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Philipp M. Grulich

Publications and source records attributed to Philipp M. Grulich.

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The NebulaStream Platform: Data and Application Management for the Internet of Things

The Internet of Things (IoT) presents a novel computing architecture for data management: a distributed, highly dynamic, and heterogeneous environment of massive scale. Applications for the IoT introduce new challenges for integrating the concepts of fog and cloud computing as well as sensor networks in one unified environment. In this paper, we highlight these major challenges and outline how existing systems handle them. To address these challenges, we introduce the NebulaStream platform, a general purpose, endto-end data management system for the IoT. NebulaStream addresses the heterogeneity and distribution of compute and data, supports diverse data and programming models going beyond relational algebra, deals with potentially unreliable communication, and enables constant evolution under continuous operation. In our evaluation, we demonstrate the effectiveness of our approach by providing early results on partial aspects.

cs.DC

Scalable real-time processing with Spark Streaming: implementation and design of a Car Information System

Streaming data processing is a hot topic in big data these days, because it made it possible to process a huge amount of events within a low latency. One of the most common used open-source stream processing platforms is Spark Streaming, which is demonstrated and discussed based on a real-world use-case in this paper. The use-case is about a Car Information System, which is an example for a classic stream processing system. First the System is de- signed and engineered, whereby the application architecture is created carefully, because it should be adaptable for similar use-cases. At the end of this paper the CIS and Spark Streaming is evaluated by the use of the Goal Question Metric model. The evaluation proves that Spark Streaming is capable to create stream processing in a scalable and fault tolerant manner. But it also shows that Spark is a very fast moving project, which could cause problems during the development and maintenance of a software project.

cs.DB