Data Mining Architecture System Expansion

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.

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.

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

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.

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 data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved.

Aiming at problem of "knowledge is flood in the data ocean", the method of knowledge acquisition based on data warehouse is studied in this paper. The structure of data warehouse is introduced firstly. Then the study pays attention on the designing and building a data warehouse. Data warehouse offers a good platform for OLAP and Data Mining. Some meaningful information can be acquired from data warehouse by OLAP or Data Mining, which will provide valuable knowledge to support business process.

In a short time, the data warehouse (DW) technology took an important place in the academic and industrial landscapes. This place materialized in the large majority of engineering and management schools that adopted it in their curriculum and in the small, medium-size and large companies that enhanced their decision making capabilities thanks to it. The 1990s saw the advent of conferences such as DaWaK and DOLAP that carried the acronyms DW and OLAP in their titles. Then, all of a sudden, this technology has been upset by the arrival of Big Data. Consequently, those actors have replaced DW and OLAP by Big Data Analytics. We are well placed to assert that this brutal move may have a negative impact on schools, academia, and industry. This technology is not dead, today’s context, with the connected world and Web of Data, is more favorable than when building DW merely stemmed from company internal sources. In this invited paper, we attempt to answer the following question: how does DW technology interact with Linked Open Data (LOD)? To answer the question, we provide a complete vision to augment the traditional DW with LOD, to capture and quantify the added value generated through this interaction. This vision covers the main steps of the DW life-cycle. This value is estimated through two different perspectives: (i) a source-oriented vision, by calculating the rate of the DW augmentation in terms of multidimensional concepts and instances, and (ii) a goal-oriented vision where the value is calculated according to the ability of the DW to estimate the performance levels of defined goals that reflect the strategy of a company, using the defined DW of the case study of a leading Algerian company.

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

As an analysis tool either of on-line analytical processing (OLAP) or data mining has its own strong points and weaknesses. It is an effective way to enhance the power and flexibility of data mining in data warehouses and large databases by integrating data mining with OLAP to offset their weaknesses. In the paper, a proposal of integrating data mining and OLAP is put forward. The mechanism of the on-line analytical data mining in data warehouses is described. Aspects which are necessary for developing a successful on-line analytical data mining system is also discussed.

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.

This article describes a data warehouse by comparing definitions. It examines the difference between a database and a data warehouse, and gives definitions of datamarts and data mining. The article goes on to look at methods for implementing a data warehouse, and problems that can be encountered during implementation. It discusses whether the data in a data warehouse are records, finally deciding that they are and that the records manager should have overall responsibility for the data.

There are ambiguities and vagueness in solar radiation records during a day. As a consequence, the development and adaptation of automatic knowledge acquisition techniques under uncertainty is entirely advisable. Among them fuzzy sets theory formulizes and analyses the situations in which the uncertainty is due to non-precise environment. Any good decision is based on information. Decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. From our findings, we conclude that data warehousing and data mining are essentials for an effective decision support system. Because organizations frequently process uncertain information, decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. Due to the potential benefits of a data warehouse as well as internal and external pressure for creating a competitive advantage, many organizations have launched data warehouse projects with the expectations of acquiring a consistent and reliable source of data for their DSS. Therefore, a DSS with data warehouse should improve the performance of users by improving information accessibility which positively affects the quality of decision making [1]. At present there is a great need to provide decision makers from middle management upward with information at the correct level of detail to support decision making. Data warehousing, on-line analytical processing (OLAP), and data mining provide this functionality [2]. Although there are researchers in fields of certainty, such as the natural science fields of physics, mathematics, biology, and in the social science fields of philosophy, economy, society, psychology, cognition, almost nobody places suspicion on the uncertain essence of the world. An increasing number of scientists believe that uncertainty is the charm of the world and only uncertainty itself is certain! For a long time, humans thought uncertainty was equal to randomness. Probability theory is the main mathematical tool to solve the problem of randomness. With more in-depth studies, people found a kind of uncertainties that could not be described with randomness. That was “fuzziness”. Fuzziness is a characteristic feature of modern science to describe quantitative relationships and space formation by using precise definitions and rigidly proven theorems, and to explore the laws of the objective world by using precisely controlled experimental methods, accurate measurements, and calculation so as to establish a rigorous theoretic system [3]. There are ambiguities and vagueness in solar radiation records during a day. There is a desired need for providing a simple technique whereby uncertainties in the process of solar radiation measurements being handled. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. The rest of the paper is organized as follows. The next section reviews related work. In Section 3 we will describe the characteristics of decision support systems. In Section 4 we demonstrate the basic data warehousing concepts. Section 5 provides the definition of a general framework for fuzzy sets in the presence of uncertainty. Section 6 introduces climate case study. In Section 7 we present our approach for solving this problem. Section 8 presents the results analysis. Section 9 shows International Journal for Information Security Research (IJISR), Volume 1, Issue 2, June 2011 Copyright © 2011, Infonomics Society 35

The article examines the influence of entropy on the digital transformation of the enterprise's business processes, which is an important topic in today's conditions of globalization and rapid technological development. The introduction of new digital technologies leads to an increase in uncertainty and chaos in organizational structures, which complicates the management of business processes. The authors noted that digitalization completely changes the way tasks are performed and the structure of processes at the enterprise, which in turn can lead to a change in the roles and responsibilities of employees. It is justified that any changes in the enterprise automatically create uncertainty and form the basis for resistance to changes. It has been proven that when an enterprise implements new technologies, such as: automation, artificial intelligence, or the Internet of Things (IoT), it faces the need to integrate relevant technologies into the already existing business processes of the enterprise, which can become de stimulators of the development of economic entities. The main focus is on the analysis of factors that contribute to the increase of entropy, such as the increase in the volume of information, inefficient data flows and the growth of cyber threats. The article also considers possible ways to reduce entropy, including reengineering business processes and using modern data management technologies. The authors emphasize the need for further research for a deeper understanding of the specific risks and opportunities associated with digital transformation, which will contribute to the successful development of enterprises in turbulent market conditions. Special attention is paid to the importance of entropy management to ensure the stability and competitiveness of enterprises. The authors emphasize the need for further research for a deeper understanding of the specific risks and opportunities associated with digital transformation, which will contribute to the successful development of enterprises in turbulent market conditions. The increase in entropy in the process of digitization is a natural consequence of the complexity and uncertainty associated with the introduction of new technologies and processes, so in order to successfully manage these challenges, enterprises must actively work on reducing the level of entropy through standardization, integration and clear communication of changes in the organizational structure.