A survey of trace exploration tools and techniques
CASCON '04 Proceedings of the 2004 conference of the Centre for Advanced Studies on Collaborative research
Understanding Execution Traces Using Massive Sequence and Circular Bundle Views
ICPC '07 Proceedings of the 15th IEEE International Conference on Program Comprehension
Semantics-aware trace analysis
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Instruction Sequential Pattern Mining in Hardware Sample Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
ICECCS '11 Proceedings of the 2011 16th IEEE International Conference on Engineering of Complex Computer Systems
The Concept of Stratified Sampling of Execution Traces
ICPC '11 Proceedings of the 2011 IEEE 19th International Conference on Program Comprehension
Signature Pattern Covering via Local Greedy Algorithm and Pattern Shrink
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
The long and the short of it: summarising event sequences with serial episodes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
USpan: an efficient algorithm for mining high utility sequential patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct Discovery of High Utility Itemsets without Candidate Generation
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
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The analysis of multimedia application traces can reveal important information to enhance program execution comprehension. However typical size of traces can be in gigabytes, which hinders their effective exploitation by application developers. In this paper, we study the problem of finding a set of sequences of events that allows a reduced-size rewriting of the original trace. These sequences of events, that we call blocks, can simplify the exploration of large execution traces by allowing application developers to see an abstraction instead of low-level events. The problem of computing such set of blocks is NP-hard and naive approaches lead to prohibitive running times that prevent analysing real world traces. We propose a novel algorithm that directly mines the set of blocks. Our experiments show that our algorithm can analyse real traces of up to two hours of video. We also show experimentally the quality of the set of blocks proposed, and the interest of the rewriting to understand actual trace data.