P266 - BTW2017 - Datenbanksysteme für Business, Technologie und Web - Workshopband
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- KonferenzbeitragWorkshop Big (and small) Data in Science and Humanities (BigDS17)(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Groß, Anika; König-Ries, Birgitta; Reimann, Peter; Seeger, Bernhard
- KonferenzbeitragPost-Debugging in Large Scale Big Data Analytic Systems(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Bergen, Eduard; Edlich, StefanData scientists often need to fine tune and resubmit their jobs when processing a large quantity of data in big clusters because of a failed behavior of currently executed jobs. Consequently, data scientists also need to filter, combine, and correlate large data sets. Hence, debugging a job locally helps data scientists to figure out the root cause and increases efficiency while simplifying the working process. Discovering the root cause of failures in distributed systems involve a different kind of information such as the operating system type, executed system applications, the execution state, and environment variables. In general, log files contain this type of information in a cryptic and large structure. Data scientists need to analyze all related log files to get more insights about the failure and this is cumbersome and slow. Another possibility is to use our reference architecture. We extract remote data and replay the extraction on the developer’s local debugging environment.
- KonferenzbeitragA Deep Learning-based Approach for Banana Leaf Diseases Classification(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Amara, Jihen; Bouaziz, Bassem; Algergawy, AlsayedPlant diseases are important factors as they result in serious reduction in quality and quantity of agriculture products. Therefore, early detection and diagnosis of these diseases are important. To this end, we propose a deep learning-based approach that automates the process of classifying ba- nana leaves diseases. In particular, we make use of the LeNet architecture as a convolutional neural network to classify image data sets. The preliminary results demonstrate the effectiveness of the proposed approach even under challenging conditions such as illumination, complex background, different resolution, size, pose, and orientation of real scene images.
- KonferenzbeitragExperiences with the Model-based Generation of Big Data Pipelines(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Eichelberger, Holger; Qin, Cui; Schmid, KlausDeveloping Big Data applications implies a lot of schematic or complex structural tasks, which can easily lead to implementation errors and incorrect analysis results. In this paper, we present a model-based approach that supports the automatic generation of code to handle these repetitive tasks, enabling data engineers to focus on the functional aspects without being distracted by technical issues. In order to identify a solution, we analyzed different Big Data stream-processing frameworks, extracted a common graph-based model for Big Data streaming applications and de- veloped a tool to graphically design and generate such applications in a model-based fashion (in this work for Apache Storm). Here, we discuss the concepts of the approach, the tooling and, in particular, experiences with the approach based on feedback of our partners.
- KonferenzbeitragMining Industrial Logs for System Level Insights(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Czora, Sebastian; Dix, Marcel; Fromm, Hansjörg; Klöpper, Benjamin; Schmitz, BjörnIndustrial systems are becoming more and more complex and expensive to operate. Companies are making considerable efforts to increase operational efficiency and eliminate unplanned downtime of their equipment. Condition monitoring has been applied to improve equipment availability and reliability. Most of the condition monitoring applications, however, focus on single components, not on entire systems. The objective of this research was to demonstrate that a combination of visual analytics and association rule mining can be successfully used in a condition monitoring context on system level.
- Editiertes Buch
- KonferenzbeitragBTW 2017 Data Science Challenge (SDSC17)(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Waizenegger, Tim
- KonferenzbeitragMultimedia Similarity Search(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Seidl, Thomas
- KonferenzbeitragUnderstanding Trending Topics in Twitter(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Kahlert, Roland; Liebeck, Matthias; Cornelius, JosephMany events, for instance in sports, political events, and entertainment, happen all over the globe all the time. It is difficult and time consuming to notice all these events, even with the help of different news sites. We use tweets from Twitter to automatically extract information in order to understand hashtags of real-world events. In our paper, we focus on the topic identification of a hashtag, analyze the expressed positive, neutral, and negative sentiments of users, and further investigate the expressed emotions. We crawled English tweets from 24 hashtags and report initial investigation results.
- KonferenzbeitragVergleich und Evaluation von RDF-on-Hadoop-Lösungen(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Amann, WolfgangMit der steigenden Anzahl von Daten, welche in Form des Resource Description Frame- work (RDF) veröffentlicht werden entsteht eine Menge von Daten, bei der Datenoperationen nicht mehr von einem einzelnen Rechner zu bewältigen sind. In dieser Arbeit werden Systeme vorgestellt, welche zur Lösung dieses Problems das Hadoop-Framework ausschließlich bzw. in Kombination mit anderen Big-Data-Frameworks nutzen. Danach werden mit PigSPARQL und Rya zwei dieser Ansätze, welche exemplarisch für die neuere Entwicklung dieser RDF-on-Hadoop-Systeme stehen, anhand der Benchmark-Queries der Waterloo SPARQL Diversity Test Suite auf spezifische Stärken und Schwächen analysiert.