Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

首先根据处理形式的不同, 介绍了不同形式数据的特征和各自的典型应用场景以及相应的代表性
处理系统, 总结了大数据处理系统的三大发展趋势; 随后, 对系统支撑下的大数据分析技术和应用 …

With the increasing interest in pedestrian detection, pedestrian datasets have also been the
subject of research in the past decades. However, most existing datasets focus on a color …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

Analyzing large graphs provides valuable insights for social networking and web companies
in content ranking and recommendations. While numerous graph processing systems have …

Growing main memory capacity has fueled the development of in-memory big data
management and processing. By eliminating disk I/O bottleneck, it is now possible to support …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

In this paper, we present GridGraph, a system for processing large-scale graphs on a single
machine. Grid-Graph breaks graphs into 1D-partitioned vertex chunks and 2D-partitioned …

Knowledge is indispensable to understanding. The ongoing information explosion highlights
the need to enable machines to better understand electronic text in human language. Much …