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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳炳宇(Bing-Yu Chen) | |
| dc.contributor.author | Chun-Ta Lin | en |
| dc.contributor.author | 林君達 | zh_TW |
| dc.date.accessioned | 2021-06-08T01:58:57Z | - |
| dc.date.copyright | 2016-07-05 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-06-28 | |
| dc.identifier.citation | [1] M. K. A. B. V. Bittorf, T. Bobrovytsky, C. C. A. C. J. Erickson, M. G. D. Hecht, M. J. I. J. L. Kuff, D. K. A. Leblang, N. L. I. P. H. Robinson, D. R. S. Rus, J. R. D. T. S. Wanderman, and M. M. Yoder. xxxxx. CIDR, 2015.
[2] B. Brown, M. Chui, and J. Manyika. Are you ready for the era of big data McKinsey Quarterly, 4(2011):24–35, 2011. [3] C. P. Chen and C.-Y. Zhang. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275:314–347, 2014. [4] J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107–113, 2008. [5] G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: amazon’s highly available key-value store. In ACM SIGOPS Operating Systems Review, volume 41, pages 205–220. ACM, 2007. [6] M. Hausenblas and J. Nadeau. Apache drill: interactive ad-hoc analysis at scale. Big Data, 1(2):100–104, 2013. [7] S. Higginbotham. How aetna is using big data to improve patient health. https://gigaom.com/2012/11/20/how-aetna-isusing-big-data-to-improve-patient-health/. Accessed: 2016-02-18. [8] J. Lin and D. Ryaboy. Scaling big data mining infrastructure: the twitter experience. ACM SIGKDD Explorations Newsletter, 14(2):6–19, 2013. [9] S. Melnik, A. Gubarev, J. J. Long, G. Romer, S. Shivakumar, M. Tolton, and T. Vassilakis. Dremel: interactive analysis of web-scale datasets. Proceedings of the VLDB Endowment, 3(1-2):330–339, 2010. [10] C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig latin: a not-soforeign language for data processing. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 1099–1110. ACM, 2008. [11] P. Poba-Nzaou, L. Raymond, and B. Fabi. Adoption and risk of erp systems in manufacturing smes: a positivist case study. Business Process Management Journal, 14(4):530–550, 2008. [12] S. Sagiroglu and D. Sinanc. Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on, pages 42–47. IEEE, 2013. [13] J. Shute, R. Vingralek, B. Samwel, B. Handy, C. Whipkey, E. Rollins, M. Oancea, K. Littlefield, D. Menestrina, S. Ellner, et al. F1: A distributed sql database that scales. Proceedings of the VLDB Endowment, 6(11):1068–1079, 2013. [14] M. Stonebraker and J. M. Hellerstein. Readings in database systems. Morgan Kaufmann San Francisco, 1998. [15] sustainability.ups.com. Ups big data infographic. http://sustainability.ups.com/media/UPS-Big-DataInfographic.pdf. Accessed: 2016-02-18. [16] P. Tambe. Big data investment, skills, and firm value. Management Science, 60(6):1452–1469, 2014. [17] O. Tene and J. Polonetsky. Privacy in the age of big data: a time for big decisions. Stanford Law Review Online, 64:63, 2012. [18] X. Tian, G. Lu, X. Zhou, and J. Li. Evolution from shark to spark sql: Preliminary analysis and qualitative evaluation. In Big Data Benchmarks, Performance Optimization, and Emerging Hardware, pages 67–80. Springer, 2015. [19] R. Wong. Big data privacy. J Inform Tech Softw Eng, 2:e114, 2012. [20] H. R. Yen and C. Sheu. Aligning erp implementation with competitive priorities of manufacturing firms: An exploratory study. International Journal of Production Economics, 92(3):207–220, 2004. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19436 | - |
| dc.description.abstract | None | zh_TW |
| dc.description.abstract | It is well known that several business sectors (advertising, insurance, finance, etc.) have been benefited from the big data revolution. However, in the manufacturing field the investment in it is relatively inactive. Previous research had pointed out that the barrier to bring big data into practice is usually facing barrier of inexpert stuff, cost and might cause a privation of sponsorship. Also, previous report estimates a lower potential value behind the data in manufacturing, however, it is hard to justify the value until someone hit the jackpot.
In addition, looking into the portion that manufacturing occupied in the GDP, we believe it worths to make some tradeoffs and design a system for preliminary study that mainly optimized to be affordable in terms of infrastructure, skill set training and deployment without tumbling existing system in the field. In this thesis, we studied an approach backed by Dremel (Commercialized product name as BigQuery), a nested and column-based OLAP system. We focus on consumer electronics products, more specifically: laptop, tablet and router. Our preliminary result shows useful cases can be obtained immediately by collecting more data (we estimate at least two orders of magnitude in terms of data size) without using advanced data mining techniques. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T01:58:57Z (GMT). No. of bitstreams: 1 ntu-105-R99922017-1.pdf: 17663353 bytes, checksum: e8217cfd68ab686d6bd4ff471c44f3c8 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 The proposed approach . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2 Related Studiesp . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Factory 101 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Challenges and characteristics . . . . . . . . . . . . . . . . . . . 7 2.3 The backends . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 3 System Design . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Log daemon component . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.1 Direct connection approach . . . . . . . . . . . . . . . . . . . 21 3.2.2 Asynchronous approach . . . . . . . . . . . . . . . . . . . . . . 23 3.3 Schema design and validator component . . . . . . . . . . . . . . . 27 3.3.1 Archiver and extractor component . . . . . . . . . . . . . . . . 30 3.4 Security and confidentiality . . . . . . . . . . . . . . . . . . . 30 3.5 Deploy with existing system . . . . . . . . . . . . . . . . . . . . 32 Chapter 4 Case Studies . . . . .. . . . . . . . . . . . . . . . . . . . 34 4.1 Failure analysis with unexpected request . . . . . . . . . . . . . 34 4.2 Monitoring in details with dashboard . . . . . . . . . . . . . . . 40 4.3 Field return analysis . . . . . . . . . . . . . . . . . . . . . . . 41 Chapter 5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 44 Chapter 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . 45 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 | |
| dc.language.iso | en | |
| dc.title | 在製造消費性電子上使用BigQuery的初步研究 | zh_TW |
| dc.title | Preliminary study on manufacturing consumer electronics with the
BigQuery approach | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 簡立峰(Lee-Feng Chien) | |
| dc.contributor.oralexamcommittee | 余孝先(Shiaw-Shian Yu),許永真(Yung-Jen Hsu) | |
| dc.subject.keyword | 消費性電子,製造,Big Query, | zh_TW |
| dc.subject.keyword | Consumer hardware,Manufacturing,Big Query, | en |
| dc.relation.page | 49 | |
| dc.identifier.doi | 10.6342/NTU201600503 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2016-06-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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