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Auflistung nach Autor:in "Scherzinger, Stefanie"

1 - 10 von 101
  • Konferenzbeitrag
    Accelerating Large Table Scan using Processing-In-Memory Technology
    (BTW 2023, 2023) Baumstark, Alexander; Jibril, Muhammad Attahir; Sattler, Kai-Uwe
    Today’s systems are capable of storing large amounts of data in main memory. In-memoryDBMSs can benefit particularly from this development. However, the processing of the data fromthe main memory necessarily has to run via the CPU. This creates a bottleneck, which affects thepossible performance of the DBMS. The Processing-In-Memory (PIM) technology is a paradigm toovercome this problem, which was not available in commercial systems for a long time. However, withthe availability of UPMEM, a commercial system is finally available that provides PIM technologyin hardware. In this work, the main focus was on the optimization of the table scan, a fundamental,and memory-bound operation. Here a possible approach is shown, which can be used to optimizethis operation by using PIM. This method was then tested for parallelism and execution time inbenchmarks with different table sizes and compared to the usual table scan. The result is a table scanthat outperforms the scan on the usual CPU significantly.
  • Konferenzbeitrag
    Adaptive Architectures for Robust Data Management Systems
    (BTW 2023, 2023) Bang, Tiemo
    Form follows function is a well-known expression by the architect Sullivan asserting that the architecture of a building should follow its function. 'Adaptive Architectures for Robust Data Management Systems' is a dissertation asserting that DBMS architectures should follow changing workload and hardware to robustly achieve high DBMS performance. The dissertation first evaluates how workload and hardware affect the performance of DBMSs with static architectures. This evaluation concludes that static DBMS architectures degrade DBMS performance under changing workload and hardware, and hence the DBMS architecture has to become adaptive. Subsequently, adaptation concepts for the architecture of single-server and multi-server DBMSs are proposed. These concepts focus fine-grained adaptation of DBMS architectures and are realized through asynchronous programming models. These programming models decouple the implementation of DBMS components from fine-grained architectural optimization. Thereby, optimizers can derive novel architectures better fitting individual DBMS components, leading to high and robust DBMS performance under changing conditions.
  • Konferenzbeitrag
    Agil und spielerisch: Neue Methoden der Software-Entwicklung in der Praxis und ihr Potential für den Schulunterricht
    (Informatik in Bildung und Beruf – INFOS 2011 – 14. GI-Fachtagung Informatik und Schule, 2011) Scherzinger, Stefanie
  • Konferenzbeitrag
    Approach to Synthetic Data Generation for Imbalanced Multi-class Problems with Heterogeneous Groups
    (BTW 2023, 2023) Treder-Tschechlov, Dennis; Reimann, Peter; Schwarz, Holger; Mitschang, Bernhard
    To benchmark novel classification algorithms, these algorithms should be evaluated on data with characteristics that also appear in real-world use cases. Important data characteristics that often lead to challenges for classification approaches are multi-class imbalance and heterogeneous groups. Real-world data that comprise these characteristics are usually not publicly available, e. g., because they constitute sensible patient information or due to privacy concerns. Further, the manifestations of the characteristics cannot be controlled specifically on real-world data. A more rigorous approach is to synthetically generate data such that different manifestations of the characteristics can be controlled. However, existing data generators are not able to generate data that feature both data characteristics, i. e., multi-class imbalance and heterogeneous groups. In this paper, we propose an approach that fills this gap as it allows to synthetically generate data that exhibit both characteristics. In particular, we make use of a taxonomy model that organizes real-world entities in domain-specific heterogeneous groups to generate data reflecting the characteristics of these groups. In addition, we incorporate probability distributions to reflect the imbalances of multiple classes and groups from real-world use cases. Our approach is applicable in different domains, as taxonomies are the simplest form of knowledge models and thus are available in many domains. The evaluation shows that our approach can generate data that feature the data characteristics multi-class imbalance and heterogeneous groups and that it allows to control different manifestations of these characteristics.
  • Konferenzbeitrag
    Automated Statement Extraction from Press Briefings
    (BTW 2023, 2023) Keller, Jüri; Bittkowski, Meik; Schaer, Philipp
    Scientific press briefings are a valuable information source. They consist of alternating expert speeches, questions from the audience and their answers. Therefore, they can contribute to scientific and fact-based media coverage. Even though press briefings are highly informative, extracting statements relevant to individual journalistic tasks is challenging and time-consuming.To support this task, an automated statement extraction system is proposed. Claims are used as the main feature to identify statements in press briefing transcripts. The statement extraction task is formulated as a four-step procedure. First, the press briefings are split into sentences and passages, then claim sentences are identified with a single-label multi-class sequence classification. Subsequently, topics are detected, and the sentences are filtered to improve the coherence and assess the length of the statements.The results indicate that claim detection can be used to identify statements in press briefings. While many statements can be extracted automatically with this system, they are not always as coherent as needed to be understood without context and may need further review by knowledgeable persons.
  • Konferenzbeitrag
    Autoshard - a Java object mapper (not only) for hot spot data objects in nosql data stores
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Scherzinger, Stefanie; Thor, Andreas
    We demonstrate AutoShard, a ready-to-use object mapper for Java applications running against NoSQL data stores. AutoShard's unique feature is its capability to gracefully shard hot spot data objects that are suffering under concurrent writes. By sharding data on the level of the logical schema, scalability bottlenecks due to write contention can be effectively avoided. Using AutoShard, developers can easily employ sharding in their web application by adding minimally intrusive annotations to their code. Our live experiments show the significant impact of sharding on both the write throughput and the execution time.
  • Konferenzbeitrag
    Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads
    (BTW 2023, 2023) Lutsch, Adrian; Singh, Gagandeep; Mundt, Martin; Mogk, Ragnar; Binnig, Carsten
    For domains with high data privacy and protection demands, such as health care and finance, outsourcing machine learning tasks often requires additional security measures. Trusted Execution Environments like Intel SGX are a powerful tool to achieve this additional security. Until recently, Intel SGX incurred high performance costs, mainly because it was severely limited in terms of available memory and CPUs. With the second generation of SGX, Intel alleviates these problems. Therefore, we revisit previous use cases for ML secured by SGX and show initial results of a performance study for ML workloads on SGXv2.
  • Konferenzbeitrag
    Better Safe than Sorry: Visualizing, Predicting, and Successfully Guiding Courses of Study
    (BTW 2023, 2023) Kerth, Alexander; Schuhknecht, Felix; Pensel, Lukas; Henneberg, Justus
    Successfully going through a course of study is a lengthy and challenging task. To obtain a degree, many obstacles must be overcome and the right decisions must be made at the right point in time, often overwhelming students. To reduce the amount of dropouts, the goal of study advisors is to reach out to endangered students in time and to provide them help and guidance. To support the work of study advisors, who typically have to monitor a large amount of students simultaneously, we present in this demonstration an easy-to-use graphical tool that (a) allows the advisor to visualize all relevant information of study data in a responsive graph in order to overview the current study situation. Additional to visualization, our tool provides (b) a forecasting functionality based on pre-trained models and (c) a warning feature to identify endangered students early on. In the on-site demonstration, the audience will be able to step into the role of a study advisor and use our tool and all of its features to identify and guide struggling students within anonymized real-world study data.
  • Konferenzbeitrag
    BTW 2023 - Complete proceedings
    (BTW 2023, 2023) Köhnen, Christoph
  • Konferenzbeitrag
    Cleager: Eager Schema Evolution in NoSQL Document Stores
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Scherzinger, Stefanie; Klettke, Meike; Störl, Uta
    Schema-less NoSQL data stores offer great flexibility in application development, particularly in the early stages of software design. Yet over time, software engineers struggle with the heavy burden of dealing with increasingly heterogeneous data. In this demo we present Cleager, a framework for eagerly managing schema evolution in schema-less NoSQL document stores. Cleager executes declarative schema modification operations as MapReduce jobs on the Google Cloud Platform. We present different scenarios that require data migration, such as adding, removing, or renaming properties of persisted objects, as well as copying and moving them between objects. Our audience can declare the required schema migration operations in the Cleager console, and then verify the results in real time.