Today there is an abundance of data on line. The grand challenge is turning this huge amount of data to knowledge useful to the individual users of the Internet. DiP3 addresses this challenge by tackling one form of data processing, often referred to as "push" data delivery. In push data delivery, instead of explicitly searching for information, users get notified when relevant information becomes available. Examples of such systems include RSS feeds, news alerts and aggregators. The scientific objective of this project is to derive models, algorithms and techniques to control both the amount and quality of information received by users. To this end, we have incorporated user preferences in data delivery to rank data items based on their relevance to the users. Although preference specification has been extensively studied, there is little previous research work on incorporating preferences in Internet-scale data delivery. Furthermore, DIP3 exploits the inherent social connections between users in Web 2.0 as expressed through social networks, social tagging, and other community-based features to enhance preference specification and ranked information delivery. DIP3 has focused on two issues central to the success of such systems: privacy preservation and performance.Summarizing, we aim at delivering to the users the most relevant and interesting information. To this end, DIP3 has:
- Introduced novel preference models and new algorithms for preference enforcement.
- Exploited the rich information available today in the social Web to enhance user preferences.
- Explored methods for retaining privacy with emphasis on personalized privacy.
- Advanced new methods at the system level (including caching, replication, indexing and clustering) to provide performance guarantees.