TOPIC 4- DQ-2- 3

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Grand Canyon University *

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831

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Computer Science

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Nov 24, 2024

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Combining structured and unstructured data can be a tedious task for even some of the best analysts. Structured data sets include data with known, organized fields and values, such as date of birth, address, gender, sex, etc. (Zhang et al., 2020). Structured data is easy to slice because the algorithm identifies the value and places it into a bucket with other like values. Unstructured data comes in the form of free form text, audio and video (Zhu et al., 2021). Due to the unorganized and uncategorized characteristics in unstructured data, without special tools, it is very time consuming and sometimes not cost efficient or beneficial for the research to analyze unstructured data. There are methods that provide researchers with the ability to organize and analyze unstructured data sets. Paragraph vectors are specifically helpful in analyzing unstructured medical data. Paragraph vectors uses a set of key words or phrases and assigns a numeric value to them. The algorithm identifies those key words, categorizes them and applies the numeric values in a machine learning concept (Zhu et al., 2021). Integrating structured and unstructured data fills gaps in research that can not be communicated via structured data alone. This is similar to using qualitative research to explain and close gaps in quantitative research. Alone, the research methods only tell so much of a story. Together, the research methods tell a much richer story, and may change the perspective of the research entirely. Zhang, D., Yin, C., Zeng, J., Yuan, X., & Zhang, P.. (2020). Combining structured and unstructured data for predictive models: a deep learning approach. BMC Medical Informatics and Decision Making , 20 (1), 1–11. Zhu, L., Wu, H., & Wells, M. T. (2021). A news-based machine learning model for adaptive asset pricing. Arxiv.
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