Odej Kao
Odej Kao
Professor of Computer Science, TU Berlin
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Citado por
Citado por
The stratosphere platform for big data analytics
A Alexandrov, R Bergmann, S Ewen, JC Freytag, F Hueske, A Heise, ...
The VLDB Journal 23, 939-964, 2014
Exploiting dynamic resource allocation for efficient parallel data processing in the cloud
D Warneke, O Kao
IEEE transactions on parallel and distributed systems 22 (6), 985-997, 2011
Nephele/PACTs: a programming model and execution framework for web-scale analytical processing
D Battré, S Ewen, F Hueske, O Kao, V Markl, D Warneke
Proceedings of the 1st ACM symposium on Cloud computing, 119-130, 2010
Scheduling in HPC resource management systems: Queuing vs. planning
M Hovestadt, O Kao, A Keller, A Streit
Job Scheduling Strategies for Parallel Processing: 9th International …, 2003
Nephele: efficient parallel data processing in the cloud
D Warneke, O Kao
Proceedings of the 2nd workshop on many-task computing on grids and …, 2009
Elastic stream processing with latency guarantees
B Lohrmann, P Janacik, O Kao
2015 IEEE 35th International Conference on Distributed Computing Systems …, 2015
Multiple service load-balancing with OpenFlow
M Koerner, O Kao
2012 IEEE 13th International Conference on High Performance Switching and …, 2012
Microrca: Root cause localization of performance issues in microservices
L Wu, J Tordsson, E Elmroth, O Kao
NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium, 1-9, 2020
Self-attentive classification-based anomaly detection in unstructured logs
S Nedelkoski, J Bogatinovski, A Acker, J Cardoso, O Kao
2020 IEEE International Conference on Data Mining (ICDM), 1196-1201, 2020
The virtual resource manager: An architecture for SLA-aware resource management
LO Burchard, M Hovestadt, O Kao, A Keller, B Linnert
IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid …, 2004
Towards ontology-driven P2P grid resource discovery
F Heine, M Hovestadt, O Kao
Fifth IEEE/ACM International Workshop on Grid Computing, 76-83, 2004
Anomaly detection from system tracing data using multimodal deep learning
S Nedelkoski, J Cardoso, O Kao
2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 179-186, 2019
Image fusion in remote sensing
T Bretschneider, O Kao
Proceedings of the 1st online symposium of electronic engineers, 1-8, 2000
Implicit parallelism through deep language embedding
A Alexandrov, A Kunft, A Katsifodimos, F Schüler, L Thamsen, O Kao, ...
Proceedings of the 2015 ACM SIGMOD International Conference on Management of …, 2015
Anomaly detection and classification using distributed tracing and deep learning
S Nedelkoski, J Cardoso, O Kao
2019 19th IEEE/ACM international symposium on cluster, cloud and grid …, 2019
MapReduce and PACT-comparing data parallel programming models
A Alexandrov, S Ewen, M Heimel, F Hueske, O Kao, V Markl, E Nijkamp, ...
Gesellschaft für Informatik eV, 2011
Massively parallel data analysis with pacts on nephele
A Alexandrov, M Heimel, V Markl, D Battré, F Hueske, E Nijkamp, S Ewen, ...
Proceedings of the VLDB Endowment 3 (1-2), 1625-1628, 2010
Retrieval of remotely sensed imagery using spectral information content
T Bretschneider, R Cavet, O Kao
IEEE International Geoscience and Remote Sensing Symposium 4, 2253-2255, 2002
Evaluating machine learning algorithms for anomaly detection in clouds
A Gulenko, M Wallschläger, F Schmidt, O Kao, F Liu
2016 IEEE International Conference on Big Data (Big Data), 2716-2721, 2016
Nephele streaming: stream processing under QoS constraints at scale
B Lohrmann, D Warneke, O Kao
Cluster computing 17, 61-78, 2014
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