Information access needs to be uncomplicated, users rather use incorrect data which is easily received than correct information which is harder to obtain. Querying bibliographic metadata from digital libraries mainly supports simple textual queries. A user's demand for answering more sophisticated queries could be fulfilled by the usage of SQL. As such means are highly complex and challenging even for trained programmers, a domain-specific query language is needed to provide a straightforward way to access data. In this paper we present SchenQL, a simple query language focused on bibliographic metadata in the area of computer science while using the vocabulary of domain-experts. By facilitating a plain syntax and fundamental aggregate functions, we propose an easy-to-learn domain-specific query language capable of search and exploration. It is suitable for domain-experts as well as casual users while still providing the possibility to answer complicated queries. A user study with computer scientists directly compared our query language to SQL and clearly demonstrated SchenQL's suitability and usefulness for given queries as well as users' acceptance.
Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook's Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook's search offerings. Recently, many content sites have started encouraging their users to engage in social activities such as adding buddies on Yahoo! Travel and sharing articles with their friends on New York Times. This has led to the emergence of social content sites, which is being facilitated by initiatives like OpenID 1 and OpenSocial 2 . These community standards enable the open access to users’ social profil es and connections by individual content sites and are bringing contentoriented sites and social networking sites ever closer. The integration of content and social information raises new challenges for information management and discoveryover such sites. We propose a logical architecture, named SocialScope, consisting of three layers, for tackling the challenges. The content managementlayer is responsible for integrating, maintaining and physicall y accessing the content and social data. The information discovery layer takes care of analyzing content to derive interesting new information, and interpreting and processing the user’s information need to identify relevant information. Finally, the information presentation layer explores the discovered information and helps users better understand it in a principled way. We describe the challenges in each layer and propose solutions for some of those challenges. In particular, we propose a uniform algebraic framework, which can be leveraged to uniformly and flexibly specify many of the infor mation discovery and analysis tasks and provide the foundation for the optimization of those tasks.