GRADES-NDA 2019: SIGMOD/PODS Workshop on Graph Data Management Experiences & Systems and Network Data Analytics SIGMOD/PODS 2019 Amsterdam, Netherlands, June 30, 2019 |
Conference website | https://sites.google.com/site/gradesnda2019/ |
Submission link | https://easychair.org/conferences/?conf=gradesnda2019 |
Poster | download |
Abstract registration deadline | March 11, 2019 |
Submission deadline | March 18, 2019 |
GRADES-NDA 2019
Collocated with SIGMOD-PODS 2019
Amsterdam, Netherlands
Sunday, June 30, 2019
Keynote Speakers:
Sourav Bhowmick, Nanyang Technological University
Tina Eliassi-Rad, Northeastern University
Bryan Thompson, Amazon Neptune
The focus of GRADES-NDA 2019 is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped data. The workshop is a forum for exchanging ideas and methods for mining, querying and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines. Additionally, considering specific techniques (e.g., algorithms, data/index structures) in the context of the systems that implement them, rather than describing them in isolation, GRADES-NDA aims to present technical contributions inside graph, RDF and other data management systems on graphs of a large size (many millions of nodes and beyond).
The goal of GRADES-NDA is to bring together researchers from academia, industry, and government, (1) to create a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards (2) addressing domain specific challenges or (3) handling noise in real-world graphs.
The workshop will be of interest to researchers in the development of novel data-management applications and systems for large-scale graph analytics. More specifically, the intended audience are, but not limited to, academic and industrial computer scientists interested in databases and data mining, machine learning, data streaming, graph theory and algorithms. Along with novel research work, we encourage submissions with demonstrations and case studies from real-life experiences in various domains such as Social Networks, Biological Network Data, Marketing and Media, Business Data Analysis, Healthcare Data, Cybersecurity etc.
Topics of interest include but are not limited to the following.
- Graph query languages, visualization techniques and querying interfaces, and their effective realization
- Graph platform and parallel platforms, e.g., Flink/Gelly, Titan, SPARK/GraphX, GraphLab/PowerGraph, Giraph, GraphChi etc.
- Network data representation, storage, indexing and querying methods.
- Experiences or techniques for graph specific operations such as traversals or inference/reasoning in the context of large data sets and on the systems that implement those operations.
- RDF data management and analytics
- Dynamic Graphs: managing graph updates; graph stream analytics; analyzing evolution and detection of community structures in real-world evolving graphs
- Mining and machine learning on heterogeneous networks -- knowledge graphs etc.
- Graph summarization and sampling
- Game Theory, Social contagion and Information propagation on networks
- Analytics on dirty, noisy, or uncertain graphs
- Spatial and temporal graph analytics
- Analytics on social, biological, retail, marketing, customer care, financial, healthcare, transportation network data sets
- Descriptions of graph data management use cases and query workloads, and experiences with applying data management technologies in such situations
- Vision and systems papers describing potential or real applications and benefits of graph management