Large Scale Graph Matching(LSGM): Techniques, Tools, Applications and Challenges

Large Scale Graph Matching(LSGM): Techniques, Tools, Applications and Challenges

Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precis...

Tools and Applications for Large - Scale Display

storage density, and network bandwidth have improved by more than three orders of magnitude. In this same time period, however, display resolution has merely doubled. The increased processor and storage capacities have enabled the computational sciences, but have simultaneously unleashed a data avalanche on the scientific community. As a result, scientific research is limited by data analysis a...

Efficient simulation techniques for large-scale applications

Methodology, Tools, and Techniques to Parallelize Large-Scale Applications: A Case Study

Multi-core processors are now mainstream, while manycore architectures are arriving. Yet getting generalpurpose software ready to take full advantage of the available hardware parallelism remains a challenge. There are, in fact, very few success stories of semi-automatic parallelization of large-scale integer applications outside the high-performance computing (HPC) and transaction processing d...

Study of Challenges and Techniques in Large Scale Matching

Matching Techniques are becoming very attractive research topic. With the development and the use of a large variety of data (e.g. DB schemas, ontologies, taxonomies), in many domains (e.g. libraries, life science, etc), Matching Techniques are called to overcome the challenge of aligning and reconciling theses different interrelated representations. In this paper, we are interested in studying...