P290 - BTW2019 - Datenbanksysteme für Business, Technologie und Web - Workshopband
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- TextdokumentDie Data Science Challenge auf der BTW 2019 in Rostock(BTW 2019 – Workshopband, 2019) Grunert, Hannes; Meyer, HolgerZum zweiten Mal — nach der BTW 2017 in Stuttgart [Wa17] — findet auf der BTW-Konferenzreihe die Data Science Challenge statt. Die Teilnehmer der Challenge hatten die Möglichkeit, ihren eigenen Ansatz zur cloud-basierten Datenanalyse zu entwickeln und damit im direkten Vergleich gegen andere Teilnehmer anzutreten.
- TextdokumentWorkshop on Big (and Small) Data in Science and Humanities (BigDS 2019)(BTW 2019 – Workshopband, 2019) Klan, Friederike; König-Ries, Birgitta; Reimann, Peter; Seeger, Bernhard; Groß, Anika
- TextdokumentAssessing the Impact of Driving Bans with Data Analysis(BTW 2019 – Workshopband, 2019) Woltmann, Lucas; Hartmann, Claudio; Lehner, Wolfgang
- TextdokumentPrediction of air pollution with machine learning(BTW 2019 – Workshopband, 2019) Schmitz, Christian; Serai, Dhiren Devinder; Escobar Gava, TatianeCities worldwide are facing air quality issues, leading to bans of vehicles and lower quality of life for inhabitants. We forecast the air quality for Stuttgart based on expected weather condition. For that purpose, we extract, cleanse, and integrate the DHT22 and SDS11 sensors’ data to feed two different machine learning models for predicting the particulate matter values for the near future.
- TextdokumentLock-free Data Structures for Data Stream Processing(BTW 2019 – Workshopband, 2019) Baumstark, AlexanderThe ever-growing amounts of data in the digital world require more and more computing power to meet the requirements. Especially in the area of social media, sensor data processing or Internet of Things, the data need to be handled on the fly during its creation. A common way to handle these data, in form of endless data streams, is the data stream processing technology. The key requirements for data stream processing are high throughput and low latency. These requirements can be accomplished with the parallelization of operators and multithreading. However, in order to realize a higher degree of parallelism, the efficient synchronization of threads is a necessity. This work examines the design principles of lock-free data structures and how this synchronization method can improve the performance of algorithms in data stream processing. For this purpose, lock-free data structures are implemented for the data stream processing engine Pipefabric and compared with current implementations. The result is an improvement for the tuple exchanging between threads and a significant improvement for the symmetric hash join algorithm based on lock-free hash maps.
- TextdokumentWorkshop Digitale Lehre im Fach Datenbanken(BTW 2019 – Workshopband, 2019) Rakow, Thomas C.; Faeskorn-Woyke, Heide
- TextdokumentA Comparison of Distributed Stream Processing Systems for Time Series Analysis(BTW 2019 – Workshopband, 2019) Gehring, Melissa; Charfuelan, Marcela; Markl, VolkerGiven the vast number of data processing systems available today, in this paper, we aim to identify, select, and evaluate systems to determine the one that is better suited to use in conducting time series analysis. Published studies of performance are used to compare several open-source systems, and two systems are further selected for qualitative comparison and evaluation regarding the development of a time series analytics task. The main interest of this work lies in the investigation of the Ease of development. As a test scenario, a discrete Kalman filter is implemented to predict the closing price of stock market data in real-time. Basic functionality coverage is considered, and advanced functionality is evaluated using several qualitative comparison criteria.
- TextdokumentTemporal Graph Analysis using Gradoop(BTW 2019 – Workshopband, 2019) Rost, Christopher; Thor, Andreas; Rahm, ErhardThe temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We therefore have started with extending Gradoop for temporal graph analysis by adding time properties to vertices, edges and graphs and using them within graph operators. We outline these extensions and show their use in a bibliographic scenario to analyze temporal citation patterns.
- TextdokumentWorkload-Driven Data Placement for GPU-Accelerated Database Management Systems(BTW 2019 – Workshopband, 2019) Schmidt, Christopher; Uflacker, MatthiasAn increase in the memory capacity of current Graphics Processing Unit (GPU) generations and advances in multi-GPU systems enables a large unified GPU memory space to be utilized by modern coprocessor-accelerated Database Management System (DBMS). We take this as an opportunity to revisit the idea of using GPU memory as a hot cache for the DBMS. In particular, we focus on the data placement for the hot cache. Based on previous approaches and their shortcomings, we present a new workload-driven data placement for a GPU-accelerated DBMS. Lastly, we outline how we aim to implement and evaluate our proposed approach by comparing it to existing data placement approaches in future work.
- TextdokumentNoSQL & Real-Time Data Management in Research & Practice(BTW 2019 – Workshopband, 2019) Wingerath, Wolfram; Gessert, Felix; Ritter, NorbertUsers have come to expect reactivity from mobile and web applications, i.e. they assume that changes made by other users become visible immediately. However, developers are challenged with building reactive applications on top of traditional pull-oriented databases, because they are ill-equipped to push new information to the client. Systems for data stream management and processing, on the other hand, are natively push-oriented and thus facilitate reactive behavior, but they do not follow the same collection-based semantics as traditional databases: Instead of database collections, stream-oriented systems are based on a notion of potentially unbounded sequences of data items. In this tutorial, we survey and categorize the system space between pull-oriented databases and push-oriented stream management systems, using their respectively facilitated means of data retrieval as a reference point. We start with an in-depth survey of the most relevant NoSQL databases to provide a comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling, and querying characteristics. We present how each system’s design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges. A particular emphasis lies on the novel system class of real-time databases which combine the push-based access paradigm of stream-oriented systems with the collection-based query semantics of traditional databases. We explore why real-time databases deserve distinction in a separate system class and dissect their different architectures to highlight issues, derive open challenges, and discuss avenues for addressing them.