Application of a mixed DEA model to evaluate relative efficiency validity

Data envelopment analysis(DEA) model is widely used to evaluate the relative efficiency of producers. It is a kind of objective decision method with multiple indexes. However, the two basic models frequently used at present, the C2R model and the C2GS2 model have limitations when used alone,resulting in evaluations that are often unsatisfactory. In order to solve this problem, a mixed DEA model is built and is used to evaluate the validity of the business efficiency of listed companies. An explanation of how to use this mixed DEA model is offered and its feasibility is verified.

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

Layered Software Patterns for Data Analysis in Big Data Environment

The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.

Efficiency Pattern and Spatial Strategy of Ports in Yangtze River Delta Region

This paper measures the efficiency of ports in the Yangtze River Delta Region(YRDR) in 2008 and 2013 using port berth quantity, quay length, and human resources as input indicators, using cargo and container throughput as output indicators, and considering traditional(foreign trade dependence and industrialization level) and modern environmental factors(traffic line density, financial development level, and informatization level). To achieve such aim, this study constructs a multi-stage data envelopment analysis model(DEA) that identifies effective port decision-making units(DMUs) and generates a highly accurate conclusion by eliminating the interference from the exogenous environment and random errors. First, the external environment significantly affects port efficiency, with the traditional environmental factors showing huge fluctuations and the modern environmental factors producing great benefits. Second, the efficiency of ports in YRDR has increased from 2008 to 2013 primarily because of their pure technical efficiency. Third, the weighted standard deviation ellipse(SDE) analysis results reveal that the efficiency pattern of ports significantly deviates from their throughput pattern, while the center of SDE of port efficiency moves from the eastern coastal regions to the northwest regions. Based on these findings, this paper proposes spatial development strategies for YRDR, such as creating an unblocked environment where spatial elements can freely circulate, intensifying port-city joint development, implementing differentiated policies, and focusing on the spatial collaboration of port efficiency.

Dynamic single-index model for functional data

We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.

Functional singular component analysis based functional additive models

We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problems of the singular components of the predictor process and response process. Consistency of estimators for the nonparametric function and prediction are proved, respectively. A simulation study is conducted to investigate the finite sample performances of the proposed estimators.

Analyzing Electricity Consumption via Data Mining

This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified successfully through five classi-fiers using the selected feature subset,and the best model can be determined further.The effects on analyzing electricity consump-tion of the other three attributes,including months,businesses,and meters,can be estimated using the chosen model.The data used for the project is provided by Beijing Power Supply Bureau.We use WEKA as the machine learning tool.The models we built are promising for electricity scheduling and power theft detection.

ndustrial eco-efficiency and its spatial-temporal differentiation in China

The aim of this paper is to study the spatialtemporal differentiation of industrial eco-efficiency in China. Using methods based on the data envelopment analysis （DEA） model and exploratory spatial data analysis （ESDA） and data from 1985, 1995, 2005, and 2008 of 30 provinces in China, the spatial-temporal pattern changes in industrial eco-efficiency are discussed. The results show that： first, the patterns of industrial eco-efficiency are dominated by clustering of relatively low efficiency provinces; second, spatial relationships between the industrial eco-efficiencies of different provinces changed slightly throughout the period and the provinces persistently exhibit spatial concentration of relatively low industrial eco-efficiency; finally, there is an obvious trend in the polarization of industrial eco-efficiency, i.e., the higher level spatial units are concentrated in eastern China, and the lower level spatial units are mainly in western and central China. （ESDA）

A Study on Recombination Efficiency of Commercial Bank Branch Based on DEA Approach

For commercial bank branch, improving efficiency is very important for enhancement of overall efficiency and appraising efficiency is also an urgent problem to resolve. The paper compared the efficiency of recombining branch with the efficiency of no recombining branch of 23 commercial banks by using the approach of output-max CCR model of Data Envelopment Analysis （DEA）. According to the empirical results of the study, we can make some conclusions. Firstly, bank branch recombination is based on the comprehensive raising of human sources, pecuniary resources and material resources, and can be inefficient under the immature condition. Secondly, the efficiency of commercial banks can be improved enormously because their inner management and resource allocation in recombining are wasted largely. Thirdly, nowadays, commercial banks should emphasize on recomposing resources, mending technical ＆ managerial innovation and improving management level generally.