Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …

Event processing (EP) is a data processing technology that conducts online processing of
event information. In this survey, we summarize the latest cutting-edge work done on EP …

Stream processors are emerging in industry as an apparatus that drives analytical but also
mission critical services handling the core of persistent application logic. Thus, apart from …

Many" big data" applications must act on data in real time. Running these applications at
ever-larger scales requires parallel platforms that automatically handle faults and stragglers …

We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets
programmers perform in-memory computations on large clusters in a fault-tolerant manner …

大数据管理:概念,技术与挑战 Page 1 大数据管理:概念,技术与挑战 孟小峰慈祥 (**人民大学信息
学院北京100872) Big Data Management: Concepts, Techniques and Challenges Meng …

We present Mesos, a platform for sharing commodity clusters between multiple diverse
cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster …

Many important “big data” applications need to process data arriving in real time. However,
current programming models for distributed stream processing are relatively low-level, often …

Kineograph is a distributed system that takes a stream of incoming data to construct a
continuously changing graph, which captures the relationships that exist in the data feed. As …

Many online data sets evolve over time as new entries are slowly added and existing entries
are deleted or modified. Taking advantage of this, systems for incremental bulk data …