- We are excited to announce that Jiawei Han (Professor, University of Illinois Urbana-Champaign) and Divesh Srivastava (Director of Databases Research, AT&T Labs Research) will be the keynote speakers!
- Submissions Open (Deadline 9th January (Abstract) and 16th January (Paper)): The submission portal is active now and we have started accepting papers. Please submit your exciting research work to NDA 2017!
- Our Program Committee has accepted three full and two short papers. See our workshop program!
- For Registration, please see the SIGMOD/PODS Conference and Workshop registration page. Online Registration Closes 7th April (Early) and 5th May (Late). Hurry Up!
Networks are prevalent in today’s electronic world in a wide variety of domains ranging from Engineering to Social Sciences, Life Sciences to Physical Sciences, Data Analytics and so on. Researchers and practitioners have studied networks in multiple ways like defining network metrics, providing theoretical results and examining problems like pattern mining, link prediction etc. Recently, we have witnessed proliferation of networks in new business domains like Telecommunications, Banking, Retail/Marketing, Healthcare, Transportation etc. Most of these real-world applications give rise to networks which exhibit unique and interesting structures supporting multiple dynamical processes that shape these networks over time. Owing to the tremendous pace of growth of electronic data, (1) Many of these networks are evolving at a rapid pace leading to evolving networks, and (2) Real-world graph data is seldom perfect; rather it is often of dubious quality, thereby being noisy.
Graphs are unarguably one of the most natural ways of representation for such data because of their ability to represent different entity and relationship types, including the temporal relationships necessary to represent the dynamics of a data stream. However, fusing such heterogeneous data into a single graph or multiple related graphs and mining is challenging task. Emerging massive data has made calls for fundamental change to graph data modelling and programming paradigm. APACHE SPARK is one such successful instantiation. Finally, it is interesting to see the applicability of graph based techniques by applying them to even wider range of data like spatial, spatio-temporal and IOT data which did not inherently exhibit network structure by modelling relationships.
This workshop is
a forum for exchanging ideas and methods for mining, querying and learning with
real-world networks, developing new common understandings of the problems at
hand, sharing of data sets where applicable, and leveraging existing knowledge
from different disciplines.
In summary, NDA 2017 is aimed at bringing together researchers from academia, industry and government with the following goals:
The workshop areas will be of interest to both theoreticians and practitioners who are interested in the development of novel data-management applications encircling large-scale analytics. More specifically, the intended audience are, but not limited to, computer scientists interested in databases and data mining, machine learning, data streaming, graph theory and algorithms.
Shourya Roy, Xerox Research Centre India
Arnab Bhattacharya, IIT Kanpur