RSS and Atom are generally less known than the HTML web format, but they are omnipresent in many modern web applications for publishing highly dynamic web contents. Nowadays, news sites publish thousands of RSS/Atom feeds, often organized into general topics like politics, economy, sports, culture, etc. Weblog and microblogging systems like Twitter use the RSS publication format, and even more general social media like Facebook produce an RSS feed for every user and trending topic. This vast number of continuous data-sources can be accessed by using general-purpose feed aggregator applications like Google Reader, desktop clients like Firefox or Thunderbird and by RSS mash-up applications like Yahoo! pipes, Netvibes or Google News. Today, RSS and Atom feeds represent a huge stream of structured text data which potential is still not fully exploited. In this thesis, we first present ROSES -Really Open Simple and Efficient Syndication-, a data model and continuous query language for RSS/Atom feeds. ROSES allows users to create new personalized feeds from existing real-world feeds through a simple, yet complete, declarative query language and algebra. The ROSES algebra has been implemented in a complete scalable prototype system capable of handling and processing ROSES feed aggregation queries. The query engine has been designed in order to scale in terms of the number of queries. In particular, it implements a new cost-based multi-query optimization approach based on query normalization and shared filter factorization. We propose two different factorization algorithms: (i) STA, an adaption of an existing approximate algorithm for finding minimal directed Steiner trees [CCC+98], and (ii) VCA, a greedy approximation algorithm based on efficient heuristics outperforming the previous one with respect to optimization cost. Our optimization approach has been validated by extensive experimental evaluation on real world data collections.
