In Virginia Tech's Digital Library Research Laboratory, we support subject-matter-experts (SMEs) in their pursuit of research goals. Their goals include everything from data collection to analysis to reporting. Their research commonly involves an analysis of an extensive collection of data such as tweets or web pages. Without support -- such as by our lab, developers, or data analysts/scientists -- they would undertake the data analysis themselves, using available analytical tools, frameworks, and languages. Then, to extract and produce the information needed to achieve their goals, the researchers/users would need to know what sequences of functions or algorithms to run using such tools, after considering all of their extensive functionality. Our research addresses these problems directly by designing a system that lowers the information barriers. Our approach is broken down into three parts. In the first two parts, we introduce a system that supports discovery of both information and supporting services. In the first part, we describe the methodology that incorporates User eXperience (UX) research into the process of workflow design. Through the methodology, we capture (a) what are the different user roles and goals, (b) how we break down the user goals into tasks and sub-tasks, and (c) what functions and services are required to solve each (sub-)task. In the second part, we identify and describe key components of the infrastructure implementation. This implementation captures the various goals/tasks/services associations in a manner that supports information inquiry of two types: (1) Given an information goal as query, what is the workflow to derive this information? and (2) Given a data resource, what information can we derive using this data resource as input? We demonstrate both parts of the approach, describing how we teach and apply the methodology, with three case studies. In the third part of this research, we rely on formalisms used in describing digital libraries to explain the components that make up the information system. The formal description serves as a guide to support the development of information systems that generate workflows to support SME information needs. We also specifically describe an information system meant to support information goals that relate to Twitter data. / Doctor of Philosophy / In Virginia Tech's Digital Library Research Laboratory, we support subject-matter-experts (SMEs) in their pursuit of research goals. This includes everything from data collection to analysis to reporting. Their research commonly involves an analysis of an extensive collection of data such as tweets or web pages. Without support -- such as by our lab, developers, or data analysts/scientists -- they would undertake the data analysis themselves, using available analytical tools, frameworks, and languages. Then, to extract and produce the information needed to achieve their goals, the researchers/users would need to know what sequences of functions or algorithms to run using such tools, after considering all of their extensive functionality. Further, as more algorithms are being discovered and datasets are getting larger, the information processing effort is getting more and more complicated. Our research aims to address these problems directly by attempting to lower the barriers, through a methodology that integrates the full life cycle, including the activities carried out by User eXperience (UX), analysis, development, and implementation experts. We devise a three part approach to this research. The first two parts concern building a system that supports discovery of both information and supporting services. First, we describe the methodology that introduces UX research into the process of workflow design. Second, we identify and describe key components of the infrastructure implementation. We demonstrate both parts of the approach, describing how we teach and apply the methodology, with three case studies. In the third part of this research, we extend formalisms used in describing digital libraries to encompass the components that make up our new type of extensible information system.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/103815 |
| Date | 11 June 2021 |
| Creators | Chandrasekar, Prashant |
| Contributors | Computer Science, Fox, Edward A., Contractor, Noshir Sarosh, Lu, Chang Tien, Franck, Christopher Thomas, Harrison, Steven R. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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