DescriptionVideo content is uploaded and shared by users on video sharing websites in vast scale. It is estimated, for example, that every minute 24 hours of video is uploaded to YouTube. Many of the clips are captured at live events, for instance, a U2 concert in Giants Stadium. Increasingly, then, individuals attending the same events upload related content: there are over three hundred YouTube clips from the said concert. This overload makes relevant and interesting videos harder to find, and the event content harder to view and understand. An innovative approach to present the video content is thus necessary. In this thesis, we propose a solution to tackle the problem above. We use the audio fingerprinting algorithm to find overlapping video clips within events, and time-align these overlapping videos based on their audio tracks. We developed a highly interactive video player that organizes and presents the time-aligned video content. The player integrates social data like view counts to help people create a better understanding of the event content, and improve the viewing experience and seeking of clips. We conducted user study sessions to understand user’s interaction and get user feedback for the video player. The web is adopting open standard these days. The work in this thesis follows this trend by providing API services. We designed and built a set of standard API services to expose the underlying audio fingerprinting. In this way other developers can utilize our system remotely and programmatically. They can also build applications using time-align data.