| LDR | | 01815nam u200385 4500 |
| 001 | | 000000419116 |
| 005 | | 20190215163423 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438127180 |
| 035 | |
▼a (MiAaPQ)AAI10903113 |
| 035 | |
▼a (MiAaPQ)umichrackham:001105 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Antenucci, Dolan. |
| 245 | 10 |
▼a Maximizing Insight from Modern Economic Analysis. |
| 260 | |
▼a [S.l.]:
▼b University of Michigan.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 186 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Michael John Cafarella. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2018. |
| 520 | |
▼a The last decade has seen a growing trend of economists exploring how to extract different economic insight from "big data" sources such as the Web. As economists move towards this model of analysis, their traditional workflow starts to become in |
| 520 | |
▼a This dissertation presents several systems and methodologies that bring economists closer to this ideal workflow, helping them address many of the challenges faced in transitioning to working with big data sources like the Web. To help users gen |
| 590 | |
▼a School code: 0127. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Michigan.
▼b Computer Science and Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0127 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000606
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |