Due to a number of requests received, the submission deadline for industry track had been extended till August 22, 2018
(Previously August 15th, 2018). There will be no further extensions.
CoDS-COMAD 2019 will include an Industrial Track session which will cover the application of recent research advances to problems of real-world interest. The industry track of the conference aims to bring together leading industry and academia practitioners to share their insights, expertise and experiences related to deployments of solutions in industry and government that address real-world challenges and highlight new and important research directions.
Application domains of interest include, but are not limited to, education, transportation, energy, real-estate, manufacturing, finance, retail, healthcare, e-commerce, digital marketing, telecommunications, social media and computational advertising. Submissions are invited on work that highlight new challenges arising from the deployment of Data Science and Database technologies.
Submitted papers should present the problem, its significance to the application domain, design choices made towards the solution, the deployment challenges, and the lessons learned. Submissions to the general Research Track will be given the option of being considered as an Industry Track candidate. At least one of the authors needs to have a non-academic affiliation. Authors of accepted papers will have the opportunity to present their work in an oral session. Authors are encouraged to contact the track chairs (Nanda Kambhatla and Vishwa Vinay) if they need clarification regarding the fit of their work to the track.
The submissions are limited to a total of 6 pages, plus up to 1 additional page for references. The papers should be formatted in the standard ACM style available at the ACM Master Article Template. The submissions should be made via Easychair. The deadline for submissions is 15th August 2018, and the notificaations will be sent out by 15th November 2018.
Representation Learning for E-commerce
Design and Analysis of High Performance Convolutional Neural Network Text Classifier and its Discriminative Sequence Learning Capability
All papers are to be submitted using Easy Chair at CODS-COMAD 2019 Easy Chair
22nd Aug, 2018
15th Aug, 2018
15th Oct, 2018
1st Nov, 2018
, Symphony AI
, Adobe Research