Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Christoph Hochreiner"'
Publikováno v:
PeerJ Computer Science, Vol 3, p e141 (2017)
The continuous increase of unbound streaming data poses several challenges to established data stream processing engines. One of the most important challenges is the cost-efficient enactment of stream processing topologies under changing data volume.
Externí odkaz:
https://doaj.org/article/acab2f5f95f14b4fa46fa3e48c15564b
Publikováno v:
IEEE Transactions on Cloud Computing. 9:1657-1674
Business Process Management Systems (BPMS) need to be able to take into account the fluctuating demand for computational resources during the execution of business process activities. Today, BPMS rely on the leasing and releasing of virtual machines
Publikováno v:
Proceedings of the VLDB Endowment. 12:724-737
Elastic distributed stream processing systems are able to dynamically adapt to changes in the workload. Often, these systems react to the rate of incoming data, or to the level of resource utilization, by scaling up or down. The goal is to optimize t
Autor:
Philipp Waibel, Christoph Hochreiner, Stefan Schulte, Ronny Hans, Olena Skarlat, Johannes Matt
Publikováno v:
Service Oriented Computing and Applications. 11:411-426
The use of cloud-based storage systems for storing data is a popular alternative to local storage systems. Beside several benefits of cloud-based storages, there are also downsides like vendor lock-in or unavailability. Moreover, the selection of the
Publikováno v:
Informatik-Spektrum 42 (4): 256-265 (2019-08)
In volatile data streams as encountered in the Internet of Things (IoT), the data volume to be processed changes permanently. Hence, to ensure timely data processing, there is a need to reconfigure the computational resources used for processing data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a06f887a300991c811a39cac5258e81
https://hdl.handle.net/11420/11898
https://hdl.handle.net/11420/11898
Publikováno v:
ICFEC
2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)
2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)
Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the clou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be6092c0639a3757a6989fc69e7d3bd2
Publikováno v:
BigDataService
Stream processing systems are able to integrate data from various sources, and to invoke self-hosted and external operators. In case of faults, such systems usually rely on the redundancy of single stream processing operators, while the relationship
Publikováno v:
IEEE Transactions on Services Computing. 9:700-713
Business Process Management is a matter of great importance in different industries and application areas. In many cases, it involves the execution of resource-intensive tasks in terms of computing power such as CPU and RAM. Due to the emergence of C
Publikováno v:
IEEE Internet Computing. 19:54-59
The current development towards the Internet of Things introduces the need for more flexibility in stream processing. To counter these challenges, the authors propose elastic stream processing for distributed environments, building on top of cloud co
Autor:
Jan Mendling, Stefan Schulte, Christoph Hochreiner, Philipp Waibel, Michael Borkowski, Svetoslav Videnov
Publikováno v:
INDIN
Cloud manufacturing supports companies to create cross-organizational elastic process landscapes with flexible and scalable processes. In recent years, the Business Process Model and Notation language gained interest in the cloud manufacturing domain