This edited volume offers a clear in-depth overview of research covering a variety of issues in social search and recommendation systems. Within the broader context of social network analysis it focuses on important and up-coming topics such as real-time event data collection, frequent-sharing pattern mining, improvement of computer-mediated communication, social tagging information, search system personalization, new detection mechanisms for the identification of online user groups, and many more. The twelve contributed chapters are extended versions of conference papers as well as completely new invited chapters in the field of social search and recommendation systems. This first-of-its kind survey of current methods will be of interest to researchers from both academia and industry working in the field of social networks.

All mainstream database systems lack support for temporal metadata, and instead store only the most recent data. Consequently, they can only manage temporal data in an ad hoc manner -- oftentimes not effectively. Yet there are countless application domains that need temporal data. This book offers a much-needed solution: It includes a bitemporal relational data model, based on nested relations, as well as its algebra and calculus languages for expressing queries. The fundamental construct for representing temporal data is a bitemporal atom; a value, its validity period, and the time this data was recorded in the database. The concept of 'context' defines different views of bitemporal data. BtSQL, a simple bitemporal variation of the SQL, is introduced. The book also provides a prototype implementation, which demonstrates the feasibility of the proposed approach in an object-relational database system. Researchers and practitioners in database development will find this book useful. It can be used as a textbook in advanced database courses -- particularly in application domains where temporal metadata can greatly leverage the value of the data as it is stored and utilized.