Upcoming

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Past

17.02. - 19.02.2017 at Hotel Traube, Graun, Italy
Unnesting Arbitrary Queries
Thomas Neumann (Technical University of Munich (TUM))
28.10.2016, 11:00, room T03, Dept. of Computer Sciences
SQL-99 allows for nested subqueries at nearly all places within a query. From a user's point of view, nested queries can greatly simplify the formulation of complex queries. However, nested queries that are correlated with the outer queries frequently lead to dependent joins with nested loops evaluations and thus poor performance. Existing systems therefore use a number of heuristics to unnest these queries, i.e., de-correlate them. These unnesting techniques can greatly speed up query processing, but are usually limited to certain classes of queries. To the best of our knowledge no existing system can de-correlate queries in the general case. We present a generic approach for unnesting arbitrary queries. As a result, the de-correlated queries allow for much simpler and much more efficient query evaluation.
Exploiting Knowledge Facets for Enhanced Information Search
Mouna Kacimi (Free University of Bolzano)
29.04.2016, 15:30, room T06, Dept. of Computer Sciences
Search results about a given query topic are typically unstructured making it hard to understand the relationships between the different sources of information. Thus, there is a need for organizing search results to help users to (1) gain more insights about query topics, and (2) have an easy access to information sources that trigger their interests. This is particularly helpful for ambiguous queries or faceted topics that involve a variety of sub-topics, meanings, versions, arguments, opinions, and many other aspects. In this talk, I present techniques that exploit existing knowledge bases to enhance information search. I first show how to exploit Wikipedia for query expansion and search results diversification. Then, I proceed with the organization of information sources allowing an effective navigation through knowledge facets.
Keyword-Based Querying with Local Intent
Christian Jensen (Aalborg University)
15.04.2016, 09:00
Datenmodellierung in der Anwendungsentwicklung mit NoSQL-Datenbanken
Stefanie Scherzinger (OTH Regensburg)
17.10.2014
NoSQL-Datenbanken sind gerade in der Webentwicklung zunehmend beliebt. Oft sind es die großen Datenmengen, die es zu verwalten gilt, mitunter sind diese Systeme aber auch wegen ihrer Schema-Flexibilität für agile Entwicklungsteams interessant. Indem viele NoSQL-Datenbanken keine Unterstützung für die Definition, Einhaltung und Wartung eines globalen Schemas bieten, verlagern sich klassische Aufgaben des Datenbank-managementsystems in die Anwendungssoftware. Dieser Vortrag gibt einen Überblick über konkrete Herausforderungen, die sich in der Praxis beim Entwurf eines Datenmodells für Key-Value- und Dokumenten-Datenbanken ergeben. Dazu zählen eine Modellierung, die atomare Updates ermöglicht, das Vermeiden von Hot-Spot-Datenobjekten, wie sie durch hochfrequente, parallele Schreibzugriffe gegen dasselbe Objekt verursacht werden, sowie Strategien zum Umgang mit kontinuierlicher Schema-Evolution. Der Vortrag zeigt auf, dass gerade die Datenbank-Community mit ihrem Erfahrungsschatz im Schema-Management und ihrem breiten Fundus an formalen Methoden hier einen wertvollen Beitrag leisten kann.
Similarity Queries
Yasin N. Silva (Arizona State University)
11.07.2013
Many application scenarios can significantly benefit from the identification and processing of similarities in the data. Even though some work has been done to extend the semantics of some operators, e.g., join and selection, to be aware of data similarities; there has not been much study on the role and implementation of similarity-aware operations as first-class database operators. Furthermore, very little work has addressed the problem of evaluating and optimizing queries that combine several similarity operations. The focus of this presentation is the study of similarity queries that contain one or multiple first-class similarity database operators, e.g., Similarity Selection, Similarity Join, and Similarity Group-by. We will present implementation techniques of several similarity operators; a comprehensive conceptual evaluation model for similarity queries; and a rich set of transformation rules to extend cost-based query optimization to the case of similarity queries. We will also discuss techniques to implement similarity operators using the MapReduce framework to process massive datasets.