分布式多数据流频繁伴随模式挖掘

多数据流频繁伴随模式是指一组对象较短时间内在同一个数据流上伴随出现，并在之后一段时间以同样方式出现在其他多个数据流上.现实生活中，城市交通监控系统中的伴随车辆发现、基于签到数据的伴随人群发现、基于社交网络数据中的高频伴随词组发现热点事件等应用都可以归结为多数据流频繁伴随模式发现问题.由于数据流规模巨大且到达速度快，基于单机的集中式挖掘算法受到硬件资源的限制难以及时发现海量数据流中出现的频繁伴随模式.为此，提出面向大规模数据流频繁伴随模式发现的分布式挖掘算法.该算法首先将每个数据流划分成若干个segment片段，然后构建适合部署在分布式计算平台上的多层挖掘模型，并利用多计算节点以并行方式对大规模数据流进行处理，从而实时发现频繁伴随模式.最后，在真实数据集上进行充分实验以验证算法性能.

Distributed Mining of Frequent Co-occurrence Patterns across Multiple Data Streams

A frequent co-occurrence pattern across multiple data streams refers to a set of objects occurring in one data stream within a short time span and this set of objects appear in multiple data streams in the same fashion within another user-specified time span. Some real applications, such as discovering groups of cars that travel together using the city surveillance system, finding the people that are hanging out together based on their check-in data, and mining the hot topics by discovering groups of frequent co-occurrence keywords from social network data, can be abstracted as this problem. Due to data streams always own tremendous volumes and high arrival rates, the existing algorithms being designed for a centralized setting cannot handle mining frequent co-occurrence patterns from the large scale of streaming data with the limited computing resources. To address this problem, FCP-DM, a distributed algorithm to mine frequent co-occurrence patterns from a large number of data streams, is proposed. This algorithm first divides the data streams into segments, and then constructs a multilevel mining model in the distributed environment. This model utilizes multiple computing nodes for detecting massive volumes of data streams in a parallel pattern to discover frequent co-occurrence patterns in real-time. Finally, extensive experiments are conducted to fully evaluate the performance of the proposal.