Auflistung nach Autor:in "Fischer, Ulrike"
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- KonferenzbeitragEfficient in-memory indexing with generalized prefix trees(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Boehm, Matthias; Schlegel, Benjamin; Volk, Peter Benjamin; Fischer, Ulrike; Habich, Dirk; Lehner, WolfgangEfficient data structures for in-memory indexing gain in importance due to (1) the exponentially increasing amount of data, (2) the growing main-memory capacity, and (3) the gap between main-memory and CPU speed. In consequence, there are high performance demands for in-memory data structures. Such index structures are used-with minor changes-as primary or secondary indices in almost every DBMS. Typically, tree-based or hash-based structures are used, while structures based on prefix-trees (tries) are neglected in this context. For tree-based and hash-based structures, the major disadvantages are inherently caused by the need for reorganization and key comparisons. In contrast, the major disadvantage of trie-based structures in terms of high memory consumption (created and accessed nodes) could be improved. In this paper, we argue for reconsidering prefix trees as in-memory index structures and we present the generalized trie, which is a prefix tree with variable prefix length for indexing arbitrary data types of fixed or variable length. The variable prefix length enables the adjustment of the trie height and its memory consumption. Further, we introduce concepts for reducing the number of created and accessed trie levels. This trie is order-preserving and has deterministic trie paths for keys, and hence, it does not require any dynamic reorganization or key comparisons. Finally, the generalized trie yields improvements compared to existing in-memory index structures, especially for skewed data. In conclusion, the generalized trie is applicable as general-purpose in-memory index structure in many different OLTP or hybrid (OLTP and OLAP) data management systems that require balanced read/write performance.
- KonferenzbeitragForecasting in Database Systems(Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Fischer, UlrikeTime series forecasting is crucial in a number of domains such as production planning and energy load balancing. In these areas, forecasts are often required by non-expert users on large multi-dimensional data sets expecting short response times. However, as current traditional database systems support forecasting only in a limited and non-declarative way, it is performed outside the database system by specially trained experts. We introduce a novel approach that seamlessly integrates time series forecasting into an existing database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data for any user and is automatically processed by the core engine of an existing DBMS. We
- KonferenzbeitragOffline design tuning for hierarchies of forecast models(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Fischer, Ulrike; Boehm, Matthias; Lehner, WolfgangForecasting of time series data is crucial for decision-making processes in many domains as it allows the prediction of future behavior. In this context, a model is fit to the observed data points of the time series by estimating the model parameters. The computed parameters are then utilized to forecast future points in time. Existing approaches integrate forecasting into traditional relational query processing, where a forecast query requests the creation of a forecast model. Models of continued interest should be deployed only once and used many times afterwards. This however leads to additional maintenance costs as models need to be kept up-to-date. Costs can be reduced by choosing a well-defined subset of models and answering queries using derivation schemes. In contrast to materialized view selection, model selection opens a whole new problem area as results are approximate. A derivation schema might increase or decrease the accuracy of a forecast query. Thus, a two-dimensional optimization problem of minimizing the model cost and model usage error is introduced in this paper. Our solution consists of a greedy enumeration approach that empirically evaluates different configurations of forecast models. In our experimental evaluation, with data sets from different domains, we show the superiority of our approach over traditional approaches from forecasting literature.
- KonferenzbeitragRethinking energy data management: trends and challenges in today's transforming markets(Datenbanksysteme für Business, Technologie und Web (BTW) 2038, 2013) Ulbricht, Robert; Fischer, Ulrike; Lehner, Wolfgang; Donker, HilkoThe energy market domain is subject to a continuous transformation process, mostly driven by governmental regulations. To efficiently handle the large amounts of data and the communication processes between market participants, specialized database applications have been developed. In this paper, we present the energy data management system (EDMS) as a standard software solution, describing its core components and typical system integration aspects. However, current market topics like smart metering, energy saving, forecasting for renewable energy sources, mobile consumption and smart grids lead to new database challenges. We provide an overview of these trends and discuss their impact on existing information systems, focusing on the technical challenges of data integration, data storage, data analytics and scalability. As energy data management has to match those new requirements, promising research opportunities are offered to the database community.
- ZeitschriftenartikelTowards Integrated Data Analytics: Time Series Forecasting in DBMS(Datenbank-Spektrum: Vol. 13, No. 1, 2013) Fischer, Ulrike; Dannecker, Lars; Siksnys, Laurynas; Rosenthal, Frank; Boehm, Matthias; Lehner, WolfgangIntegrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.
- TextdokumentZeitreihenprognose in relationalen Datenbanksystemen(Ausgezeichnete Informatikdissertationen 2013, 2014) Fischer, Ulrike