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.

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）