Title: Graph Compression Using Quadtrees
 
Abstract: With the advent of Big Data era, faster and cheaper computing resources have gained tremendous significance across domains spanning both the industry and academia. The process of extracting information from huge data sets requires novel storage techniques to aid the computing devices to perform necessary computation. With pervasive use of heterogeneous systems and advent of non-traditional computing units like GPUs, with limited memory, it has become relevant to underline the relevance of data storage, especially to utilize such computing devices. Quadtrees, generally used to represent images, can be used as an effective technique to perform compression. Using additional topological information that depict certain patterns for the data sets, further improvements can be made to the space complexity of storing graph data. In this talk we discuss algorithms that take into consideration the properties of graphs, and perform compression based on quadtrees.
 
Short Bio: Dr. Amlan Chatterjee graduated from the University of OklahomA with a Ph.D. in Computer Science in December 2014. Prior to moving to Oklahoma, he completed his M.S. in Computer Science from the State University of New York at Buffalo, and Bachelor of Technology in Computer Science & Engineering from West Bengal University of Technology in India. Dr. Chatterjee's research interests are primarily in the areas of high-performance computing and big-data, with potential collaborative work across multiple disciplines. His current projects involve analyzing large graphs, specifically online social networks, using commodity multi-core hardware such as graphics processing units (GPU). Dr. Chatterjee is also a professional member of IEEE and ACM.