Knowledge acquisition process based on data warehouse

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.

Due to its historical nature, data warehouses require that large volumes of data need to be stored in their repositories. Some organizations are beginning to have problems to manage and analyze these huge volumes of data. This is due, in large part, to the relational databases which are the primary method of data storage in a data warehouse, and start underperforming, crumbling under the weight of the data stored. In opposition to these systems, arise the NoSQL databases that are associated with the storage of very large volumes of data inherent to the Big Data paradigm. Thus, this article focuses on the study of the feasibility and the implications of the adoption of a NoSQL database, within the data warehousing context. MongoDB was selected to represent the NoSQL systems in this investigation. In this paper will be explained the processes required to design the structure of a data warehouse and typically dimensional queries in the MongoDB system. The undertaken research culminates in the performance analysis of queries executed in a traditional data warehouse, based on the SQL Server system, and an equivalent data warehouse based on the MongoDB system.

Commercial OLAP systems usually consider OLAP dimensions as static entities. In practice, dimension updates are often necessary in order to adapt the multidimensional database to changing requirements. Query languages are then needed to retrieve historical information from a data warehouse. Although many proposals addressing temporal OLAP exist, none supports full operation over the World Wide Web. After introducing a temporal data model supporting historical dimensions and fact table versioning, we present a three-tier architecture based on web services, SOAP and XML, allowing efficient querying over the web. In this architecture, XML metadata is stored at the application server in the form of XML documents containing the data warehouse structure. This allows addressing most of the requests (navigation through time, updates and queries) without accessing the data warehouse. In the second part of the paper, we present TOLAP-QL, an SQL-like temporal query language. We describe the syntax and semantics of the language, a sequel of its logic-based predecessor, TOLAP. We study query optimisation in temporal OLAP, and introduce a technique based on materialisation of historical views. Last, but not least, we present a system implementation, and a real-world case study, that shows that our proposal achieves good query performance for OLAP queries over large historical databases in a web environment.

Five high-quality workshops were held at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) in Bangkok, Thailand during April 27-30, 2009. There were 17, 6, 9, 4 and 5 accepted papers to be presented at the Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2009), the workshop on Advances and Issues in Biomedical Data Mining (AIBDM 2009), the workshop on Data Mining with Imbalanced Classes and Error Cost (ICEC 2009), the workshop on Open Source in Data Mining (OSDM 2009), and the workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2009). One competition, PAKDD 2009 Data Mining Competition, and one local workshop, Thai Track Session, were arranged. From these workshops (except PAISI which published its works in separate LNCS proceedings), we selected two or three best papers for this LNCS publication. PAKDD is a major international conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scientific discovery,data visualization, causal induction and knowledge-based systems.

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.

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

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.

Traffic accidents are one of the major health problems that cause serious death in the world and ranks 9th in the world. Traffic accidents in Indonesia ranks 5th in the world. One effort to improve traffic safety is to design traffic accident prediction models. Prediction models will utilize accident-related data in traffic through data mining processing. The data warehouse offers benefits as a basis for data mining. Building an effective data warehouse requires knowledge and attention to key issues in database design, data acquisition and processing, as well as data access and security. This study is the first step in the development of data mining accidents based prediction system. The output of this initial stage is the design of data warehouses that can provide periodic and incidental data to the data mining process, especially in the prediction of accidents. The method used to design data warehouse is Entity Relationship Diagram (ERD).

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.

The main objective of conducting this research is to investigate empirically the role of data warehouse (DW) in enhancing decision-making or, more precisely, the researchers surveyed the top management of Jordanian firms regarding the role and importance of DW in improving the effectiveness of decision-making. It is believed and assumed that meaningful and significant information can be acquired from DW which provides valuable knowledge to support business process and decision-making. A mail questionnaire survey was regarded as the appropriate method for gathering data. The questionnaire was developed based on the findings from related literature and other related research questionnaires. All the firms (277), which were listed on Amman Stock Exchange (ASE), at the time of data collection, were selected over half answered positively. The researchers arrived at scores of significant and remarkable results regarding DW and its role in enhancing the process of decision-making. The survey's findings showed that the percentage of implementing DW in the Jordanian firms involved is 35%. In general, the respondents had a positive attitude towards the implementation of DW.

One key area involved in implementing the data warehouse is the transfer of information from the Source Databases (SDBs) into the data warehouse and making it available to the user. In looking at the overall intent of the data warehouse as a repository for vast amounts of diverse information, changes made to the SDBs, can be critical to the correctness of the query responses provided by the warehouse. The paper presents an alternative to the state of the art data warehouse environment by providing an auxiliary structure to accept these updates and store them until the data warehouse has available cycle time to incorporate the new data and update the necessary views. Using the Integrated Definition (IDEF) modeling process, we have created models of both the state of the art and alternative data warehouse structures to validate our design and performance characteristics.

Clinical databases have gathered a huge amount of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Thus it is a difficult task focusing to model a data warehouse, very often, into internal structures and implementation applications. The aim of is project is to find the parameters of medical data warehouse which will be able to construct a flexible framework by analyze the approaches of data warehouse architectures and compare them regarding the cost and integrity. The proposed parameters may assist in achieving continual access to the common data warehouse. The important consideration, however, is that the clinical data record should contain not only longitudinal health summary information but also be used for business intelligence tool such as data mining and OLAP

Increasingly, large organizations are engaging in data warehousing projects in order to achieve a competitive advantage through the exploration of the information as contained therein. It is therefore paramount to ensure that the data warehouse includes high quality data. However, practitioners agree that the improvement of the quality of data in an organization is a daunting task. This is especially evident in data warehousing projects, which are often initiated “after the fact”. The slightest suspicion of poor quality data often hinders managers from reaching decisions, when they waste hours in discussions to determine what portion of the data should be trusted. Augmenting data warehousing with data mining methods offers a mechanism to explore these vast repositories, enabling decision makers to assess the quality of their data and to unlock a wealth of new knowledge. These methods can be effectively used with inconsistent, noisy and incomplete data that are commonplace in data warehouses.

Base on China's institutions of higher learning to use educational materials on Data Warehouse and Data Mining which edited and published in China, and Some examples on the different content from the educational materials are listed. Then through the analysis classroom-teaching base on educational materials, we think it is highly necessary to adjust the proportion between teaching theoretical courses and experiment courses of undergraduate education of Data Warehouse and Data Mining in China. That is, adding the proportion of experimental classes is an absolute essential. It will be beneficial to brush up to improve undergraduate student's abilities of solving problems with software and to improve our teaching quality. Therefore, the reasons of experimental classes can't arouse our adequate appreciate be point out. That is: (1) There are certain limitation in the programming and actualization of the Data Warehouse technology in Chinese enterprises; (2) The authors of educational materials lack practical experience from Data Warehouse project. Finally, base on the objective of training for applied talents, the new educational materials related to architecture and training scheme are given.

The objective of this investigation is to (1) integrate remote sensing data into an existing data warehouse of the US crop insurance program 1990 to 2007, (2) test remote sensing correlations with crop production, and (3) use remotely sensed time series data to assess variation in crop production. Previously (2000 to 2007) data mining of the data warehouse was based upon probabilistic and algorithmic approaches to identification of possible fraud, waste, or abuse. The value of adding satellite data warehouse to data mining resources is provision of (1) an additional empirical metric, (2) objective data on vegetative health, (3) measurable metrics for capturing the variance of plant health, and (4) a means for measuring the covariance of location and production. Refinement of data mining through the addition of satellite data for routine use to reduce fraud, waste, and abuse will ultimately diminish the frequency of false positives.