Using cloud technologies to optimize data-intensive service applications
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
The role of data analytics increases in several application domains to cope with the large amount of captured data. Generally, data analytics are data-intensive processes, whose efficient execution is a challenging task. Each process consists of a collection of related structured activities, where huge data sets have to be exchanged between several loosely coupled services. The implementation of such processes in a service-oriented environment offers some advantages, but the efficient realization of data flows is difficult. Therefore, we use this paper to propose a novel SOA-aware approach with a special focus on the data flow. The tight interaction of new cloud technologies with SOA technologies enables us to optimize the execution of data-intensive service applications by reducing the data exchange tasks to a minimum. Fundamentally, our core concept to optimize the data flows is found in data clouds. Moreover, we can exploit our approach to derive efficient process execution strategies regarding different optimization objectives for the data flows.
Details
| Original language | English |
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| Title of host publication | 2010 IEEE 3rd International Conference on Cloud Computing |
| Pages | 19-26 |
| Number of pages | 8 |
| ISBN (electronic) | 978-0-7695-4130-3 |
| Publication status | Published - 2010 |
| Peer-reviewed | Yes |
Publication series
| Series | IEEE International Conference on Cloud Computing, CLOUD |
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| ISSN | 2159-6182 |
Conference
| Title | 3rd IEEE International Conference on Cloud Computing, CLOUD 2010 |
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| Duration | 5 - 10 July 2010 |
| City | Miami, FL |
| Country | United States of America |
External IDs
| ORCID | /0000-0001-8107-2775/work/200630395 |
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| ORCID | /0000-0002-3513-6448/work/200630907 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Subject groups, research areas, subject areas according to Destatis
ASJC Scopus subject areas
Keywords
- Data cloud, Data-intensive, Service applications