Auflistung nach Autor:in "Nicklas, Daniela"
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- Textdokument1st Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC)(BTW 2019 – Workshopband, 2019) Broneske, David; Habich, Dirk
- TextdokumentAn Actor Database System for Akka(BTW 2019 – Workshopband, 2019) Schmidl, Sebastian; Schneider, Frederic; Papenbrock, ThorstenSystem architectures for data-centric applications are commonly comprised of two tiers: An application tier and a data tier. The fact that these tiers do not typically share a common format for data is referred to as object-relational impedance mismatch. To mitigate this, we develop an actor database system that enables the implementation of application logic into the data storage runtime. The actor model also allows for easy distribution of both data and computation across multiple nodes in a cluster. More specifically, we propose the concept of domain actors that provide a type-safe, SQL-like interface to develop the actors of our database system and the concept of Functors to build queries retrieving data contained in multiple actor instances. Our experiments demonstrate the feasibility of encapsulating data into domain actors by evaluating their memory overhead and performance. We also discuss how our proposed actor database system framework solves some of the challenges that arise from the design of distributed databases such as data partitioning, failure handling, and concurrent query processing.
- KonferenzbeitragAnfrage-getriebener Wissenstransfer zur Unterstützung von Datenanalysten(Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Wahl, Andreas M.; Endler, Gregor; Schwab, Peter K.; Herbst, Sebastian; Lenz, RichardIn größeren Organisationen arbeiten verschiedene Gruppen von Datenanalysten mit unterschiedlichen Datenquellen, um analytische Fragestellungen zu beantworten. Das Formulieren effektiver analytischer Anfragen setzt voraus, dass die Datenanalysten profundes Wissen über die Existenz, Semantik und Verwendungskontexte relevanter Datenquellen besitzen. Derartiges Wissen wird informell innerhalb einzelner Gruppen von Datenanalysten geteilt, jedoch meist nicht in formalisierter Form für andere verfügbar gemacht. Mögliche Synergien bleiben somit ungenutzt. Wir stellen einen neuartigen Ansatz vor, der existierende Datenmanagementsysteme mit zusätzlichen Fähigkeiten für diesen Wissenstransfer erweitert. Unser Ansatz fördert die Kollaboration zwischen Datenanalysten, ohne dabei etablierte Analyseprozesse zu stören. Im Gegensatz zu bisherigen Forschungsansätzen werden die Analysten beim Transfer des in analytischen Anfragen enthaltenen Wissens unterstützt. Relevantes Wissen wird aus dem Anfrageprotokoll extrahiert, um das Auffinden von Datenquellen und die inkrementelle Datenintegration zu erleichtern. Extrahiertes Wissen wird formalisiert und zum Anfragezeitpunkt bereitgestellt.
- TextdokumentAngepasstes Item Set Mining zur gezielten Steuerung von Bauteilen in der Serienfertigung von Fahrzeugen(BTW 2019 – Workshopband, 2019) Spieß, Marco; Reimann, PeterQualitätsprobleme im Bereich Fahrzeugbau können nicht nur zum Imageverlust des Unternehmens führen, sondern auch mit entsprechend hohen Kosten einhergehen. Wird ein Bauteil als Verursacher eines Qualitätsproblems identifiziert, muss dessen Verbau gestoppt werden. Mit einer Datenanalyse kann herausgefunden werden, welche Fahrzeugkonfigurationen Probleme mit diesem fehlerverursachenden Bauteil haben. Im Rahmen der domänenspezifischen Problemstellung wird in diesem Beitrag die Anwendbarkeit von Standardalgorithmen aus dem Bereich Data-Mining untersucht. Da die Analyseergebnisse auf Standardausstattungen hinweisen, sind diese nicht zielführend. Für dieses Businessproblem von Fahrzeugherstellern haben wir einen Data-Mining Algorithmus entwickelt, der das Vorgehen des Item Set Mining der Assoziationsanalyse an das domänenspezifische Problem anpasst. Er unterscheidet sich zum klassischen Apriori-Algorithmus in der Beschneidung des Ergebnisraumes sowie in der nachfolgenden Aufbereitung und Verwendungsweise der Item Sets. Der Algorithmus ist allgemeingültig für alle Fahrzeughersteller anwendbar. Die Ergebnisse sind anhand eines realen Anwendungsfalls evaluiert worden, bei dem durch die Anwendung unseres Algorithmus 87% der Feldausfälle verhindert werden können.
- KonferenzbeitragApplication and Testing of Business Processes in the Energy Domain(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Böhmer, Kristof; Stertz, Florian; Hildebrandt, Tobias; Rinderle-Ma, Stefanie; Eibl, Günther; Ferner, Cornelia; Burkhart, Sebastian; Engel, DominikThe energy domain currently struggles with radical legal and technological changes, such as, smart meters. This results in new use cases which can be implemented based on business process technology. Understanding and automating business processes requires to model and test them. However, existing process testing approaches frequently struggle with the testing of process resources, such as ERP systems, and negative testing. Hence, this work presents a toolchain which tackles that limitations. The approach uses an open source process engine to generate event logs and applies process mining techniques in a novel way.
- Konferenzbeitragasprin: Answer Set Programming with Preferences(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Romero, JavierAnswer Set Programming (ASP) is a well established approach to declarative problem solving, combining a rich yet simple modeling language with high-performance solving capacities. In this talk we present asprin, a general, flexible and extensible framework for preferences in ASP. asprin is general and captures many of the existing approaches to preferences. It is flexible, because it allows for the combination of different types of preferences. It is also extensible, allowing for an easy implementation of new approaches to preferences. Since it is straightforward to capture propositional theories and constraint satisfaction problems in ASP, the framework is also relevant to optimization in Satisfiability Testing and Constraint Processing.
- TextdokumentAssessing the Impact of Driving Bans with Data Analysis(BTW 2019 – Workshopband, 2019) Woltmann, Lucas; Hartmann, Claudio; Lehner, Wolfgang
- TextdokumentAutomated Architecture-Modeling for Convolutional Neural Networks(BTW 2019 – Workshopband, 2019) Duong, Manh KhoiTuning hyperparameters can be very counterintuitive and misleading, yet it plays a big (or even the biggest) part in many machine learning algorithms. For instance, finding the right architecture for an artificial neural network (ANN) can also be seen as a hyperparameter e.g. number of convolutional layers, number of fully connected layers etc. Tuning these can be done manually or by techniques such as grid search or random search. Even then finding optimal hyperparameters seems to be impossible. This paper tries to counter this problem by using bayesian optimization, which finds optimal parameters, including the right architecture for ANNs. In our case, a histological image dataset was used to classify breast cancer into stages.
- KonferenzbeitragAutonomous Data Ingestion Tuning in Data Warehouse Accelerators(Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Stolze, Knut; Beier, Felix; Müller, JensThe IBM DB2 Analytics Accelerator (IDAA) is a state-of-the art hybrid database system that seamlessly extends the strong transactional capabilities of DB2 for z/OS with very fast processing of OLAP and analytical SQL workload in Netezza. IDAA copies the data from DB2 for z/OS into its Netezza backend, and customers can tailor data maintenance according to their needs. This copy process, the data load, can be done on a whole table or just a physical table partition. IDAA also o ers an incremental update feature, which employs replication technologies for low-latency data synchronization. The accelerator targets big relational databases with several TBs of data. Therefore, the data load is performance-critical, not only for the data transfer itself, but the system has to be able to scale up to a large number of tables, i. e., tens of thousands to be loaded at the same time, as well. The administrative overhead for such a number of tables has to be minimized. In this paper, we present our work on a prototype, which is geared towards e ciently loading data for many tables, where each table may store only a comparably small amount of data. A new load scheduler has been introduced for handling all concurrent load requests for disjoint sets of tables. That is not only required for a multi-tenant setup, but also a significant improvement for attaching an accelerator to a single DB2 for z/OS system. In this paper, we present architecture and implementation aspects of the new and improved load mechanism and results of some initial performance evaluations.
- KonferenzbeitragBenchmarking Univariate Time Series Classifiers(Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Schäfer, Patrick; Leser, UlfTime series are a collection of values sequentially recorded over time. Nowadays, sensors for recording time series are omnipresent as RFID chips, wearables, smart homes, or event-based systems. Time series classification aims at predicting a class label for a time series whose label is unknown. Therefore, a classifier has to train a model using labeled samples. Classification time is a key challenge given new applications like event-based monitoring, real-time decision or streaming systems. This paper is the first benchmark that compares 12 state of the art time series classifiers based on prediction and classification times. We observed that most of the state-of-the-art classifiers require extensive train and classification times, and might not be applicable for these new applications.