Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining

A conditional role-involved purpose-based access control model

This paper presents a role-involved conditional purpose-based access control (RCPBAC) model, where a purpose is defined as the intension of data accesses or usages. RCPBAC allows users using some data for certain purpose with conditions. The structure of RCPBAC model is defined and investigated. An algorithm is developed to achieve the compliance computation between access purposes (related to data access) and intended purposes (related to data objects) and is illustrated with role-based access control (RBAC) to support RCPBAC. According to this model, more information from data providers can be extracted while at the same time assuring privacy that maximizes the usability of consumers' data. It extends traditional access control models to a further coverage of privacy preserving in data mining environment as RBAC is one of the most popular approach towards access control to achieve database security and available in database management systems. The structure helps enterprises to circulate clear privacy promise, to collect and manage user preferences and consent

Challenging Issues of Spatio-Temporal Data Mining

The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining. Keywords: database, data mining, spatial, temporal, spatio-tempora

Data mining in medical records for the enhancement of strategic decisions: a case study

The impact and popularity of competition concept has been increasing in the last decades and this concept has escalated the importance of giving right decision for organizations. Decision makers have encountered the fact of using proper scientific methods instead of using intuitive and emotional choices in decision making process. In this context, many decision support models and relevant systems are still being developed in order to assist the strategic management mechanisms. There is also a critical need for automated approaches for effective and efficient utilization of massive amount of data to support corporate and individuals in strategic planning and decision-making. Data mining techniques have been used to uncover hidden patterns and relations, to summarize the data in novel ways that are both understandable and useful to the executives and also to predict future trends and behaviors in business. There has been a large body of research and practice focusing on different data mining techniques and methodologies. In this study, a large volume of record set extracted from an outpatient clinic’s medical database is used to apply data mining techniques. In the first phase of the study, the raw data in the record set are collected, preprocessed, cleaned up and eventually transformed into a suitable format for data mining. In the second phase, some of the association rule algorithms are applied to the data set in order to uncover rules for quantifying the relationship between some of the attributes in the medical records. The results are observed and comparative analysis of the observed results among different association algorithms is made. The results showed us that some critical and reasonable relations exist in the outpatient clinic operations of the hospital which could aid the hospital management to change and improve their managerial strategies regarding the quality of services given to outpatients.Decision Making, Medical Records, Data Mining, Association Rules, Outpatient Clinic.

The Sloan Digital Sky Survey Science Archive: Migrating a Multi-Terabyte Astronomical Archive from Object to Relational DBMS

The Sloan Digital Sky Survey Science Archive is the first in a series of multi-Terabyte digital archives in Astronomy and other data-intensive sciences. To facilitate data mining in the SDSS archive, we adapted a commercial database engine and built specialized tools on top of it. Originally we chose an object-oriented database management system due to its data organization capabilities, platform independence, query performance and conceptual fit to the data. However, after using the object database for the first couple of years of the project, it soon began to fall short in terms of its query support and data mining performance. This was as much due to the inability of the database vendor to respond our demands for features and bug fixes as it was due to their failure to keep up with the rapid improvements in hardware performance, particularly faster RAID disk systems. In the end, we were forced to abandon the object database and migrate our data to a relational database. We describe below the technical issues that we faced with the object database and how and why we migrated to relational technology

Data Mining Technology Used in an Internet of Things-Based Decision Support System for Information Processing Intelligent Manufacturing

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance

Recent Developments in Data Warehousing

Data warehousing is a strategic business and IT initiative in many organizations today. Data warehouses can be developed in two alternative ways -- the data mart and the enterprise-wide data warehouse strategies -- and each has advantages and disadvantages. To create a data warehouse, data must be extracted from source systems, transformed, and loaded to an appropriate data store. Depending on the business requirements, either relational or multidimensional database technology can be used for the data stores. To provide a multidimensional view of the data using a relational database, a star schema data model is used. Online analytical processing can be performed on both kinds of database technology. Metadata about the data in the warehouse is important for IT and end users. A variety of data access tools and applications can be used with a data warehouse - SQL queries, management reporting systems, managed query environments, DSS/EIS, enterprise intelligence portals, data mining, and customer relationship management. A data warehouse can be used to support a variety of users - executive, managers, analysts, operational personnel, customers, and suppliers. Data warehousing concepts are brought to life through a case study of Harrah\u27s Entertainment, a firm that became a leader in the gaming industry with its CRM business strategy supported by data warehousing