Scalable Detection of Crowd Motion Patterns

Studying the movements of crowds is important for understanding and predicting the behavior of large groups of people. When analyzing crowds, one is often interested in the long-term macro-level motions of the crowd as a whole, as opposed to the micro-level short-term movements of individuals. A high-level representation of these motions is thus desirable. In this work, we present a scalable method for detection of crowd motion patterns, i.e., spatial areas describing the dominant motions within crowds. For measuring crowd movements, we propose a fast, scalable, and low-cost method based on proximity graphs. For analyzing crowd movements, we utilize a three-stage pipeline: (1) represents the behavior of each person at each moment in time as a low-dimensional data point, (2) cluster these data points based on spatial relations, and (3) concatenate these clusters based on temporal relations. Experiments on synthetic datasets reveals our method can handle various scenarios including curved lanes and diverging flows. Evaluation on real-world datasets shows our method is able to extract useful motion patterns which could not be properly detected by existing methods. Overall, we see our work as an initial step towards rich pattern recognition.</p

LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms

In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms