Supporting information system for power transformer fault forecasting applications

Power transformers' failures carry great costs to electric companies since they need resources to recover from them and to perform periodical maintenance. To avoid this problem in four working 40 MVA transformers, the authors have implemented the measurement system of a failure prediction tool that is the basis of a predictive maintenance infrastructure. The prediction models obtain their inputs from sensors, whose values must be previously conditioned, sampled and filtered, since the forecasting algorithm need clean data to work properly. Applying data warehouse techniques, the models have been provided with an abstraction of sensors the authors have called virtual cards (VC). By means of these virtual devices, models have access to clean data, both fresh and historic, from the set of sensors they need. Besides, several characteristics of the data flow coming from the VCs, such as the sample rate or the set of sensors itself, can be dynamically reconfigured. A replication scheme was implemented to allow the distribution of demanding processing tasks and the remote management of the prediction applications. Virtual cards and the modular architecture proposed make the system versatile and scalable, respectively. The system is currently working in four power distribution plants of a Spanish electric company.

As an efficient data management and analysis technology, the data warehouse realizes centralized storage and management of data by integrating data scattered in educational institutions. It can dig out the laws and trends hidden behind the data and provide more accurate and comprehensive data support for educational evaluation. In the educational evaluation system, the application of data warehouses is embodied in data integration and management, multidimensional evaluation, personalized learning support, and educational decision optimization. Through a multidimensional evaluation model, the school can comprehensively evaluate students' academic performance and comprehensive quality and provide strong support for personalized teaching. Data warehouses can also record and analyze students' learning trajectory and provide customized learning materials and exercises for each student, which significantly improves learning efficiency.

Data warehousing is emerging as one of the hottest areas of growth in global business and the critical component and tool for business intelligence. In the data warehouse environment, the level of summarization plays an important role. Some Data Warehouse applications, directly account for the uncertainty and vagueness often associated with time-series and enterprise-wide decision making. In this paper, we enhance the concept of summarization using fuzzy technology, keeping in view the time-series and enterprise-wide data. This enhancement, which extends the aggregation process used in data warehouse, provides insightful information intelligently and allows greater facility for modeling human decision making.

How to build a data warehouse and data mining is constantly worth exploring and optimization, not only technically, in commercial applications as well. This article discusses the introduction of new technologies and concepts, the traditional method of data warehouse technology has changed dramatically, based on data warehouse applications are a new development. Each enterprise data warehouse based on the characteristics of different companies, you can use a very flexible method of selection and design selection, implementation. According to some relatively new technical features, talk about data warehousing and data mining architecture.

The Small&Medium steel enterprises is relatively weak on information technology, and the information management personnel lack the urgent need for information integration. According to the actual needs of the Small&Medium steel enterprises information management, three-tier architecture of information integration systems is proposed in this paper. By using SQL Server to implement database and data warehouse, data mining models is built and the design of information integration system is completed. The system covers the steel purchase, inventory, sale, distribution, contracts, finance, statistical analysis, decision support and other management functions. The proposed system can meet the Small&Medium steel enterprises scientific and orderly management for information resources, and the needs of managers forecasting, decision-making. Data warehouse and data mining applications can give an excellent support to the implementation of business intelligence in the future.

Through building a data warehouse which is used to support manufacturing inventory analysis, according to the analysis theme, this paper extracted, purified and integrated the historical data which is accumulated by the enterprise in a long term and distributed in the various heterogeneous systems, and store them in the data warehouse fact table; through the establishment of the data cube, it generate the aggregated data with different particle size, as well as provide on-line analytical processing technology (OLAP), to provide users data query analysis with multi-angle, multi-granularity, visualization, which can solve many problems difficult to solve in the inventory analysis in traditional enterprise information processing systems.

This paper discusses the concepts of data warehouse technology and its importance for decision support system (DSS). The DSS as one kind smart software system of the important application value, provides, each kind of decision information as well as the commercial question solution for the enterprise. Thus Data warehouse can meet the requirements of the database management subsystems of DSS, and is fitted to form its technology frame. Then we put forth the structure of data warehouse and its main functional components. Along with widespread application of the data warehouse, DSS based on data warehouse arises at the historic moment. Begin with actual demand of the DSS, characteristic and the structure of the data warehouse have been analyzed. DSS based on the data warehouse technology is elaborated, and finally give the application example.

Currently, the shipping company's statistical data on cargo is usually obtained through the complex operation of the business database. The statistical items are also very limited. To improve the competitiveness of shipping company and optimize its ship scheduling business, this paper mainly researches on technology and application of data warehouse in oceangoing ship scheduling business. According to the customer's actual and objective needs, the ship scheduling data warehouse is designed. Based on Microsoft SQL Server 2008 platform and ship scheduling data of a shipping company, the ship scheduling data warehouse for cargo loading/discharging analysis in global ports is established in this paper, which can offer data deeply analysis and multidimensional display.

The purpose of this research is to assist in providing information to support decision-making processes in sales, purchasing and inventory control at PT Tatamas Pelita Jaya. With the support of data warehouse, business leaders can be more helpful in making decisions more quickly and precisely. Research methodology includes analysis of current systems, library research, designing a data warehousing schema using bintang. The result of this research is the availability of a data warehouse that can generate information quickly and precisely, thus helping the company in making decisions. The conclusion of this research is the application of data warehouse can be a media aide related parties on PT Tatamas Pelita Jaya in decision making.

E-government allows governments to service citizens in a more timely, effective, and costefficient method. E-Government services focus on four main customers: citizens, the business community, government employees, and government agencies. E-government can provide four types of services, which are Government-to-Citizen (G2C), Government-toBusiness (G2B), Government-to-Employee (G2E), and Government-to-Government (G2G). E-government system has already been very popular all over the world, because Egovernment helps to disseminate information. Further, it aids in the collection of information that helps decision makers serve citizens more effectively. E-Government allows government agencies to centralize decision making. China have started first E-Government program in the late 1980s, in which the governments both at central and local levels built up office automation (OA) systems and established an intranet, subsequently the Central Government of China had formally launched five Golden Projects (Golden Bridge Project, Golden Customs Project, Golden Card Project, Golden Tax Project and Government online Project) aimed at building E-Government in China ever since 1990s. After realizing five Golden Projects, Chinese government has set ambitious visions in the implementation of E-Government: quicken the pace of change in government functions to suit the requirement of reform, opening up and modernization policies, improve the performance of government operation, introduce new government measures in a scientific manner and more effective mechanisms to monitor the economic activities, place a greater emphasis on central co-ordination and transparency of government work, carry out administrative functions in accordance with law and provide better service for the public. To meet above ambitious visions, the Central Government of China have programmed to establish four governance information resource databases in next five years, governance information resource databases consist of four databases: population basic information database, judicial entity basic information database, natural resource and geography basic information database, and macroscopically economy database. A data warehouse has been defined as a collection of data in support of management decisions which is: subject oriented, integrated, nonvolatile, time variant. Data warehouse, as a collection of database or data management technologies, emerged in the early 1990s. The data warehouse has now been more generally seen as a strategy to bring heterogeneous 19

Intelligent vehicle monitoring system is collecting and processing the large amounts of information all the time. The paper applies data warehouse technique to intelligent vehicle monitoring system to store and manage the information. Based on the analysis of the subject field in the system and the design of dimension table, the system logical model and physical data model, the data warehouse of system is built. And, the data in the data warehouse are analyzed and summarized by the data analysis and display tools. Decision-makers can clearly and intuitively see the result, and this can offer the decision foundation for development of China's transportation.

The emergence of electronic commerce has changed the traditional business model, it has changed vendors and customers. Expanded customer choice makes them pay more attention to the value of goods, rather than before the first brand and geographical factors into account. Therefore, in terms of the seller, as much as possible about their customers, and conducting business activities for customers efficiently and easily provide the necessary goods and services in order to remain invincible in the competition. This general framework of the system is designed to complete a computer-based support of CRM systems. The system is versatile, practical, easy to use, easy expansion, high maintainability, security, performance, and low cost advantages, the use of this system will help enterprises to standardize and improve their own management system and improve management efficiency.

First of all, the necessities and advantages of applying Web and data warehouse to MDSS was analyzed and the multi-agent systems theory was introduced. Further, the business schemas in the data warehouse were identified and the multi-dimension datasets were established. The structure of the Web-enabled MDSS was designed based on multi-agent systems and each agent's function was given. Finally, the developing tools and the implementation of the Web-enabled data warehouse were discussed.

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. Much of the interest and success in this area can be attributed to the need for software and tools to improve data management and analysis given the large amounts of information that are being accumulated in corporate as well as scientific databases.However, even though the high maturity of these technologies, new data needs or applications currently run at companies not only demand more capacity, but also new methods, models, techniques or architectures to satisfy these new needs. Some of the hot topics in data warehouses (DWs) include distributed DWs, advanced OLAP for business intelligence, web warehouses, DWs for new applications such as XML documents, stream data, spatial or GIS data or biomedical data. Moreover, there are other aspects very developed in other software areas such as security or quality, which still remain uncovered by current design methods or technologies for DWs.Like the previous successful DOLAP workshops held in conjunction with CIKMs, the eighth edition of the Workshop on Data Warehousing and OLAP (DOLAP'05) aims to synergistically connect the research community and industry practitioners. It provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, and practical experiences. This year, DOLAP'05 will be specially focused on new research directions and emerging application domains in the areas of data warehousing and OLAP.This year, we received papers from 18 different countries distributed over all continents such as The Netherlands, France, Spain, Israel, Korea, USA, Canada and Argentine. We received 31 submissions and, after a careful review, only 12 papers were selected by the Program Committee, making an acceptance rate of 38.7%.These proceedings contain the papers selected for presentation at the workshop. The accepted papers were presented in 5 sessions: (i) querying OLAP databases, (ii) data warehouse models, (iii, iv) data warehouse design, and (v) query processing and view maintenance. A keynote address was given by Jens Lechtenburger on Schema transformations. We hope that these proceedings will serve as a valuable reference for data warehousing and OLAP researchers and practitioners.

The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger’s ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in “RAWAT” NTDC grid station, Islamabad, and “UCH-II” power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer.