Enhancing Tracking Techniques in Social Networks

Abstract

Understanding business behaviors requires acquiring huge amounts of data from diverse field studies. The additive growing use of mobile devices in social media, especially in recent years, provides large amounts of data transactions that can help in understanding business behaviors replacing the data acquired by the exhaustive field studies. Location Based Social Networks (LBSN)are considered as a solution providing such data used in urban analysis for economic reasons. Towards more insight for business behavior in this dissertation, a suggestion of global perspectiveexploiting data collected from LBSNs is introduced in order to predict business behavior according to the business geographical location. Moreover, business behavior prediction in LBSNs is studied in this research for big data application. Prediction of customers’ presence ratesfor business venues isintroduced to be implemented using machine learning techniques. Machine learning techniques are investigated for both static and dynamic business predictions in LBSNs. Spatial regression modelsarethoroughly presentedasstatic machine learning techniques. A comparative study is attained in this dissertation for suitabilityto model the relationships in LBSNsin order to be usedfor prediction. Geographically Weighted Regression (GWR) model proved to be the appropriate model in handling the sparse geographical distribution imposed by the LBSNs data. A proposed enhancement over the GWR model is introduced through a distributed training process that is integrated into a partitioned-GWR architecture. The proposed architecture includes a three blocks processes that aredesigned to deal with LBSNs data heterogeneity pursuing more enhanced predictions for business behavior.