Information analysis for dynamic sale planning by AI decision support process. Issue 3 (May 2023)

• Record Type: Journal Article
• Authors: Wang, Feng; Luan, Yu; Badawi, Abdel Nour; Ayad, Abbad; Abdallah, Abdel Fattah; Ali, Mansour; Ahmad, Zobair; Jiang, Wu
• Is Part Of: Information processing & management. Volume 60:Issue 3(2023)
• Publication Date: 2023-05
• Subjects: Artificial intelligence AI -- Decision support process -- Information analysis -- Particle swarm optimization -- Sale planning -- Database management system; DW data warehouse -- DB Database -- QPSO quantum particle swarm optimization -- PSO particle swarm optimization -- DBMS database management system -- PG polynomial greedy -- HPSO human particle swarm optimization -- DSS decision support system; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2023.103319
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Marketing is all about finding out what your customers want and need. The information is used to create the model and improve the quality of products and services to satisfy customers. There are two major parts to a sale's planning: containing sales tactics and sales strategy. To aid in business decision-making, a data warehouse (DW) for sales collects and organizes relevant and historical data. DW is also a method for combining data from various heterogeneous databases (DB) and other sources of information for analysis. In this paper, Information analysis for dynamic Sale planning by AI Decision support process was done. Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) algorithms have been used to find optimal MVs for sale planning since several authors prove that these algorithms are better than other existing algorithms. MVs selection is based on three factors: time running of MVs, area of MVs, and access frequency of MVs by manipulating the selection result using a weighted combination of each factor as needed by the designer. For PSO, the most cost of frequency was 1.924 for View (V) 11, while for QPSO, it was 1.722 for V11. For PSO, the most cost of time was 1.931 for V2, while for QPSO, it was 1.221 for V22. For PSO, the most cost of the area was 1.800 for V17, while for QPSO, it was 1.071 for V17. The results revealed that Quantum Particle Swarm Optimization is much more accurate than Particle Swarm Optimization.