The objective of the joint Industry track is to bring together the leading industry and academia practitioners to share their insights, expertise and experiences. The track will comprise of invited talks, technical talks (based on peer-reviewed submissions), and panel discussions by leading experts in the field of applied data science and data management technologies. The track will feature highly influential speakers who have developed and deployed successful data science and data management applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or social applications of data mining in areas such as education, transportation, real-estate, manufacturing, finance, retail, healthcare, e-commerce, telecommunications, social media, law, public policy, and computational advertising. Apart from offering technical talks and discussions, the track will offer an excellent opportunity for networking with leading names in Indian data science and data management domains.

We invite submissions of papers describing research and implementations of data mining/data analytics/big data/data science solutions and systems for practical tasks and practical settings. Technical topics of interest include but are not limited to:

Data Management Systems: Big-Data management; Data exchange and integration; Database monitoring and tuning; Data privacy and security; Data quality, cleaning and lineage; Data warehousing; Managing uncertain, imprecise and inconsistent information; parallel, distributed and cloud-based databases; Peer-to-peer data management; Personalized information systems; Columnar Storage/NoSQL databases; Distributed Storage Systems; New data management architectures (e.g., data stream management and cloud)

Data Mining: Novel data mining algorithms and foundations; Innovative applications of data mining; Data mining and KDD systems and frameworks; Mining data streams and sensor data; Mining multimedia, graph, spatio-temporal and semi-structured data; Security and privacy in data mining; High performance and parallel/distributed data mining; Mining tera-/peta-scale data; Visual data mining and data visualization; Big Data analytics

Data Science and Machine Learning: Large Scale Optimization; Statistical Relational Learning; Online Learning; Parallel/Distributed ML; Deep Learning and Neural Models; Semi-supervised Learning; Unsupervised Learning and Clustering; Learning with Noisy Data; Mining Linked Data; Approximation algorithms for ML; Knowledge-driven mining; Privacy and Security

Text Analytics and Information Retrieval: Categorization, clustering, and filtering; Document representation and content analysis; Information extraction and summarization; IR theory, platform, evaluation; Deep learning and word embedding models; Question answering and cross-language IR; Web and IR; Semantic Web/Ontologies

Applications: Social network analysis; web and social media analysis; text analytics; information retrieval and information extraction; recommender systems; online advertising; bioinformatics; systems biology; multimedia processing

Rich and Big Data: Mining linked data; mining sequences; time series analysis; mining temporal and spatial data; large scale analytics and optimization; online learning; parallel and distributed machine learning; novel statistical techniques for big data.

The application domains of interest include, but are not limited to education, transportation, real-estate, manufacturing, finance, retail, healthcare, e-commerce, telecommunications, law, public policy, government, or non-profit settings. Our primary emphasis is on papers that advance the understanding of, and show how to deal with, practical issues related to deploying analytics technologies. This track also highlights new research challenges motivated by analytics and data mining applications in the real world.

Submitted papers will go through a peer review process. The Industry Track is distinct from the Research Track in that submissions solve real-world problems and focus on systems that are deployed or are in the process of being deployed. Submissions must clearly identify one of the following three areas they fall into: "deployed", "discovery", or "emerging".

Submission Instructions:

The submissions should not exceed 2 pages of content (including references) and should be formatted in the standard ACM style available here. The submissions should be mailed to CODS-COMAD2017IndustryTrack@gmail.com.

All papers will go through the same reviewing process, and selected papers will be presented either as an oral talk or as a poster at the conference during the joint industry session. The accepted papers will be included in the conference proceedings. However, the authors of selected papers will also have the choice to opt their paper out of the conference proceedings. This will enable the paper presented at CoDS to be resubmitted at a different future venue. CODS 2017 proceedings will be published in ACM Digital Library.

Important Dates: