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• W4377030752 startingPage "102227" @default.
• W4377030752 abstract "Process-oriented data analysis techniques allow organizations to understand how their processes operate, where modifications are needed and where enhancements are possible. A recurrent task in any process analysis technique is querying. Process data querying allows analysts to easily explore the data with the intent of getting insights about the execution of business processes. The current generation of process query languages targets data scientists. However, there is a need to a query language to support domain analysts who may be inexperienced with database technologies. This paper addresses this challenge by proposing a natural language interface that assists the end-users in querying the stored event data. The interface takes a natural language query from the user, automatically constructs a corresponding structured query to be executed over the stored event data. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using two publicly available event logs." @default.
• W4377030752 created "2023-05-19" @default.
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• W4377030752 date "2023-06-01" @default.
• W4377030752 modified "2023-10-11" @default.
• W4377030752 title "Natural language querying of process execution data" @default.
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• W4377030752 doi "https://doi.org/10.1016/j.is.2023.102227" @default.
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