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3-1:ビッグデータの活用と分析に至るプロセス

1 3-1 3 ICT 1 2 3 4 5 1 2 3 4 2 3-1 3 V 3 [3 ] [2] [1] 3 2 3 1[1] 2 1 2 4 37465771871060204060801001202016 2017 2018 2019 2020 2021 TB/ PC IoT SNS

3-1:ビッグデータの活用と分析に至るプロセス] ビッグデータの特性の「3つのv」を説明し、それぞれの特性によって可能になる分析を示します。 データの品質のいくつかの観点から紹介し、品質の悪いデータがもたらす社会的費用を紹介します。 データ形式の標準化およびデータクレン …

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Transcription of 3-1:ビッグデータの活用と分析に至るプロセス

1 1 3-1 3 ICT 1 2 3 4 5 1 2 3 4 2 3-1 3 V 3 [3 ] [2] [1] 3 2 3 1[1] 2 1 2 4 37465771871060204060801001202016 2017 2018 2019 2020 2021 TB/ PC IoT SNS 2017 6 2016 2021 byte 1000 kB MB GB TB PB EB ZB 1

2 365 31,536,000 1 37TB 36,550GB Cisco Visual Networking Index Cisco provider/visual networking index vni/complete white paper c11 1 B 1 kB 1,000 MB 1,000,000 GB 1,000,000,000 TB 1,000,000,000,000 PB 1,000,000,000,000,000 EB 1,000,000,000,000,000,000 ZB 1,000,000,000,000,000,000,000 5 3 1[1] 5 3 V 3 V 2001 3 V IBM 3 V 4 V Veracity V Variety Volume Velocity 3 V 3 V Variety Volume Velocity Deja VVVu.

3 Others Claiming Gartner s Construct for Big Data Gartner | Doug Laney laney/deja vvvue others claiming gartners volume velocity variety construct for big data/ IBM Data Engine for Hadoop and Spark P4 IBM Value 5 V 10 V 4 V 3 V Variety Volume Velocity 3 1[1] Variety Volume Velocity V 3 V Variety Volume Velocity 6 Variety Variety ABEJA ABEJA Platform Wi Fi/ IoT POS ABEJA ABEJA platform for Retail 3 1[1] POS point of sales system 7 Volume Volume Yahoo! Japan 2017 2018 3 1[1] 8 Velocity Velocity 3 1[1]

4 5 1 3 5 9 Variety < > < > </ > < > </ > < > </ > < > </ > </ > CSV Excel XML JSON PDF 1 2 1 XML 3 1[1] 2 1 XML JSON 1 4 API 10 e-Stat e Stat Excel CSV ( ) PDF HTML JPEG e Stat e Stat 3 1[1] 4 3 2 R e Stat API e Stat XML JSON ( )

5 4 1 11 6 3 1[2] Timeliness Consistency Completeness 100 Uniqueness 2 Timeliness Validity Accuracy Consistency THE SIX PRIMARY DIMENSIONS FOR DATA QUALITY ASSESSMENT DAMA UK DAMA UK 6 12 2016 IBM Data

6 Engine for Hadoop and Spark P4 IBM SOFTWARE AG cost of bad data 3 1[2] 10 25 3140 31 4000 * 1 =100 3 300 2011 2 300 3 IT 50 18 1800 13 2015 NTT

7 3 1[2] 14 gyousei/lg 2018 2 3 1[2] 15 IPA IMI DMD Editor IMI Infrastructure for Multilayer Interoperability DMD Data Model Description DMD Editor csv xlsx RDF/XML JSON RDF/XML 1 5 XML 4 1 5 IMI IMI csv xlsx XML RDF/XML Web API.

8 <ic: rdf:resource=" "/> <ic: > <rdf:Description> <ic: > </ic: >..{"@id":"_:b2", " # ": [{"@value":" "}], " # ": JSON RDF/XML NPO 3 1[2] 2018 1 IMI e 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Kasumigaseki 100 0013 : : OpenRefine OpenRefine Sony SONY Sony SONY ( ) Sony( ) SONY( ) ( ) ( ) ( ) Sony( ) SONY( ) ( ) ( ) Sony Corporation 6758 1946 5 7 16 3 1[2] 3 2 Excel Excel 17 53%19%10%9%8%0%10%20%30%40%50%60%70%80%9 0%100% 2017 2 197 53 3 1[2]}

9 48%51%60%3%3%5%14%10%0%20%40%60%80%100% 3 6%1%10%65%78%61%13%3%0%20%40%60%80%100% 3 2017 Data Scientist Report CrowdFlower 18 3 1[3] 2014 4,672 72 3,357 67 5 47%46%31%24%14%0%20%40%60% 67%47%11%15%0%20%40%60%80% 2 3.

10 357 19 3 1[3] 20 1 3 1[3]


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