P266 - BTW2017 - Datenbanksysteme für Business, Technologie und Web - Workshopband
Auflistung P266 - BTW2017 - Datenbanksysteme für Business, Technologie und Web - Workshopband nach Erscheinungsdatum
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- KonferenzbeitragScalable Cloud Data Management Workshop (SCDM 2017)(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Gessert, Felix; Ritter, Norbert
- 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.
- KonferenzbeitragPreserving Recomputability of Results from Big Data Transformation Workflows(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Kricke, Matthias; Grimmer, Martin; Schmeißer, MichaelThe ability to recompute results from raw data at any time is important for data-driven companies to ensure data stability and to selectively incorporate new data into an already delivered data product. When external systems are used or data changes over time this becomes even more challenging. In this paper, we propose a system architecture which ensures recomputability of results from big data transformation workflows on internal and external systems by using distributed key-value data stores.
- 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.
- KonferenzbeitragPossible Voter Control in k-Approval and k-Veto Under Partial Information(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Erdélyi, Gábor; Reger, ChristianWe study the complexity of possible constructive/destructive control by adding voters (PCCAV/PDCAV) and deleting voters (PCCDV/PDCDV) under nine different models of partial information for k-Approval and k-Veto. For the two destructive variants, we can further settle a polynomial-time result holding even for each scoring rule. Generally, in voter control, an external agent (called the chair) tries to change the outcome of the election by adding new voters to the elec- tion or by deleting voters from the election. Usually there is full information in voting theory, i.e., the chair knows the candidates, each voter’s complete ranking about the candidates and the voting rule used. In this paper, we assume the chair to have partial information about the votes and ask if the chair can add (delete) some votes so that his preferred (despised) candidate is (not) a winner for at least one completion of the partial votes to complete votes.
- KonferenzbeitragFake war crime image detection by reverse image search(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Askinadze, AlexanderIn the media, images of war crimes are often shared, which in reality come from other contexts or other war sites. In this paper an approach is proposed to detect duplicate or fake war crime images. For this, the bag of visual words model is used in conjunction with localized soft assignment coding and the k-nn classifier. For evaluation, a data set with 600 images of war crimes was crawled. Different distances and parameters were used for evaluation. Unmodified images can be recognized with this approach with 100% accuracy. Rotated and scaled images can also be detected with nearly 100% accuracy. Modifications like cropping or the combination of scaling and cropping ensure significantly smaller accuracy results. The run time was investigated and it was found that about 3000 images per second can be processed on an Intel Core i5 processor.
- KonferenzbeitragSurvey and Comparison of Open Source Time Series Databases(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Bader, Andreas; Kopp, Oliver; Falkenthal, MichaelTime series data, i.e., data consisting of a series of timestamps and corresponding values, is a special type of data occurring in settings such as “Smart Grids”. Extended analysis techniques called for a new type of databases: Time Series Databases (TSDBs), which are specialized for storing and querying time series data. In this work, we aim for a complete list of all available TSDBs and a feature list of popular open source TSDBs. The systematic search resulted in 83 TSDBs. The twelve most prominent found open source TSDBs are compared. Therefore, 27 criteria in six groups are defined: (i) Distribution/Clusterability, (ii) Functions, (iii) Tags, Continuous Calculation, and Long-term Storage, (iv) Granularity, (v) Interfaces and Extensibility, (vi) Support and License.
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- KonferenzbeitragTowards Complex User Feedback and Presentation Context in Recommender Systems(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Peska, Ladislav; Vojtas, PeterIn this paper, we present our work in progress towards employing complex user feedback and its context in recommender systems. Our work is generally focused on small or medium-sized e-commerce portals. Due to the nature of such enterprises, explicit feedback is unavailable, but implicit feedback can be collected in both large amount and rich variety. However, some perceived values of implicit feedback may depend on the context of the page or user’s device (further denoted as presentation context). In this paper, we present an extended model of presentation context, propose methods integrating it into the set of implicit feedback features and evaluate these on the dataset of real e-commerce users. The evaluation corroborated the importance of leveraging presentation context in recommender systems.
- KonferenzbeitragEnergy Efficiency in Main-Memory Databases(Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Noll, StefanAs the operating costs of today’s data centres continue to increase and processor man- ufacturers are forced to meet thermal design power constraints, the energy efficiency of a main-memory database management system becomes more and more important. In this paper, we experi- mentally study the impact of reducing the clock frequency of the processor on the energy efficiency of common database algorithms such as scans, simple aggregations, simple hash joins and state-of- the-art join algorithms. We stress the fundamental trade-off between peak performance and energy efficiency, as opposed to the established race-to-idle strategy. Ultimately, we learn that database workloads behave considerably different than other workload types and that reducing the computing power e.g. by limiting the clock frequency can significantly improve energy efficiency.