OLAP Council (1997) define Online Analytical Processing (OLAP) as a group of decision support system that facilitate fast, consistent and interactive access of information that has been reformulate, transformed and summarized from relational dataset mainly from data warehouse into Multi-Dimensional Databases (MDDB) which allow optimal data retrieval and for performing trend analysis. OLAP is an important concept for strategic database analysis. OLAP have the ability to analyze large amount of data for the extraction of valuable information. Analytical development can be of business, education or medical sectors. The technologies of data warehouse, OLAP, and analyzing tools support that ability. Online Analytical Processing (OLAP) enable discovering pattern and relationship contain in business activity by query tons of data from multiple database source systems at one time. Processing database information using OLAP required an OLAP server to organize and transformed and builds MDDB. MDDB are then separated by cubes for client OLAP Continue reading
Data Management Concepts
Text Mining Concept in Data Mining
Data mining is the process of extracting patterns from data. Data mining is becoming an increasingly important tool to transform the data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery. Data mining can be applied on a variety of data types. Data types include structured data (relational), multimedia data, free text, and hypertext. Nowadays, text is the most common and convenient way for information exchange. This due to the fact that much of the world’s data is contained in text documents (newspaper articles, emails, literature, web pages, etc.). The importance of this way has led many researchers to find out suitable methods to analyze natural language texts to extract the important and useful information. In comparison with data stored in structured format (databases), texts stored in documents is unstructured and to deal with such data, a Continue reading
Understanding Different Eras of Data Analytics
Back in the 1950’s and 1970’s, the role of information systems (IS) was to support operational functions within the organization. IS was primarily used in transaction applications, involving accounting transactions. The focus was on IS efficiency and IS effectiveness. With the advent of internet and as information technology (IT) advanced over time, the methods to assess the data and the amount that could be processed increased significantly. Data became more quickly accessible and could be processed at greater speeds. This assisted organizations and managers to report and run data more effectively. Information systems have helped organizations improve their decision making process and help managers reduce the risk involved. The way in which Data Analytics field has evolved in terms of capabilities and volume is of significance. Analytics has evolved and has been perceived differently across different periods in time or eras. ANALYTICS 1.0 – THE ERA OF BUSINESS INTELLIGENCE AND TRADITIONAL Continue reading
The Difference Between Traditional File Systems and DBMS
Traditional File Systems File-based systems were an early attempt to computerize the manual filing system. File-based system is a collection of application programs that perform services for the end-users, such as updating, insertion, deletion adding new files to database etc. Each program defines and manages its data. When a computer user wants to store data electronically they must do so by placing data in files. Files are stored in specific locations on the hard disk (directories). The user can create new files to place data in, delete a file that contains data, rename the file, etc which is known as file management; a function provided by the Operating System (OS). Database Management System The improvement of the File-Based System (FBS) was the Database Management System (DBMS) which came up in the 60’s. (DBMS) consists of software that operates databases, providing storage, access, security, backup and other facilities. This system can Continue reading
What is an Enterprise Database?
Data are the raw material from which information is produced. Therefore, it is not surprising that in today’s information-driven environment, data are a valuable asset that requires careful management. To access data’s monetary value, data that stored in company database are data about customers, suppliers, inventory, and operations and so on. Imagine that all the data in the database loss. What will happen if the situation like that happen? Data loss puts any company in a difficult position. The company might be unable to handle daily operation effectively; it might be faced with the loss of customers who require quick and efficient service, and it might lose the opportunity to gain new customers. Data are a valuable resource that can translate into information. If the information is accurate and timely, it is likely to trigger action that enhance the company’s competitive position and generate wealth. In effect, an organization is Continue reading
Data Warehouse Architecture – Concept and Models
According to William Inmon, data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of the management’s decision-making process. Data warehouse is a database containing data that usually represents the business history of an organization. This historical data is used for analysis that supports business decisions at many levels, from strategic planning to performance evaluation of a discrete organizational unit. It provides an effective integration of operational databases into an environment that enables strategic use of data. These technologies include relational and MDDB management systems, client/server architecture, meta-data modelling and repositories, graphical user interface and much more. The emergence of cross discipline domain such as knowledge management in finance, health and e-commerce have proved that vast amount of data need to be analyzed. The evolution of data in data warehouse can provide multiple dataset dimensions to solve various problems. Thus, critical decision making process of this dataset Continue reading