频繁子图在监控系统数据挖掘中的应用

监控系统数据挖掘研究近年来受到了国内外学者的逐渐关注,监控系统数据挖掘是发现基于视频原始底层数据对应于应用层的语义信息关联及对应关系。视频底层数据特征十分复杂,沿用传统数据挖掘思想去理解和看待监控系统数据挖掘还不够。与一般的数据比较,图能够表达更加丰富的语义。把监控系统视频数据转化为图模型,将频繁子图挖掘算法应用到监控系统语义挖掘中,提出一种新的监控系统语义挖掘方法,与现有的方法相比较,该方法具有有效性和可行性。     

Bayesian life test planning for the Weibull distribution with given shape parameter

This paper describes Bayesian methods for life test planning with Type II censored data from a Weibull distribution, when the Weibull shape parameter is given. We use conjugate prior distributions and criteria based on estimating a quantile of interest of the lifetime distribution. One criterion is based on a precision factor for a credibility interval for a distribution quantile and the other is based on the length of the credibility interval. We provide simple closed form expressions for the relationship between the needed number of failures and the precision criteria. Examples are used to illustrate the results.Received: October 2002 / Revised: March 2004     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

旅游服务质量信息的挖掘分析

运用粗糙集方法对旅游服务质量信息进行分析。采用ROSETTA系统挖掘了旅游服务质量评价的一些关联规则,如旅游交通条件和导游服务水平是影响旅游服务质量的最重要因素;并根据游客对旅游服务质量的信息对游客进行聚类。分析表明,数据挖掘方法可以作为辅助旅游服务部门改进服务质量和进行决策的有力工具。     

我国地质资料管理发展趋势研究

我国地质资料管理经历了档案化、商品化和服务化管理三个时期。在我国地质工作发展新形势下,地质资料馆藏机构建设逐步加强,监管与服务日趋完善,管理工作制度趋于丰富。地质资料管理对策建议：⑴明确职责,完善地质资料汇交体制;⑵建立诚信体系,监管地质勘查工作质量;⑶加快地质资料信息化建设,建立全覆盖信息共享平台;⑷加快开发地质资料产品,提高资源利用效率。     

Identifying Discretionary Fiscal Policy Reactions with Real‐Time Data

We propose a method of identifying discretionary fiscal policy reactions using real‐time data. Automatic stabilizers should depend on true GDP, while discretionary fiscal policy is contingent on the information that policy makers have in real time. We can compute a real‐time measurement error by comparing the first release of GDP data with later revisions. Discretionary fiscal policy is influenced by this measurement error, whereas automatic fiscal policy is not. We use this identification approach to test the central identifying assumption of Blanchard and Perotti’s (2002) seminal structural vector autoregression (VAR). According to this assumption, fiscal policy makers do not react to GDP developments contemporaneously in a discretionary fashion. We find that government expenditure is adjusted upward if GDP growth in real time is lower than true GDP. This suggests that fiscal policy makers use short‐term funds to buy goods and services in response to their perception of GDP dynamics.     

Simple formulas for standard errors that cluster by both firm and time

When estimating finance panel regressions, it is common practice to adjust standard errors for correlation either across firms or across time. These procedures are valid only if the residuals are correlated either across time or across firms, but not across both. This paper shows that it is very easy to calculate standard errors that are robust to simultaneous correlation along two dimensions, such as firms and time. The covariance estimator is equal to the estimator that clusters by firm, plus the estimator that clusters by time, minus the usual heteroskedasticity-robust ordinary least squares (OLS) covariance matrix. Any statistical package with a clustering command can be used to easily calculate these standard errors.     

BUILDING AND EVOLVING DATA WAREHOUSING AND BUSINESS INTELLIGENCE ARTEFACTS: THE CASE OF SYSCO

This paper investigates use of data warehouse and business intelligence capabilities to integrate with customers in the supply chain and improve insights into customer sales. By making internal data warehouse sales information available to customers, additional value to those customers is created, eliminating asymmetries of information in the supply chain. In addition, the evolution of data warehousing into business intelligence is investigated, expanding sales information to include marketing associate performance analysis generated for internal use. Further, a methodology that was used for building a business intelligence system is also examined. Finally, what appears to be a business‐intelligence‐driven focus on enterprise resource planning systems is analyzed. These issues are illustrated using real‐world data warehousing and business intelligence artefacts developed at SYSCO. Copyright © 2012 John Wiley & Sons, Ltd.     

Foreign direct investment in Mexico Determinants and its effect on income inequality

By conducting panel data analysis across 32 federal entities in Mexico over the period 1994-2006, this paper studies the determinants of foreign direct investment (FDI) and its effect on income inequality within and between regions. We observe that the level of development and the size of the market have a direct relationship with FDI and therefore, they tend to increase the inequality gap between regions, but there is no evidence suggesting that it creates inequality within them.