A survey on algorithms for mining frequent itemsets over data streams

The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI mining problem hinders the application of the stream mining techniques. We recognize that a critical review of existing techniques is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the state-of-the-art algorithms on mining frequent itemsets over data streams. We classify the stream-mining techniques into two categories based on the window model that they adopt in order to provide insights into how and why the techniques are useful. Then, we further analyze the algorithms according to whether they are exact or approximate and, for approximate approaches, whether they are false-positive or false-negative. We also discuss various interesting issues, including the merits and limitations in existing research and substantive areas for future research.     

A computer program for estimating the re-transformed mean in heteroscedastic two-part models

The population of health care costs is typically skewed, heteroscedastic, and may include zero costs. Without proper accounting for these special distributional features, resulting prediction may be biased, and wrong inferences about the distribution of patients’ health care costs may be made. Welsh and Zhou [A.H. Welsh, X.H. Zhou, Estimating the retransformed mean in a heteroscedastic two-part model, J. Stat. Plan. Inference 136 (2006) 860–881] proposed a semi-parametric regression model, which addressed these special features. In this paper we developed a software program to implement this statistical method, which would provide better prediction of health care costs for clinical researchers.

Our program computed two mean estimators, their asymptotical standard deviation, confidence interval, and optional bootstrap confidence interval. Our program included user-friendly interactive mode and more efficient and flexible batch mode. It was written in free statistical computing language R and could be run on a wide variety of platforms.     

Single-machine scheduling with deteriorating jobs under a series–parallel graph constraint

This paper considers single-machine scheduling problems with deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting times. In addition, the jobs are related by a series–parallel graph. It is shown that for the general linear problem to minimize the makespan, polynomial algorithms exist. It is also shown that for the proportional linear problem of minimization of the total weighted completion time, polynomial algorithms exist, too.     

ER-TCP: an efficient TCP fault-tolerance scheme for cluster computing

This paper proposes a novel scheme, named ER-TCP, which transparently masks the failures happened on the server nodes of a cluster from clients at TCP connection granularity. In this scheme, TCP connections at the server side are actively and fully replicated to remain consistency so as to be transplanted over healthy parts during failure. A log mechanism is designed to cooperate with the replication to achieve small sacrifice on the performance of communication and makes the scheme scales beyond a few nodes, even when they have different processing capacities. We built a prototype system at a four-node cluster with ER-TCP, and conducted a series of experiments on that. The experimental result told us that ER-TCP has relatively small penalty on the communication performance, especially when it is used to synchronize multiple replicas. The results of real applications show that ER-TCP will incur small sacrifice on performance of web server at light load, and it can be used to distribute files very efficiently and reliably.

多参数变送器的工业以太网接口设计

以ATmega128单片机为多参数变送器研制工业以太网通信接口,使用了高集成度的以太网控制芯片来实现数据链路层和物理层的功能;利用TCP、UDP、PING、IP、ARP五个协议组成一个小型化的TCP/IP协议栈;在应用层选用了Modbus/TCP协议.在流量测试过程中,设计的上位机软件与通信接口配合实验,取得了较好的设计效果.对传统仪表和控制装置设计工业以太网接口以及现场设备的网络化,都具有一定的应用和推广价值.     

基于DSP2812的静止无功补偿控制器

针对电力系统中无功补偿装置发展的现状,介绍了一种基于DSP2812的静止无功补偿装置控制器的设计.该控制器充分利用了DSP2812内部资源丰富的特点,即响应速度快、动态性能好,系统结构简单、可靠性高、成本低.试验结果表明,该控制器具有优异的性能,可用于低压系统的动态无功补偿.     

基于数字图像处理的控制系统动态性能测量

针对目前位置控制伺服系统动态性能较难准确测量的问题,提出了一种基于激光和数字图像处理的测量思路,实现了对运动轨迹的数字化监测,并以某火炮检修中的位置控制伺服系统为例,详细介绍了系统组成、软件处理系统及相应的试验结果.这是开展位置控制伺服系统动态性能准确测量的有益尝试.     

异构机群系统上带返回信息的可分负载多轮调度算法

针对处理机具有不同的计算速度、通信能力的异构机群计算环境,以及实际应用中许多问题的求解在处理完任务后向中心处理机节点返回处理结果信息的情形,通过允许计算和通信操作重叠执行,采取FIFO调度策略和多次并行分配计算任务的方法,提出一种带返回结果信息的调度轮数可变的可分负载多轮调度算法.实验结果表明,该算法对于处理具有返回结果信息的应用的调度性能优于UMR可分负载多轮调度算法,并且可以获得近似最优的调度轮数.