Eliciting Evolving Topics, Trends and Foresight about Self-driving Cars Using Dynamic Topic Modeling

Ayele, Workneh Y.; Juell-Skielse, Gustaf

doi:10.1007/978-3-030-39445-5_37

Cited by 3 publications

(19 citation statements)

References 30 publications

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“…3) Design and development: Each phase of the CRISP-IM is customized by mapping it with the DTM process carried out to identify emerging trends in [52]. Additionally, the key elements of the phases of the CRISP-IM are inspired by the CRISP-DM [2] model.…”

Section: ) Identification Of the Problemmentioning

confidence: 99%

“…The running example is based on the DTM and the succeeding statistical analysis to demonstrate and motivate parts of the CRISP-IM. Also, the components of each phase of CRISP-IM are motivated from previous research work that involves data collection, preprocessing, selecting the best machine learning model, generating topics, identifying trends, and interpreting the work as presented in [52].…”

Section: ) Demonstrationmentioning

confidence: 99%

“…Moreover, it is possible to use scientometric, machine learning, and visual analytics to elicit trends and temporal patterns [54]. 2) Demonstration: The goal of the running example [52] is to identify emerging trends and patterns to elicit innovative ideas. Hence, [52] identified what could be gained by understanding the project.…”

Section: How Can the Activities In This Phase Be Done?mentioning

confidence: 99%

“…2) Demonstration: The goal of the running example [52] is to identify emerging trends and patterns to elicit innovative ideas. Hence, [52] identified what could be gained by understanding the project.…”

Section: How Can the Activities In This Phase Be Done?mentioning

confidence: 99%

“…The analysis of cost-benefit, risk, and contingencies was not done as the purpose of the work by [52] is academic research.…”

Section: Ghzmentioning

confidence: 99%

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Adapting CRISP-DM for Idea Mining

Ayele¹

2020

IJACSA

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Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study.

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Section: ) Identification Of the Problemmentioning

confidence: 99%

Section: ) Demonstrationmentioning

confidence: 99%

Section: How Can the Activities In This Phase Be Done?mentioning

confidence: 99%

Section: How Can the Activities In This Phase Be Done?mentioning

confidence: 99%

“…The analysis of cost-benefit, risk, and contingencies was not done as the purpose of the work by [52] is academic research.…”

Section: Ghzmentioning

confidence: 99%

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Adapting CRISP-DM for Idea Mining

Ayele¹

2020

IJACSA

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A Process Model for Generating and Evaluating Ideas: The Use of Machine Learning and Visual Analytics to Support Idea Mining

Ayele

Juell-Skielse

2020

Electronic Government and the Information Systems Perspective

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The significance and possibilities of idea generation and evaluation are increasing due to the increasing demands for digital innovation and the abundance of textual data. Textual data such as social media, publications, patents, documents, etc. are used to generate ideas, yet manual analysis is affected by bias and subjectivity. Machine learning and visual analytics tools could be used to support idea generation and evaluation, referred to as idea mining, to unlock the potential of voluminous textual data. Idea mining is applied to support the extraction of useful information from textual data. However, existing literature merely focuses on the outcome and overlooks structuring and standardizing the process itself. In this paper, to support idea mining, we designed a model following design science research, which overlaps with the Cross-Industry-Standard-Process for Data Mining (CRISP-DM) process and adapts well-established models for technology scouting. The proposed model separates the duties of actors in idea mining into two layers. The first layer presents the business layer, where tasks performed by technology scouts, incubators, accelerators, consultants, and contest managers are detailed. The second layer presents the technical layer where tasks performed by data scientists, data engineers, and similar experts are detailed overlapping with CRISP-DM. For future research, we suggest an ex-post evaluation and customization of the model to other techniques of idea mining.

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A Systematic Literature Review about Idea Mining: The Use of Machine-Driven Analytics to Generate Ideas

Ayele

Juell-Skielse

2021

Advances in Intelligent Systems and Computing

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Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally serve as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored.

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Eliciting Evolving Topics, Trends and Foresight about Self-driving Cars Using Dynamic Topic Modeling

Cited by 3 publications

References 30 publications

Adapting CRISP-DM for Idea Mining

Adapting CRISP-DM for Idea Mining

A Process Model for Generating and Evaluating Ideas: The Use of Machine Learning and Visual Analytics to Support Idea Mining

A Systematic Literature Review about Idea Mining: The Use of Machine-Driven Analytics to Generate Ideas

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