MicroFilter: Scalable Real-Time Filtering Of Micro-blogging Content

Abstract. Microblogging systems have become a major trend over the Web. After only 7 years of existence, Twitter for instance claims more than 500 million users with more than 350 billion delivered update each day. As a consequence the user must today manage possibly extremely large feeds, resulting in poor data readability and loss of valuable infor- mation and the system must face a huge network load. In this demon- stration, we present and illustrate the features of MicroFilter (MF in the the following), an inverted list-based filtering engine that nicely extends existing centralized microblogging systems by adding a real-time filtering feature. The demonstration proposed illustrates how the user experience is improved, the impact on the traffic for the overall system, and how the characteristics of microblogs drove the design of the indexing structures.