In this work, we consider processing k-nearest-neighbor (k-NN) queries, with the additional requirement that the result objects are of a specific type. To solve this problem, we propose an approach based on a combination of an inverted index and state-of-the-art similarity search index structure for efficiently pruning the search space early-on. Furthermore, we provide a cost model, and an extensive experimental study, that analyzes the performance of the proposed index structure under different configurations, with the aim of finding the most efficient one for the dataset being searched.

A novel approach to explore databases using ranked lists is demonstrated. Working with ranked lists, capturing the relative performance of entities, is a very intuitive and widely applicable concept. Users can post lists of entities for which explanatory SQL queries and full result lists are returned. By refining the input, the results, or the queries, user can interactively explore the database content. The demonstrated system is centered around our PALEO framework for reverse engineering OLAP-style database queries and novel work on mining interesting categorical attributes.

In the big-data era data is arriving at such a high pace and volume that data exploration and querying can only be feasible if data loading and indexing happens reasonably quick—if at all. Recent research on handling large scien- tific data suggests ignoring any database indexing or even data-loading processing steps but rather turns toward pro- cessing raw data as it is handed in by scientists, manually or by semi-automated means—if needed in multiple, itera- tive steps. In this paper, we describe the anatomy and re- search challenges of a system coined ligDB 1 that is operat- ing purely on incomplete database tables, JSON documents, or sets of SPO triplets that are being filled over time. There is no data stored per se; the only data stored is stemming from previously posed queries over the stream of arriving data; kept as long as it is used by forthcoming queries and otherwise evicted. A key point is that velocity dimension of big dataallows queries being processed as they are posted, with higher-level queries processed on historic query results (views) and live data. Data that is not touched by any posted query is immediately discarded.

We consider the task of automatically phrasing and computing top-k rankings over the information contained in common knowledge bases (KBs), such as YAGO or DBPedia. We assemble the thematic focus and ranking criteria of rankings by inspecting the present Subject, Predicate, Object (SPO) triples. Making use of numerical attributes contained in the KB we are also able to compute the actual ranking content, i.e., entities and their performances. We further discuss the integration of existing rankings into the ranking generation process for increased coverage and ranking quality. We report on first results obtained using the YAGO knowledge base.