Data Warehouse Technology and Application in Data Centre Design for E-government

Data warehouse technology is an effective method to use the large amount data with complex correlation. We apply the data warehouse technology to CIPS; it plays a crucial role in process production. In this paper, the basic conception and realization method of data warehouse is summarized. On the basis of this summary, the meaning of building data warehouse in CIPS, the role and function of data warehouse, the method to build data warehouse in CIPS and the steps of building data warehouse, is addressed in detail.

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.

Data warehousing is an emerging technology that greatly extends the capabilities of relational databases specifically in the analysis of very large sets of time-oriented data. The emergence of data warehousing has been somewhat eclipsed over the past decade by the simultaneous emergence of Web technologies. However, Web technologies and data warehousing have some natural synergies that are not immediately obvious. First, Web technologies make data warehouse data more easily available to a much wider variety of users. Second, data warehouse technologies can be used to analyze traffic to a Web site in order to gain a much better understanding of the visitors to the Web site. It is this second synergy that is the focus of this article.

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.

Marine environmental information is the main decision-making basis on ocean development, marine environmental protection and marine management. With the rapid development of ocean science technology and requirement of ocean application, human beings are more relay on the marine environmental information, and the requirement for the management and service on marine environmental information becomes higher and higher. SOA (Service Oriented Architecture) encapsulate information in the shape of service, now is the one of the most concernful themes in the IT field, and is the trend for marine data information construction. This article recommends the concept and technology of data warehouse, SOA and Web Service, does deep research on the traditional data warehouse architecture and the features of current marine heterogeneous data informatization, does analysis on the status and existing problems of marine data warehouse, and proposed a information integration architecture strategy based on SOA and marine environmental data warehouse.

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making.

The concept of integrating technology and the application of the World Wide Web to bridge the gap between people and the government and the reduction of bureaucratic barriers has become a popular trend in the Middle East as many Arab nations have now developed portals that extend e-services to their users (Al-Nuaim, 2009). United Arab Emirates (UAE), in particular, has been participating in and developing models of E-government to facilitate its citizens with high quality service (Dutta et al., 2011). Previously known as e-government the name of the department that handled E-government initiatives in Dubai was changed to Dubai smart government starting from June 30, 2013. The new name was given by Shaikh Mohammad Bin Rashid Al Maktoum, the Vice-President and Prime Minister of the UAE and the Ruler of Dubai. The new name was consistent with the smart government initiative as it showed the government's outlook on the future (Gulf News, 2013). Instigation of E-government programs is also part of the same vision, E-government programs are aimed at increasing the competence and efficacy of government and governance system. The E-government programs aim to assist in policy making, governance and service delivery. One of the major postulates of the strategy of the UAE Government is to enhance and improve the delivery of public services and become more citizen centric. They aspire to meet international standards of service quality and service delivery to their citizens and the major focus of government is on improving standards of education, health-care, judicial system, innovation inculcation, and sustainability and government services. The research problem that this paper aims to address and explore is formed on the grounds that there is less usage of transformation E-government in UAE's public sector and there is still more room for improvement. The public sector must adopt and engage citizens through the use of e-services. Henceforth, the objective of this study shall be based on the investigation of barriers and problems responsible for the failure of the implementation of projects pertaining to Dubai E- government in UAE. Until such time that the barriers and hurdles have been identified and removed there would be no chance of achieving the full success in E-government implementation. As a result of this investigation, the study aspires to develop a guiding framework that addresses such barriers in future projects. The secondary literature and academic journals contain a vast amount of research on various barriers and CSF however unfortunately they are not specific to UAE or the GCC region, creating a vacuum in the knowledge base for UAE and other gulf countries. There is a need to understand that UAE, being a multicultural melting pot is different from other countries, while it has a strong Arab cultural rooted in its base at the same time it has adopted western practices of diversity and inclusion. The research aims to investigate the current situation of E-government in U.A.E and examine the barriers to successful implementation of E- government in U.A.E public sector with specific reference to Government employee users. To achieve the above aims, the following objectives have therefore been considered. i. To identify, examine and measure the extent and significance of barriers hindering the implementation of e-government in public sector in Dubai. ii. To develop a road map for successful implementation of e-government in UAE public sector.

With the development of distributed computing technology, there remain some important problems of collaborative control in distributed multi-user data sharing: heterogeneous environment, data consistency and concurrent operation. To solve the sharing issues in drilling engineering, this paper introduces a method of realizing information sharing by combining the advantages of multi-agent collaborative working mechanism and data warehouse technology. Firstly, the structure of multi-agent collaborative working mechanism is put forward based on distributed data warehouse, in which the underlying data layer adopts the distributed data warehouse to organize the information, and the model of that is set up by dimensional modeling technology. Then the information sharing platform and multi-user access controlling method are analyzed, and the collaborative model is put forward. Finally through the multi-agent interaction, the management and sharing of all kinds of information in DDW can be realized. So the application of multi-agent and DDW technology can maximize the value of drilling information and play an important role in improving data sharing degree. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4411

The term data warehouse was first used by Inmon in 1990 which he defined as “A warehouse is a subject oriented, integrated, time-invariant and nonvolatile collection of data in support of management decision making process” [Inmon, 1990]. On the other hand, Ralph Kimball provided a much simpler definition of the data warehouse. In the book ‘The Data Warehouse Toolkit’ he said that a data warehouse is “a copy of transaction data specifically structured for query and analysis” [Inmon and Kimball, 1996]. This paper provides theoretical description of data ware housing. Implementation of clinical Data warehousing were analyzed, finally evaluating them. The proto type considered for implementation has been collected from the East Hospital London Clinical Data Warehouse Project.

The framework of the assessment system for transformer condition is proposed using data warehouse, multi-agent system and data mining technologies synthetically. It is used to support the condition-based maintenance of transformer, improve the equipment reliability and reduce the substation operation cost. The health state of the transformer depends on the design, present and historical operation conditions of equipment. Usually, the off-line testing results, operation history and fault records of equipment, and weather conditions, etc. have been stored in different systems and they are very difficult to be utilized synthetically. Therefore, a data warehouse has been set up to integrate all of these data. We used data mining techniques to get the regulation and trend of the health condition of transformer and then make a conclusion. In order to implement the system efficiently and effectively, we employed some ideas of open agent architecture (OAA) to compose a multi-agent-based framework of the proposed system.

It is estimated that when the Human Genome Project's DNA sequencing phase reaches it peak steady-state, the production rate will be approximately 70 to 80 “new” genes each day, every day. This estimate is based on genomic sequencing and not expressed sequence tags (ESTs) or full-length cDNA sequencing. The challenge to research scientists is to identify these “genes” to some level of detail and determine which are of potential value, and to exploit that information as quickly as possible. This is a daunting task even with powerful computer and information systems; however, without a sophisticated informatics infrastructure it is impossible. The supporting information infrastructure can be broken into a number of components: data-acquisition systems (including inventory control and reagent manipulation systems, sequence production software, etc.); data-analysis systems (including sequence analysis software, structure prediction software, gene mapping software, feature extraction software, etc.); and data-management system (including local and shared databases). Databases form the core systems for the identification of “new” genome features and are the core of any informatics-based drug development strategy. The development and appropriate use of database collections, often referred to as data warehouses, is an essential process and computer science has provided tools for the extraction of knowledge from these database collections. In high throughput research environments, automated systems for processing new data through these data warehouses are essential tools for discovery research. Drug Dev. Res. 41:120–128, 1997. © 1997 Wiley-Liss, Inc.

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.

A data warehouse is a subject-oriented, integrated, relatively stable collection of data that reflects historical changes and is used to support management decisions. Common tools for building a data warehouse are IBM Cognos and SAP BO. However, both of the above use centralized single-node mode to build data warehouses. This type of data warehouse has poor scalability, and due to the rapid increase in the scale of the Internet, traditional data warehouses can no longer meet the actual needs of use. This paper mainly introduces the integration of cloud data warehouse and machine learning as well as the importance and application of parallel integration methods. First, the paper describes how the combination of cloud data warehousing and machine learning can promote business innovation and increase output. It then discusses the challenges of managing machine learning models in production environments, and introduces the role of cloud data warehouses in addressing these challenges. Subsequently, the cloud computing integration of Snowflake, as well as the implementation steps and processes of the parallel integration approach are also introduced in detail. Finally, the results of parallel integration method are analyzed, and it is considered that it has a good application prospect and development potential in cloud data warehouse.

This paper identifies and highlights the significance of Wasta as a barrier to e-government implementation within The Hashemite Kingdom of Jordan and is part of a wider qualitative research study of all barriers. A longitudinal research approach was applied to explore any dynamism within the presence of barriers over a three year study, as well as to seek a richer understanding of such barriers. Data, principally collected via interviews with relevant stakeholders, was analysed using Strauss and Corbin’s variant of grounded theory. Using illustrative quotations primarily from interview transcripts, this paper enunciates the significant and persistent role that Wasta plays in hindering Jordan’s e-government implementation, both as an explicitly mentioned barrier and as cause of other barriers. The paper supports the view that culture is a root cause of e-government implementation difficulty, and that barriers vary with the different country settings in which e-government systems are embedded.

Over the last few years, the area of electronic government (eGovernment) has received increased prominence and attention; people are increasingly interacting with e-Government systems. It is therefore important to measure the development of eGovernment. This study via principal component analysis (PCA) from the local perspective presents, validates and updates an evaluation model with 5 dimensions, which captures the multidimensional and interdependent nature of e-Government system. The validity of the model is empirically investigated using a sample of local e-Government of 18 cities in China, all of them with high Internet penetration and mature ICT use. The five dimensions of the evaluation model are project construction, information security management, special construction, transparency of government affairs and informationized ability. K-means clustering is applied in the subspace created by PCA to evaluate the local e-Government stages of growth of these 18 cities. The findings provide several important implications for e-Government research and practice.