@article{AzodiChengMeinel2015,
  author    = {Azodi, Amir and Cheng, Feng and Meinel, Christoph},
  title     = {Event Driven Network Topology Discovery and Inventory Listing Using REAMS},
  series = {Wireless personal communications : an international journal},
  volume    = {94},
  journal   = {Wireless personal communications : an international journal},
  publisher = {Springer},
  address   = {New York},
  issn      = {0929-6212},
  doi       = {10.1007/s11277-015-3061-3},
  pages     = {415 -- 430},
  year      = {2015},
  abstract  = {Network Topology Discovery and Inventory Listing are two of the primary features of modern network monitoring systems (NMS). Current NMSs rely heavily on active scanning techniques for discovering and mapping network information. Although this approach works, it introduces some major drawbacks such as the performance impact it can exact, specially in larger network environments. As a consequence, scans are often run less frequently which can result in stale information being presented and used by the network monitoring system. Alternatively, some NMSs rely on their agents being deployed on the hosts they monitor. In this article, we present a new approach to Network Topology Discovery and Network Inventory Listing using only passive monitoring and scanning techniques. The proposed techniques rely solely on the event logs produced by the hosts and network devices present within a network. Finally, we discuss some of the advantages and disadvantages of our approach.},
  language  = {en}
}
@article{WeidlichZiekowGaletal.2014,
  author    = {Weidlich, Matthias and Ziekow, Holger and Gal, Avigdor and Mendling, Jan and Weske, Mathias},
  title     = {Optimizing event pattern matching using business process models},
  series = {IEEE transactions on knowledge and data engineering},
  volume    = {26},
  journal   = {IEEE transactions on knowledge and data engineering},
  number    = {11},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  issn      = {1041-4347},
  doi       = {10.1109/TKDE.2014.2302306},
  pages     = {2759 -- 2773},
  year      = {2014},
  abstract  = {A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.},
  language  = {en}
}