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Automated interpretation and reduction of in-vehicle network traces at a large scale

Published: 24 June 2018 Publication History

Abstract

In modern vehicles, high communication complexity requires cost-effective integration tests such as data-driven system verification with in-vehicle network traces. With the growing amount of traces, distributable Big Data solutions for analyses become essential to inspect massive amounts of traces. Such traces need to be processed systematically using automated procedures, as manual steps become infeasible due to loading and processing times in existing tools. Further, trace analyses require multiple domains to verify the system in terms of different aspects (e.g., specific functions) and thus, require solutions that can be parameterized towards respective domains. Existing solutions are not able to process such trace amounts in a flexible and automated manner. To overcome this, we introduce a fully automated and parallelizable end-to-end preprocessing framework that allows to analyze massive in-vehicle network traces. Being parameterized per domain, trace data is systematically reduced and extended with domain knowledge, yielding a representation targeted towards domain-specific system analyses. We show that our approach outperforms existing solutions in terms of execution time and extensibility by evaluating our approach on three real-world data sets from the automotive industry.

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Cited By

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  • (2019)Learning Temporal Specifications from Imperfect Traces Using Bayesian InferenceProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317847(1-6)Online publication date: 2-Jun-2019
  • (2019)Context by ProxyProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330780(2059-2068)Online publication date: 25-Jul-2019
  • (2018)Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional GroupingMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10997-4_6(86-102)Online publication date: 10-Sep-2018

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cover image ACM Conferences
DAC '18: Proceedings of the 55th Annual Design Automation Conference
June 2018
1089 pages
ISBN:9781450357005
DOI:10.1145/3195970
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 24 June 2018

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Author Tags

  1. automotive
  2. big data
  3. data mining
  4. data-driven verification
  5. in-vehicle network traces
  6. trace analysis

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DAC '18
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DAC '18: The 55th Annual Design Automation Conference 2018
June 24 - 29, 2018
California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

View all
  • (2019)Learning Temporal Specifications from Imperfect Traces Using Bayesian InferenceProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317847(1-6)Online publication date: 2-Jun-2019
  • (2019)Context by ProxyProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330780(2059-2068)Online publication date: 25-Jul-2019
  • (2018)Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional GroupingMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10997-4_6(86-102)Online publication date: 10-Sep-2018

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