Social and Business Intelligence Analysis Using PSO

— The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self-descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour. I. INTRODUCTION warm describes a behavior of an aggregate of animals of similar size and body orientation [1]. Swarm intelligence (SI) is based on the collective behavior of a group of animals. Collective intelligence emerges via grouping and communication, resulting in successful foraging (the act of searching for food and provisions) for individual in the group, for examples Bees, ants, termites, fishes, birds etc. The perform the following sequence of activity in group: Marching of ants in an army, Birds flocking in high skies, Fish school in deep waters, Foraging activity of microorganisms .In the context of AI, SI systems are based on collective behavior of decentralized, self-organized systems [2]. Typically made up of a population of simple le agents interacting with one another locally and with their environment causing coherent functional global pattern to emerge. Distributed problem solving model without centralized control. Even with no