http://dl.gi.de/handle/20.500.12116/21091 2026-07-23T11:13:44Z http://dl.gi.de/handle/20.500.12116/944 A Deep Learning-based Approach for Banana Leaf Diseases Classification Amara, Jihen; Bouaziz, Bassem; Algergawy, Alsayed Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Plant 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. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/942 Post-Debugging in Large Scale Big Data Analytic Systems Bergen, Eduard; Edlich, Stefan Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Data 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. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/943 Workshop Big (and small) Data in Science and Humanities (BigDS17) Groß, Anika; König-Ries, Birgitta; Reimann, Peter; Seeger, Bernhard Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/940 Experiences with the Model-based Generation of Big Data Pipelines Eichelberger, Holger; Qin, Cui; Schmid, Klaus Mitschang, Bernhard; Nicklas, Daniela; Leymann, Frank; Schöning, Harald; Herschel, Melanie; Teubner, Jens; Härder, Theo; Kopp, Oliver; Wieland, Matthias Developing 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. 2017-01-01T00:00:00Z