About
Computational
methods to represent, model and analyze problems using social
information have come a long way in the last decade. Computational
methods, such as social network analysis, have provided exciting
insights into how social information can be utilized to better
understand social processes, and model the evolution of social systems
over time. We have also seen a rapid proliferation of sensor
technologies, such as smartphones and medical sensors, for collecting a
wide variety of social data, much of it in real time. Meanwhile, the
emergence of parallel architectures, in the form of
multi-core/many-core processors, and distributed platforms, such as
MapReduce, have provided new approaches for large-scale modeling and
simulation, and new tools for analysis. These two trends have
dramatically broadened the scope of computational social systems
research, and are enabling researchers to tackle new challenges. These
challenges include modeling of real world scenarios with dynamic and
real-time data, and formulating rigorous computational frameworks to
embed social and behavioral theories. The 2nd
IEEE Workshop on Parallel and Distributed Processing for Computational
Social Systems (ParSocial) provides a platform to bring together
interdisciplinary researchers from areas, such as computer science,
social sciences, applied mathematics and computer engineering, to
showcase innovative research in computational social systems that
leverage the emerging trends in parallel and distributed processing,
computational modeling, and high performance computing.
The papers selected for ParSocial will be published in the proceedings. Proceedings of the workshops are distributed at the conference and are submitted for inclusion in the IEEE Xplore Digital Library after the conference. |