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Understanding Challenges in the Data Pipeline for Development Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Understanding Challenges in the Data Pipeline for Development Data. |
| 개인저자 | Pervaiz, Fahad. |
| 단체저자명 | University of Washington. Computer Science and Engineering. |
| 발행사항 | [S.l.]: University of Washington., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 165 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085746366 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Anderson, Richard. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | The developing world is relying more and more on data driven policies. Numerous development agencies have pushed for on-ground data collection to support the development work they pursue. Many governments have launched efforts for more frequent information gathering. Overall, the amount of data collected is tremendous, yet we face significant issues in doing useful analysis. Most of these barriers are around data cleaning and merging, and they require a data engineer to support some parts of the analysis. This thesis aims to understand the pain points of cleaning development data. It also proposes solutions that harness the thought process of a data engineer to reduce the manual workload of the tedious process of cleaning such data. To achieve these goals, two research areas are critical: (1) to discern current data usage patterns and to build a taxonomy of data cleaning in the developing world |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
