01848nas a2200253   4500000000100000000000100001008004100002260001200043653002800055653003400083653001700117653000800134100001700142700002500159700001600184700002000200700001900220245006600239856009800305300001000403490000600413520116100419022001401580       2014                            d  c06/201410aArtificial Intelligence10aGeographic information system10aOptimization10aPSO1 aVinay Bhaska1 aAbhishek Kumar Singh1 aJyoti Dhruw1 aAnubha Parashar1 aMradula Sharma00aBusiness and Social Behaviour Intelligence Analysis Using PSO  uhttp://www.ijimai.org/journal/sites/default/files/files/2014/05/ijimai20142_6_8_pdf_20898.pdf  a69-740 v23 aThe 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  a1989-1660