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Multi-scale navigation of large trace data: A survey

Naser Ezzati-Jivan and Michel Dagenais

Article (2017)

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Dynamic analysis through execution traces is frequently used to analyze the runtime behavior of software systems. However, tracing long running executions generates voluminous data, which are complicated to analyze and manage. Extracting interesting performance or correctness characteristics out of large traces of data from several processes and threads is a challenging task. Trace abstraction and visualization are potential solutions to alleviate this challenge. Several efforts have been made over the years in many subfields of computer science for trace data collection, maintenance, analysis, and visualization. Many analyses start with an inspection of an overview of the trace, before digging deeper and studying more focused and detailed data. These techniques are common and well supported in geographical information systems, automatically adjusting the level of details depending on the scale. However, most trace visualization tools operate at a single level of representation, which are not adequate to support multilevel analysis. Sophisticated techniques and heuristics are needed to address this problem. Multi-scale (multilevel) visualization with support for zoom and focus operations is an effective way to enable this kind of analysis. Considerable research and several surveys are proposed in the literature in the field of trace visualization. However, multi-scale visualization has yet received little attention. In this paper, we provide a survey and methodological structure for categorizing tools and techniques aiming at multi-scale abstraction and visualization of execution trace data and discuss the requirements and challenges faced to be able to meet evolving user demands.

Uncontrolled Keywords

Data abstraction ; data visualization ; multilevel data analysis ; trace analysis

Subjects: 2700 Information technology > 2700 Information technology
Department: Department of Computer Engineering and Software Engineering
Funders: CRSNG/NSERC, Ericsson Software Research, Development Canada
Grant number: CRDPJ468687-14
PolyPublie URL: https://publications.polymtl.ca/2980/
Journal Title: Concurrency and Computation: Practice and Experience (vol. 29, no. 10)
Publisher: Wiley
DOI: 10.1002/cpe.4068
Official URL: https://doi.org/10.1002/cpe.4068
Date Deposited: 12 Feb 2018 16:55
Last Modified: 13 May 2023 15:06
Cite in APA 7: Ezzati-Jivan, N., & Dagenais, M. (2017). Multi-scale navigation of large trace data: A survey. Concurrency and Computation: Practice and Experience, 29(10), 1-20. https://doi.org/10.1002/cpe.4068


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