An advertising analytics framework using social network big data

Since 2000, the Internet has become a primary advertising and marketing channel for businesses. With the recent advance of mobile computing and wireless networking, mobile advertising is now becoming popular because mobile devices provide an effective advertising platform. People today believe that big data analytics provides new opportunities and needs for advertisers. However, most existing advertisement solutions primarily use Behavior Targeting (BT) technology to provide static services, which cannot satisfy the real-time, fast big data processing requirements. The purpose of this project is to develop a new big data analytics service in advertising and marketing based on emergent big data technologies, data mining algorithms, and machine learning solutions. The primary objective of this project is to provide real-time and static on demand services for advertisers and publishers to decide when, what, where, who, and how to place advertisements. In addition, this project requires solutions to analyze the collected big advertising data, discover customers behavior patterns, and establish an innovative model for advertising recommendation and trend prediction. The system will be developed based on advanced machine learning and data mining algorithms, NoSQL database technologies, and visualization techniques. This service will allow advertisers and publishers to reduce their costs while improving their effectiveness.