research-article

Natural language querying of process execution data

Authors:

Meriana Kobeissi,

Boualem Benatallah,

Bassem HaidarAuthors Info & Claims

Volume 116, Issue C

https://doi.org/10.1016/j.is.2023.102227

Published: 01 June 2023 Publication History

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.

Highlights

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We proposed a natural language interface for querying process execution data from natural language.

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We presents a Labeled Graph metamodel for stroing process data.

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We proposed a hybrid pipline to automatically constructing Cypher queries from natural language.

•

Our NLI system is hybrid and combines machine learning and rule-based approaches.

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We defined a set of general intent patterns that are domain-independent.

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We evaluated the proposed system with more than 530 natural language queries.

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cover image Information Systems

Information Systems Volume 116, Issue C

Jun 2023

174 pages

ISSN:0306-4379

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Elsevier Ltd.

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 June 2023

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