18,883 research outputs found
Optimizing Abstract Abstract Machines
The technique of abstracting abstract machines (AAM) provides a systematic
approach for deriving computable approximations of evaluators that are easily
proved sound. This article contributes a complementary step-by-step process for
subsequently going from a naive analyzer derived under the AAM approach, to an
efficient and correct implementation. The end result of the process is a two to
three order-of-magnitude improvement over the systematically derived analyzer,
making it competitive with hand-optimized implementations that compute
fundamentally less precise results.Comment: Proceedings of the International Conference on Functional Programming
2013 (ICFP 2013). Boston, Massachusetts. September, 201
Turning Logs into Lumber: Preprocessing Tasks in Process Mining
Event logs are invaluable for conducting process mining projects, offering
insights into process improvement and data-driven decision-making. However,
data quality issues affect the correctness and trustworthiness of these
insights, making preprocessing tasks a necessity. Despite the recognized
importance, the execution of preprocessing tasks remains ad-hoc, lacking
support. This paper presents a systematic literature review that establishes a
comprehensive repository of preprocessing tasks and their usage in case
studies. We identify six high-level and 20 low-level preprocessing tasks in
case studies. Log filtering, transformation, and abstraction are commonly used,
while log enriching, integration, and reduction are less frequent. These
results can be considered a first step in contributing to more structured,
transparent event log preprocessing, enhancing process mining reliability.Comment: Accepted by EdbA'23 workshop, co-located with ICPM 202
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