Ring: Real-Time Emerging Anomaly Monitoring System Over Text Streams

Microblog platforms have been extremely popular in the big data era due to its real-time diffusion of information. It's important to know what anomalous events are trending on the social network and be able to monitor their evolution and find related anomalies. In this paper we demonstrate <sc>Ring</sc>, a <underline>r</underline>eal-t<underline>i</underline>me emerging a<underline>n</underline>omaly monitorin<underline>g</underline> system over microblog text streams. <sc>Ring</sc> integrates our efforts on both emerging anomaly monitoring research and system research. From the anomaly monitoring perspective, <sc>Ring</sc> proposes a graph analytic approach such that (1) <sc>Ring</sc> is able to detect emerging anomalies at an earlier stage compared to the existing methods, (2) <sc>Ring</sc> is among the first to discover emerging anomalies correlations in a streaming fashion, (3) <sc>Ring</sc> is able to monitor anomaly evolutions in real-time at different time scales from minutes to months. From the system research perspective, <sc>Ring</sc> (1) optimizes time-ranged keyword query performance of a full-text search engine to improve the efficiency of monitoring anomaly evolution, (2) improves the dynamic graph processing performance of Spark and implements our graph stream model on it, As a result, <sc>Ring</sc> is able to process big data to the entire Weibo or Twitter text stream with linear horizontal scalability. The system clearly presents its advantages over existing systems and methods from both the event monitoring perspective and the system perspective for the emerging event monitoring task.