Big Data Analytics for Improving Fidelity of Engineering Design Decisions

Steingrimsson, Baldur; Yi, Sung; Jones, Robert; Kisialiou, M.; Yi, Kwan; Rose, Zachary

doi:10.4271/2018-01-1200

Cited by 4 publications

(10 citation statements)

References 7 publications

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“…Business need assessment can be done through the use of corporate foresight [23] and requirement elicitation. Topic modeling also enables the identification of valuable insights [26]. Forecasting improves creative performance as part of idea generation [53].…”

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

confidence: 99%

“…Predicting trends through time series analysis when enough time series data is available -Insights and foresight generated using text analytics, topic modeling, can be used to elicit ideas [26]. Similarly, it is possible to elicit trends and identify temporal patterns using machine learning, scientometric, and visual analytics [54].…”

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

confidence: 99%

“…Eliciting ideas and align assessment of data mining results with goals and success criteria specified in Phase 1 -Elicitation of ideas can be done by using topic modeling [26] and using association of terms found in publications and patents [23]. Evolving trends identified from topics can be as inputs for decision making in research and real-world technology [28].…”

Section: Running Correlation Tests On Candidate Terms -mentioning

confidence: 99%

“…For example, topic modeling, a type of unsupervised machine learning, can be applied to identify hidden topics [25], and ideas [11]. Also, a topic modeling technique, such as Latent Semantic Analysis (LSA) is used to predict the relevance of ideas and provide valuable insights [26]. According to [27], the elicitation and analysis of trends can be helpful for decision-makers and stakeholders in academia and the industry [27].…”

Section: Introductionmentioning

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: 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%

Section: Running Correlation Tests On Candidate Terms -mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adapting CRISP-DM for Idea Mining

Ayele¹

2020

IJACSA

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“…Changes in technology affect the way idea generation is performed [17]. For example, a machine learning technique, such as topic modeling, is used to elicit insights and evaluate the significance of ideas [18]. Also, unsupervised clustering of text, such as Latent Dirichlet Allocation (LDA) and Dynamic Topic Modelling (DTM) techniques (c.f.…”

Section: Introductionmentioning

confidence: 99%

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

Ayele

Juell-Skielse

2020

Advances in Intelligent Systems and Computing

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Big Data Analytics for Improving Fidelity of Engineering Design Decisions

Cited by 4 publications

References 7 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

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

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