Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data

Spenrath, Yorick; Hassani, Marwan; Tariq, Haseeb

doi:10.1007/978-3-030-67670-4_20

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Recently, various studies investigated the application of existing process mining techniques, i.e., process discovery, conformance, or enhancement studies, on non-standard event data sources. Examples include, among various others, customer journey analysis [98], various applications in healthcare [99] and the analysis of sensor data [100]. In such contexts, the recorded data are often of a different level of granularity compared to the level at which one aims to analyze the process.…”

Section: Event Abstractionmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms.

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Section: Event Abstractionmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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On Inferring a Meaningful Similarity Metric for Customer Behaviour

Berg

Hassani

2021

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

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Online Prediction of Aggregated Retailer Consumer Behaviour

Spenrath

Hassani

2022

Lecture Notes in Business Information Processing

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Predicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.

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Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data

Cited by 4 publications

References 18 publications

Partial-order-based process mining: a survey and outlook

Partial-order-based process mining: a survey and outlook

On Inferring a Meaningful Similarity Metric for Customer Behaviour

Online Prediction of Aggregated Retailer Consumer Behaviour

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