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Transactions on large-scale data and knowledge-centered systems : XI Special Issue on Advanced Data Stream Management and Continuous Query Processing
This, the 11th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five selected papers focusing on Advanced Data Stream Management and Processing of Continuous Queries. The contributions cover different methods for avoiding unauthorized access to streaming data, mode...
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| Autres auteurs : | , , , , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Transactions on large-scale data and knowledge-centered systems. XI : Special Issue on Advanced Data Stream Management and Continuous Query Processing / edited by Abdelkader Hameurlain, Josef Küng, Roland Wagner, Bernd Amann, Philippe Lamarre. |
| Édition : | 1st ed. 2013. |
| Publié : |
Berlin, Heidelberg :
Springer Berlin Heidelberg
, [20..] Cham : Springer Nature |
| Collection : | Transactions on large-scale data- and knowledge-centered systems (Online) ; 8290 |
| Accès en ligne : |
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| Condition d'utilisation et de reproduction : | Conditions particulières de réutilisation pour les bénéficiaires des licences nationales : https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |
| Contenu : | ASSIST: Access Controlled Ship Identification Streams. The HIT Model: Workflow-Aware Event Stream Monitoring. P-Bench: Benchmarking in Data-Centric Pervasive Application Development. Efficient Mining of Lag Patterns in Evolving Time Series. On the Power of the Adversary to Solve the Node Sampling Problem |
| Sujets : | |
| Documents associés : | Autre format:
Transactions on Large-Scale Data- and Knowledge-Centered Systems XI |
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| 330 | |a This, the 11th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five selected papers focusing on Advanced Data Stream Management and Processing of Continuous Queries. The contributions cover different methods for avoiding unauthorized access to streaming data, modeling complex real-time behavior of stream processing applications, comparing different event-centric and data-centric platforms for the development of applications in pervasive environments, capturing localized repeated associative relationships from multiple time series, and obtaining uniform and fresh sampling strategies over input data streams generated by large open systems containing malicious participants | ||
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