, which is the fastest growing and most voluminous content, causing many administrative problems. Cloud storage option enables the users and organizations to store their data remotely and enjoy good quality applications on demand without having any burden associated with local hardware resources and software managements. But there are many issues associated with storing data in the cloud. Security of the data, Ensuring the correctness of the data, Fault tolerance, meeting the consistency requirements of the applications in a multi user environment, Performance, reliability and scalability are some of the challenging issues.

There are many techniques existing today, which in turn provide correctness of data in cloud, like Merkle Hash Tree (MHT), Distributed erasure-coded data and flexible distributed storage integrity auditing mechanism. Erasure coded storage scheme offers a promising future for cloud storage. Highlights of erasure coded storage systems are that these offers same level of fault tolerance as of replication at a lower storage footprint.

This tutorial will give a glimpse into the advances, technical challenges and research issues in the cloud storage sector. There will a special focus on erasure coded storage systems and simulation tools that can be used for testing the erasure coded storage systems for cloud.

Mr. Rahul Agrawal
Principal Machine Learning Manager

Topic :

Deep Learning and Applications to Language Understanding


Rahul Agrawal leads a team of engineers and scientists in Microsoft Bing Ads. His team is responsible for query intent understanding and matching it to advertiser's intent. His primary interest is in large scale machine learning algorithms, language understanding and deep learning architectures. Prior to Microsoft he was with Yahoo labs where he was responsible for click prediction for Yahoo display advertising.


In this tutorial, we will start with the basics of deep learning and study how it can be applied to various tasks in language understanding. We will look at how Deep Learning compares with respect to conventional bag-of-words representation. We will also look at how deep learning algorithms stack up when solving problems such as NER, segmentation, classification and document clustering. We will also look at various deep learning based architectures and how embedding play a role in intent representation.

Dr. V. Vaidehi
Dean, SCSE
VIT university, Chennai


Dr. V. Vaidehi received her BE (ECE) from College of Engineering, Guindy, ME (Applied Electronics) and Ph.D. in the area of Parallel Processing from Madras Institute of Technology, Anna University, Chromepet, Chennai. She was task team member in Micro Satellite (ANUSAT) and Executed several funded project in the area of Target Tracking, Multi-Sensor Fusion, Semantic Intrusion Detection System and Complex Event Processing. She has served as the Head of Computer Centre, Head of Electronics department, Head of Computer technology, Head of Information Technology and Director of AU-KBC Research Centre, MIT, Anna University, Chennai and Chairman of Faculty of Information and Communication Engineering, Anna University, Chennai. Currently she is Dean, School of Computer Science and Engineering, VIT University, Chennai. Her research interest includes Networking, Parallel and Distributed Processing, Adaptive Digital Signal Processing, Image and Video Processing, Network and System Security.

Topic :

Automated Pentesting for faster Security Assessment


Automating penetration testing can be used to test the insecurity of an application. It is conducted to find the security risk which might be present in the system. If a system is not secured, then any attacker can disrupt or take authorized access to that system. Security risk is normally an accidental error that occurs while developing and implementing the software. Penetration testing is essential because it can identify the simulation environment i.e., how an intruder may attack the system. It helps in finding the weak point of the system where attacker make use of the weak point to exploit the target.


The following paper ID's have been notified to submit the extended version of their work to IJBDI   
Paper ID's : 2, 9, 21, 37, 47, 57, 61, 62, 79, 81
Conference Proceeding will be published in Springer CCIS Series (Final approval pending)
Selected papers will be considered for publication in special issue Journal . See Call For Papers

Important Dates

Event Date
Paper Submission deadline 24th August, 2016
Final Camera-ready papers due 25th October, 2016
Early Registration 25th October, 2016
Late Registration 31st October, 2016
Tutorial Participation Registration 15th November, 2016
30th December 2016
Pre-Conference Tutorial 6th December, 2016
3rd January 2017

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