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The earliest research into business intelligence focused in on unstructured textual data, rather than numerical data. [8] As early as 1958, computer science researchers like H.P. Luhn were particularly concerned with the extraction and classification of unstructured text. [8]
While the data from an unstructured interview has more quality than that obtained from a structured interview, in the sense that the participant has more of an opportunity to say what they like freely, the data collected in unstructured interviews is also prone to digression and much of the data collected could be worthless.
A structured interview (also known as a standardized interview or a researcher-administered survey) is a quantitative research method commonly employed in survey research. The aim of this approach is to ensure that each interview is presented with exactly the same questions in the same order. This ensures that answers can be reliably aggregated ...
Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context. Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display.
Interviews are considered to be "the most common method of gathering data for qualitative research"; further, they "are an integral part of most research traditions." [16] Interviews may be structured, semi-structured, or unstructured. [16]
The largest portion of information contained in business documents (about 80% [10]) is encoded in natural language and therefore unstructured. Because unstructured data is rather a challenge for knowledge extraction, more sophisticated methods are required, which generally tend to supply worse results compared to structured data. The potential ...
Data science is an interdisciplinary academic field [1] that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.
Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. [38] Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data , etc., are rapidly becoming a relevant ...