Literature Review about Intention Mining in Information Systems

Diaz, Oswaldo; Pérez, María Jesús Verdú; Lascano, Jorge Edison

doi:10.1080/08874417.2019.1633569

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…According to the literature reviews performed between 2010 and 2018 (Diaz et al, 2019) (Ghasemi & Amyot, 2020), each study has implemented its own methodology. But most research have been made by three technologies groups.…”

Section: Intention Miningmentioning

confidence: 99%

Intention process mining using a context-aware Hidden Markov Model

Choukri

Guermah

Hafiddi

et al. 2023

Preprint

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The emerging availability of digital devices that can be utilized for activity tracking and context sensing opened new opportunities for context awareness and user intention recognition. Mainly, the opportunity to use generated data during user operational process execution and understand the intention behind its behavior under a given context. The hidden Markov model (HMM) has been widely used in many fields, such as speech recognition and computational biology. It can be seen as a class of stochastic processes that have a finite-state structure. It then offers a good promise for their applications in process mining. Then, much research has been done to build generic process models based on the study of user behavior captured during the implementation of operational processes. But they merely considered the relationship between the observed activities and their sequences. They ignored the implicit intention and the surrounding context, conditioning the user’s behavior when triggering the actual process. Consequently, the objective of this research was twofold. First, we specify a context HMM for intention mining in an unsupervised manner. Secondly, we upgrade the resulting model within context awareness property. Finally, we evaluated these models in a case study with a travel activity dataset. The experiments revealed that intention mining within a context-aware model had better precision in discovering the correct intentions.

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Section: Intention Miningmentioning

confidence: 99%

Intention process mining using a context-aware Hidden Markov Model

Choukri

Guermah

Hafiddi

et al. 2023

Preprint

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“…By comparing the current comparison framework used in the field of software engineering, the available literature survey was analysed in order to develop research issues. [6,7], Information Systems [8], event-log based approach [9], Business related online reviews [10]. RQ1: What is intention mining?…”

Section: Define Research Questionsmentioning

confidence: 99%

An Intensive Spectrum for Intention Mining Analysis

Varsha D. Jadhav

2023

IJRITCC

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There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining.

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Domain Ontology Construction with Activity Logs and Sensors Data – Case Study of Smart Home Activities

Elali

Kornyshova

Deneckère

et al. 2022

Lecture Notes in Business Information Processing

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Process mining relies on activity logs to discover process models, check their conformance, enhance processes, and recommend the next activity. On another side, many environmental factors such as time, location, weather, and profile are obtained from many sources, such as sensors, external systems, outside actors, or domain knowledge bases, and could also enhance recommendations. The existing research mainly focuses on single activity log datasets; only a few consider combining various sources. Our main goal is to provide better inputs to process discovery and better recommendations. In this paper, we focus on the combination of activity logs and sensors data with domain ontology as an intermediate step to attaining our goal. We use a case study of smart home activities to test this combination.

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Literature Review about Intention Mining in Information Systems

Cited by 8 publications

References 22 publications

Intention process mining using a context-aware Hidden Markov Model

Intention process mining using a context-aware Hidden Markov Model

An Intensive Spectrum for Intention Mining Analysis

Domain Ontology Construction with Activity Logs and Sensors Data – Case Study of Smart Home Activities

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