GDAM 2015 - International Workshop on Graph Data Analysis and Management

First International Workshop on Graph Data Analysis and Management (GDAM), to be held at Singapore on December 6, 2015, in association with IEEE/WIC/ACM Web Intelligence Conference.
Overview
Usage and generation of inter‐related data is on the rise. Be it Social Networks, Biological Networks, Internet of Things or Business analysis, there is a growing emphasis on relating and inferring information from different entities. Graphs are ideal structures for representing such connected data. Improvements in data generation and exchange over the web has caused a tremendous explosion in the amount of graph‐like data and has necessitated the need for sophisticated storage, analysis and mining techniques. With the scale of graph data available increasing ever faster, high performance distributed graph storage and analysis platforms are also being built. However, most graph problems are computationally intensive and the unique structure of graphs also produces distinctive challenges in designing efficient storage mechanisms. This workshop aims to provide an avenue to address both these issues.
Scope
The scope is novel and original ideas in developing high performance storage systems as well as efficient algorithms and mining techniques for big graph data. Interesting graph application experiences are also welcomed. Papers can be from any of the following topics including but not limited to:
Graph Data Management:
Graph representation, storage, indexing and querying methods
Distributed storage techniques
Graph query languages, visualization techniques and querying interfaces
Benchmarking RDF and/or graph database systems
Managing graph updates, evolving and heterogeneous graphs
Graph integration techniques with non‐graph systems
Graph data modelling
Graph Mining:
Graph summarization and sampling
Distributed storage techniques
Graph clustering, partitioning and classification methods
Frequent subgraph mining, graph pattern matching
Parallel graph processing techniques/architectures
Knowledge discovery from graphs
Measuring graph characteristics ? diameter, eigenvalues, triangle counting
Approximation techniques Graph search algorithms
Graph Mining:
Graph Applications: Use cases and case studies
Novel applications of big graph including but not limited to Social Networks, Web analysis and Mining, Business analysis, healthcare, security.