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An Introduction to Business Intelligence
Published in Deepmala Singh, Anurag Singh, Amizan Omar, S.B. Goyal, Business Intelligence and Human Resource Management, 2023
It is an important component for the success of the modern organization that can take advantage of all available information (Cody et al., 2002). OLAP refers to the techniques of performing complex analysis over the information stored in a data warehouse to transfer it into decision information. It is a computing method that allows users to sort, select, and analyze data for strategic decisions. It has become a vital tool for analysts, managers, and executives for analyzing and extracting various interesting patterns from a large volume of stored data (Techapichetvanich & Datta, 2005). It helps in financial reporting, market forecasting, budgeting, trend analysis, and other planning purposes. OLAP systems can be categorized into Multidimensional OLAP, Relational OLAP, and Hybrid OLAP. OLAP process starts with the accumulation of data from various sources and these accumulated data are then collected in a data warehouse. The data stored in the warehouse are then cleansed and are accumulated in OLAP Cubes from which users can generate queries.
Business Intelligence, Big Data and Data Governance
Published in Pedro Novo Melo, Carolina Machado, Business Intelligence and Analytics in Small and Medium Enterprises, 2019
Hélder Quintela, Davide Carneiro, Luís Ferreira
The next step is to analyze the data under the light of the specific domain of the organization, generating information. The main technology in this step is OLAP tools that are supported by the Data Warehouse. OLAP operations can be very diverse and include slicing (selecting data from one dimension/perspective), drilling (navigating up or down in the hierarchical dimensions of the data), and pivoting or rotating (visualize data from a new perspective). In this step, technology mainly enables the generation and visualization of information about the organization in real-time. This allows stakeholders to track business performance, answering the question “what is happening right now?” and taking real-time decision that impact the near future of the organization. These decisions can be supported by dashboards, KPI (Key Performance Indicator) scorecards, and other similar visual tools.
New database technologies (‘big data’)
Published in Markus Franke, Managing Airline Networks, 2020
But what do ‘huge amounts of data’ and ‘very rapid response rates’ mean? Since these are technical terms, it would seem reasonable to measure them against the performance parameters of database solutions already existing at that time. Standard SQL (Structured Query Language) databases, such as Microsoft SQL and Oracle 11G, can easily handle data volumes up to 1 terabyte (1012 = 1 trillion bytes), achieving response times of a few minutes for complex requests. SQL solutions are more than sufficient for the analysis of operations. Many data warehouse solutions contain so-called data or OLAP (online analytical processing) cubes, offering well-structured access to multidimensional data.
OCP: an OLAP-based bus crowdedness smart-perceiving mechanism for urban transportation
Published in Connection Science, 2022
Shiwen Gong, Dandan Liu, Md Zakirul Alam Bhuiyan, Xiaoguang Niu
Online Analytical Processing (OLAP) is a technology that enables analysts to quickly, consistently, and interactively observe information from all aspects to gain a deep understanding of the data. To facilitate this analysis, data is collected from multiple data sources and stored in a data warehouse, which is then cleaned and organised into data cubes. Each OLAP cube contains dimensionally ordered data extracted from dimension tables in the data warehouse. The dimensions are then populated by members of the hierarchical organisation. OLAP cubes are often pre-aggregated across dimensions to dramatically reduce query times for relational databases.