Auflistung nach Autor:in "Hose, Katja"
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- ZeitschriftenartikelBericht zur Herbstschule Information Retrieval 2010(Datenbank-Spektrum: Vol. 11, No. 1, 2011) Metzger, Steffen; Hose, Katja; Broschart, Andreas
- KonferenzbeitragEin kooperativer XML- Editor für Workgroups(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Gropengießer, Francis; Hose, Katja; Sattler, Kai -UweIn vielen Anwendungsgebieten, z.B. im Design oder in Medienproduktionsprozessen, hat sich XML als Format für den Datenaustausch etabliert. Zur Verarbeitung von XML-Daten in Mehrbenutzerumgebungen sind spezielle Werkzeuge nötig. Allerdings berücksichtigen
- KonferenzbeitragSharing knowledge between independent grid communities(INFORMATIK 2011 – Informatik schafft Communities, 2011) Hose, Katja; Metzger, Steffen; Schenkel, RalfIn recent years, grid-based approaches for processing scientific data became popular in various fields of research. A multitude of communities has emerged that all benefit from the processing and storage power the grid offers to them. So far there has not yet been much collaboration between these independent communities. But applying semantic technologies to create knowledge bases, sharing this knowledge, and providing access to data maintained by a community, allows to exploit a synergy effect that all communities can benefit from. In this paper, we propose a framework that applies information extraction to generate abstract knowledge from source documents to be shared among participating communities. The framework also enables users to search for documents based on keywords or metadata as well as to search for extracted knowledge. This search is not restricted to the community the user is registered at but covers all registered communities and the data they are willing to share with others.
- ZeitschriftenartikelSkyline Queries(Datenbank-Spektrum: Vol. 16, No. 3, 2016) Hose, KatjaMany applications face the problem that users are overwhelmed by the large amount of available data. In some cases an objective ranking function can be used to order data items by their relevance – similar to the top 10 results displayed by a Web search engine. Other applications, however, aim at considering more diverse preferences and multiple criteria to help users find good results. Such applications can benefit from skyline queries.The best known example use case for a skyline query is a hotel booking scenario where users are looking for hotels. Assume many hotels are available and the user wants to find one based on two criteria: distance to the beach and price per night. Further assume that the user is unable to say which of these criteria is more important. So, we need to look for hotels representing a good combination of both criteria. The skyline consists of all hotels that represent a “good” combinations of both criteria. For each of the other hotels, there is always at least one hotel in the skyline that is better with respect to the two criteria. So, being presented the skyline, the user gets an overview of the available hotels and can make the final decision with respect to her personal preferences for the two criteria. No matter how the user will eventually weigh her personal preferences, she will find her favorite hotel in the skyline.This article gives a short introduction to skyline queries, their main characteristics, and basic ways of processing them.
- KonferenzbeitragSparqling pig - processing linked data with pig Latin(Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Hagedorn, Stefan; Hose, Katja; Sattler, Kai-UweIn recent years, dataflow languages such as Pig Latin have emerged as flexible and powerful tools for handling complex analysis tasks on big data. These languages support schema flexibility as well as common programming patterns such as iteration. They offer extensibility through user-defined functions while running on top of scalable distributed platforms. In doing so, these languages enable analytical tasks while avoiding the limitations of classical query languages such as SQL and SPARQL. However, the tuple-oriented view of general-purpose languages like Pig does not match very well the specifics of modern datasets available on the Web, which often use the RDF data model. Graph patterns, for instance, are one of the core concepts of SPARQL but have to be formulated as explicit joins, which burdens the user with the details of efficient query processing strategies. In this paper, we address this problem by proposing extensions to Pig that deal with linked data in RDF to bridge the gap between Pig and SPARQL for analytics. These extensions are realized by a set of user-defined functions and rewriting rules, still allowing to compile the enhanced Pig scripts to plain MapReduce programs. For all proposed extensions, we discuss possible rewriting strategies and present results from an experimental evaluation.