Auflistung nach Autor:in "Klettke, Meike"
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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.
- 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.
- TextdokumentArchitectural Principles for Database Systems on Storage-Class Memory(BTW 2019, 2019) Oukid, IsmailStorage-Class Memory (SCM) is a novel class of memory technologies that combine the byte addressability and low latency of DRAM with the density and non-volatility of traditional storage media. Hence, SCM can serve as persistent main memory, i.e., as main memory and storage at the same time. In this thesis, we dissect the challenges and pursue the opportunities brought by SCM to database systems. To solve the identified challenges, we devise necessary building blocks for enabling SCM-based database systems, namely memory management, data structures, transaction concurrency control, recovery techniques, and a testing framework against new failure scenarios stemming from SCM. Thereafter, we leverage these building blocks to build SOFORT, a novel hybrid SCM-DRAM transactional storage engine that places data, accesses it, and updates it directly in SCM, thereby doing away with traditional write-ahead logging and achieving near-instant recovery.
- 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.
- TextdokumentThe Best of Both Worlds: Combining Hand-Tuned and Word-Embedding-Based Similarity Measures for Entity Resolution(BTW 2019, 2019) Chen, Xiao; Campero Durand, Gabriel; Zoun, Roman; Broneske, David; Li, Yang; Saake, GunterRecently word embedding has become a beneficial technique for diverse natural language processing tasks, especially after the successful introduction of several popular neural word embedding models, such as word2vec, GloVe, and FastText. Also entity resolution, i.e., the task of identifying digital records that refer to the same real-world entity, has been shown to benefit from word embedding. However, the use of word embeddings does not lead to a one-size-fits-all solution, because it cannot provide an accurate result for those values without any semantic meaning, such as numerical values. In this paper, we propose to use the combination of general word embedding with traditional hand-picked similarity measures for solving ER tasks, which aims to select the most suitable similarity measure for each attribute based on its property. We provide some guidelines on how to choose suitable similarity measures for different types of attributes and evaluate our proposed hybrid method on both synthetic and real datasets. Experiments show that a hybrid method reliant on correctly selecting required similarity measures can outperform the method of purely adopting traditional or word-embedding-based similarity measures.
- TextdokumentBig graph analysis by visually created workflows(BTW 2019, 2019) Rostami, M. Ali; Peukert, Eric; Wilke, Moritz; Rahm, ErhardThe analysis of large graphs has received considerable attention recently but current solutions are typically hard to use. In this demonstration paper, we report on an effort to improve the usability of the open-source system Gradoop for processing and analyzing large graphs. This is achieved by integrating Gradoop into the popular open-source software KNIME to visually create graph analysis workflows, without the need for coding. We outline the integration approach and discuss what will be demonstrated.
- TextdokumentBlockchain in the Context of Business Applications and Enterprise Databases(BTW 2019, 2019) Renkes, Frank; Sommer, ChristianBlockchain seems to be the future of all cross-company business applications. Similar to the adoption of machine learning into all novel and existing business applications and processes we can see the same trend for blockchain. Nearly every application tries to leverage blockchain technology to improve the application related process chains. Is this just a hype or is blockchain really the solution to all problems, in which applications rely on an intelligent and secure data distribution / sharing? What are the most relevant qualities of blockchain needed in modern business applications and which role can a traditional database play in this? Wouldn’t be an integration of some of the qualities into traditional databases a better approach to build the so called ‘distributed business applications’? What is the relationship and overlap between core blockchain and core database concepts like (redo) logging, security features like auditing and encryption, distributed (query) processing, as well as stored procedures/smart contracts? This talk discusses how blockchain can be integrated into existing business applications and processes, what the biggest challenges are and which role a traditional database can play in this context.
- TextdokumentThe Borda Social Choice Movie Recommender(BTW 2019, 2019) Kastner, Johannes; Ranitovic, Nemanja; Endres, MarkusIn this demo paper we present a recommender system, which exploits the Borda social choice voting rule for clustering recommendations in order to produce comprehensible results for a user. Considering existing clustering techniques like k-means, the overhead of normalizing and preparing the preferred user data is dropped. In our demo showcase we facilitate a comparison of our clustering approach to the well known k-means++ with traditional distance measures.