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Recreating Efficient Framework for Resource-Constrained Environment: HR Analytics and Its Trends for Society 5.0
Published in Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh, Data Science and Innovations for Intelligent Systems, 2021
Kamakshi Malik, Rakesh K. Wats, Aman Khera
The first step is to load the data from a data silo or several source databases and bring them into one target data warehouse environment. Once the data has been loaded into a data warehouse, the next step is to organize the tables in the form of Analysis Models or OLAP (Online Analytical Processing) cube which is a multi-dimensional array of data. It allows the user to query on multi-dimensional data. The next step is to run the analytical queries against these tables which are normally run using the SQL or Structured Query Language and that helps to select and manipulate the data. Based on the output of the query, the data can be visualized in the form of dashboards and reports which helps to provide a view organizations’ KPIs which are the “Key Performance Indicators” and this helps the core management team of a company to make data driven decisions and perform proactive “Strategic Planning.”
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.
Does Knowledge Sharing Belief of Data Analysts Impact Their Behavior?
Published in Journal of Computer Information Systems, 2023
Tool comprehensiveness refers to the maturity of the tools.61 Comprehensive analytical tools provide in-depth knowledge about current or past events, provide precise projections of future happenings, and help firms to understand why something happened in the past.27 Therefore, when data analysts use comprehensive analytical tools, they can obtain more insights and knowledge about the market and the business. In the literature, TTF theory argues that technology characteristic plays a critical role in impacting individuals behavior. In the context of this study, the use of comprehensive tools enables the data analysts to obtain more precise and faster information related to both optimization of business processes and improvement of products/services.22 For example, a data analyst may analyze data using Online Analytical Processing (OLAP) which is a sophisticated technology that enables the data analyst to discover patterns, conduct predictive analysis and some complex analytical calculations.50,53 Data analysts who have a positive belief about sharing their knowledge and have rich knowledge about business, market, and customers through using comprehensive analytical tools may be more likely to share their knowledge with other organizational members. Based on contingency theory, the impact of individuals’ beliefs on their behavior depends on a set of contingency factors. Thus: H3. Tool comprehensiveness moderates the effect of data analysts’ knowledge sharing belief on their knowledge sharing behavior, such that the effect is stronger with comprehensive tools.
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.