By Chris Ackermann, Rance Cleaveland, Samuel Huang, Arnab Ray, Charles Shelton (auth.), Howard Barringer, Ylies Falcone, Bernd Finkbeiner, Klaus Havelund, Insup Lee, Gordon Pace, Grigore Roşu, Oleg Sokolsky, Nikolai Tillmann (eds.)
This e-book constitutes the completely refereed convention complaints of the 1st foreign convention on Runtime Verification, RV 2010, held in St. Julians, Malta, in November 2010. The 23 revised complete papers offered including 6 invited papers, 6 tutorials and four device demonstrations have been rigorously reviewed and chosen from seventy four submissions. The papers tackle a variety of themes resembling runtime tracking, research and verification, statically and dynamical, runtime simulations, including purposes in malware research and failure restoration, in addition to execution tracing in embedded systems.
Read or Download Runtime Verification: First International Conference, RV 2010, St. Julians, Malta, November 1-4, 2010. Proceedings PDF
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Additional resources for Runtime Verification: First International Conference, RV 2010, St. Julians, Malta, November 1-4, 2010. Proceedings
Example text
4 Basic Model for Tracing Streaming Applications As mentioned in Section 1, finding the cause of unexpected or incorrect results in streaming applications can be challenging since cause and effect can be located in different operators of the distributed application and be disjoint in time. For example, a particular operator might have a computational bug, which is noticed only when an incorrect output tuple generated by that operator is processed further downstream by another operator. Moreover, massive amounts of fast moving data in streaming applications can make it difficult for a user to isolate and retrieve the causal paths, in particular, because of the interweaving patterns created by correlation and aggregation operators.
In our earlier work [11, 12] we proposed efficient techniques to track data and explored a model-based solution for data provenance in stream computing platforms. In a nutshell, our approach compresses the meta-data needed to track data provenance into a set of rules capturing input-output dependencies. The system that we developed is limited to backward provenance queries. Furthermore, the capture of provenance meta-data is severely limited by the capacity of the underlying storage system where the traces are persisted.
Hence, one can infer that the provenance of intermediate and final results can be clouded by operations like joins and aggregations. ). In this paper, we will focus primarily on dependencies caused by the regular stream data flow. The complexity of data dependencies can grow quickly for any non-trivial streaming application. The key to our visual debugging techniques is to trace and process information that allows the reconstruction and filtering of the data dependency paths that are of interest to the user.