상세정보
Export to Refworks부가기능
Network Structures, Concurrency, and Interpretability: Lessons from the Development of an AI Enabled Graph Database System
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Network Structures, Concurrency, and Interpretability: Lessons from the Development of an AI Enabled Graph Database System. |
| 개인저자 | Cooper, Hal James. |
| 단체저자명 | Columbia University. Operations Research. |
| 발행사항 | [S.l.]: Columbia University., 2020. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2020. |
| 형태사항 | 174 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781392874592 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2020. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Iyengar, Garud N. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This thesis describes the development of the SmartGraph, an AI enabled graph database.The need for such a system has been independently recognized in the isolated fields of graphdatabases, graph computing, and computational graph deep learning systems, such as TensorFlow.Though prior works have investigated some relationships between these fields, we believe that theSmartGraph is the first system designed from conception to incorporate the most significant anduseful characteristics of each. Examples include the ability to store graph structured data, runanalytics natively on this data, and run gradient descent algorithms. It is the synergistic aspectsof combining these fields that provide the most novel results presented in this dissertation. Keyamong them is how the notion of "graph querying" as used in graph databases can be used to solvea problem that has plagued deep learning systems since their inception |
| 일반주제명 | Computer science. Operations research. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
