Abstract. Until recently algorithms continuously gained free performance improvements due to ever increasing processor speeds. Unfortunately, this development has reached its limit. Nowadays, new generations of CPUs focus on increasing the number of processing cores instead of simply increasing the performance of a single core. Thus, sequential algorithms will be excluded from future technological advances. Instead, highly scalable parallel algorithms are needed to fully tap new hardware potentials. In this paper we establish a design space for parallel algorithms in the domain of personalized database retrieval, taking skyline algorithms as a representative example. We will investigate the spectrum of base operations of different retrieval algorithms and various parallelization techniques to develop a set of highly scalable and high-performing skyline algorithms for different retrieval scenarios. Finally, we extensively evaluate these algorithms to showcase their superior characteristics.

By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowdsourcing to obtain all values individually, we leverage the usergenerated data found in the Social Web: By exploiting user ratings we build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.

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Abstract. When issuing user-specific queries, users often have a vaguely defined information need. Skyline queries identify the most "interesting" objects for users' incomplete preferences, which provides users with intuitive query formulation mechanism. However, the applicability of this intuitive query paradigm suffers from a severe drawback. Incomplete preferences on domain values can often lead to impractical skyline result sizes. In particular, this challenge is more critical over categorical domains. This paper addresses this challenge by developing an iterative elicitation framework. While user preferences are collected at each iteration, the framework aims to both minimize user interaction and maximize skyline reduction. The framework allows to identify a reasonably small and focused skyline set, while keeping the query formulation still intuitive for users. All that is needed is answering a few well-chosen questions. We perform extensive experiments to validate the benefits of our strategy and prove that a few questions are enough to acquire a desired manageable skyline set.

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The storage, management, and retrieval of entity data has always been among the core applications of database systems. However, since nowadays many people access entity collections over the Web (e.g., when searching for products, people, or events), there is a growing need for integrating unconventional types of data into these systems, most notably entity descriptions in unstructured textual form. Prime examples are product reviews, user ratings, tags, and images. While the storage of this data is well-supported by modern database technology, the means for querying it in semantically meaningful ways remain very limited. Consequently, in entity-centric search suffers from a growing semantic gap between the users' intended queries and the database's schema. In this paper, we introduce the notion of conceptual views, an innovative extension of traditional database views, which aim to uncover those query-relevant concepts that are primarily reflected by unstructured data. We focus on concepts that are vague in nature and cannot be easily extracted by existing technology (e.g., business phone and romantic movie). After discussing different types of concepts and conceptual queries, we present two case studies, which illustrate how meaningful conceptual information can automatically be extracted from existing data, thus enabling the effective handling of vague real-world query concepts.

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