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ํ•˜๋‘ก ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹ / ๊ธฐ๊ณ„ํ•™์Šต ์˜คํ”ˆ์†Œ์Šค!

ankus !
!
ankus community / ์ „์ˆ˜ํ˜„!
suhyunjun@gmail.com!
openankus.orgโ€จ
๋น…๋ฐ์ดํ„ฐ์™€ ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹
๋น…๋ฐ์ดํ„ฐ = ์‹œ์Šคํ…œ + ๋ถ„์„ + ..
์‹œ์Šคํ…œ (์ธํ”„๋ผ)

๋ฐ์ดํ„ฐ ๋ถ„์„
๋น…๋ฐ์ดํ„ฐ ํ”„๋กœ์„ธ์Šค

collection
(์ˆ˜์ง‘)

storage
(์ €์žฅ)

analysis
(๋ถ„์„)
๋น…๋ฐ์ดํ„ฐ ๊ณผ์ œ

๋น…๋ฐ์ดํ„ฐ๋ฅผ ๋„์ž…ํ•˜๋ฉด ๊ณผ์—ฐ ํšจ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์„๊นŒ?
๋น…๋ฐ์ดํ„ฐ์˜ ์„ฑ๊ณต์ ์ธ ์‚ฌ๋ก€๋“ค์€ ์ง๊ฐ„์ ‘์ ์œผ๋กœ ์ฆ๋ช…๋˜๊ณ  ์žˆ๋‹ค!

์˜ค๋ฐ”๋งˆ ็พŽ ํ–‰์ •๋ถ€ ๋น…๋ฐ์ดํ„ฐ '์˜ฌ์ธ'ยทยทยท"ํšจ๊ณผ ์•„๋‹ˆ๊นŒ~"
๋น…๋ฐ์ดํ„ฐ์˜ ์„ฑ๊ณต์ ์ธ ์‚ฌ๋ก€๋“ค์€ ์ง๊ฐ„์ ‘์ ์œผ๋กœ ์ฆ๋ช…๋˜๊ณ  ์žˆ๋‹ค!

Netflix ๊ฒฝ์˜์ง„์€ ์ž์‚ฌ ๊ณ ๊ฐ์˜ ๋™์˜์ƒ ์‹œ์ฒญ ์„ ํ˜ธ๋„๋ฅผ ๋ถ„์„ํ•˜์—ฌ 1990๋…„ BBC ์‚ฌ์˜ ๋ฏธ์Šคํ„ฐ๋ฆฌ๋ฌผ์„ ๋ฆฌ๋ฉ”์ดํฌํ•˜๊ธฐ๋กœ ๊ฒฐ์ •!

๋Œ€๋ฐ• ์‚ฌ๊ฑด!!
1์–ต ๋‹ฌ๋Ÿฌ(1์ฒœ์–ต์›) ํˆฌ์ž!!
๋น…๋ฐ์ดํ„ฐ์˜ ํšจ์œจ์ ์ธ ํ™œ์šฉ์„ ์œ„ํ•ด์„œ๋Š” ๊ณ ๊ธ‰ ๋ถ„์„ ๊ธฐ๋ฒ•์ด ํ•„์š”
๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹ ์ •์˜

Data

+

Mining

๋ฐ์ดํ„ฐ ์†์˜ ์ž ์žฌ์ ์ธ ์œ ์šฉํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง„ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š” ๊ฒƒ์„ ๋งํ•˜๋ฉฐ
๋˜ํ•œ KDD(knowledge-discovery in databases) ๊ณผ์ • ์ค‘ ํ•œ ๋‹จ๊ณ„์ด๊ธฐ๋„ ํ•˜๋‹ค.
๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹์˜ ์ดํ•ด - ๊ณผ๊ฑฐ

๋ฉ”์ธํ”„๋ ˆ์ž„(Big Iron)!
(๊ณ ๊ฐ€์˜ ๋น„์šฉ)

๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ธฐ๋ฒ• ์ ์šฉ!
(๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹)!

๋‚ฎ์€ ํ’ˆ์งˆ์˜ ๊ฒฐ๊ณผ๋ฌผ ์ดˆ๋ž˜!
๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹์˜ ์ดํ•ด - ํ˜„์žฌ

๋ถ„์‚ฐ ์ปดํ“จํŒ…!
(์ €๋ ดํ•œ ๋น„์šฉ)!

๊ณ ๊ธ‰ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ธฐ๋ฒ• ์žฌ๊ตฌํ˜„!
(๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹)!

์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋ฐ์ดํ„ฐ ๋ถ„์„ !
ํ’ˆ์งˆ์ด ๋†’์•„์ง!
๊ทธ๋ž˜์„œ !
๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ธฐ๋ฒ•์ธ ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹์ด ๋‹ค์‹œ ์ฃผ๋ชฉ๋ฐ›๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค.
๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹ ๊ธฐ๋ฒ•
Predictive
(์˜ˆ์ธก ๋ชจ๋ธ๋ง)

data
mining

Classi๏ฌcation (๋ถ„๋ฅ˜)

Clustering (๊ตฐ์ง‘ํ™”)

K-NN!
Decision Tree!
Neural Networks!
SVM!
Regression!
Bayesian Network

K-Means!
EM!
Density Based!
SOM!
Hierarchical

Descriptive
(๊ธฐ์ˆ  ๋ชจ๋ธ๋ง)
Association (์—ฐ๊ด€์„ฑ)

Apriori!
FP-Growth

Recommendation system
13
Classification(๋ถ„๋ฅ˜)

๋ฐ์ดํ„ฐ๋“ค์„ ๋ฏธ๋ฆฌ ์ง€์ •๋œ ์นดํ…Œ๊ณ ๋ฆฌ๋‚˜ ๋“ฑ๊ธ‰์œผ๋กœ ๋‚˜๋ˆ„๋Š” ๋ถ„์„
14
Clustering(๊ตฐ์ง‘)

๋ฐ์ดํ„ฐ๋“ค์„ ์œ ์‚ฌํ•œ ์„ฑ๊ฒฉ์„ ๊ฐ€์ง€๋Š” ์ž„์˜์˜ ๊ทธ๋ฃน์œผ๋กœ ๋ถ„ํ• 
15
Association(์—ฐ๊ด€)

๊ตฌ๋งค ๋ฐ์ดํ„ฐ์—์„œ ๋ฐ˜๋ณต์ ์œผ๋กœ ํ•จ๊ป˜ ํŒ๋งค๋˜๋Š” ์ƒํ’ˆ๋“ค๊ฐ„์˜
์—ฐ๊ด€์„ฑ์— ๋Œ€ํ•œ ๊ทœ์น™์„ ์ฐพ์•„๋‚ด๊ธฐ
16
๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹์˜ ์ดํ•ด

Choice algorithm

๋ฐ์ดํ„ฐ์˜ ํŠน์ง•๊ณผ !
ํ•ด๊ฒฐํ•˜๋ ค๋Š” ๋ฌธ์ œ์— ์ ํ•ฉํ•œ ๊ธฐ๋ฒ•(์•Œ๊ณ ๋ฆฌ์ฆ˜)์„ ์ž˜ ์„ ํƒํ•ด์•ผ ํ•œ๋‹ค.
17
ํ•˜๋‘ก ๊ธฐ๋ฐ˜์˜ ๋ฐ์ดํ„ฐ ๋ถ„์„
์ „ํ†ต์  ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋„๊ตฌ

WEKA

IBM SPSS Modeler

R

SAS Enterprise Miner

ECMiner

19
ํ•˜๋‘ก ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋„๊ตฌ

Data Analysis

Data mining / machine learning

20
๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ ์ธํ”„๋ผ ๋น„๊ต
Previous Analysis Tools
Data Analysis Tool
Local: Data Aggregation

MapReduce based Analysis
Data Processing/Extraction
and Analysis Tool
Data Mining /

Data Processing/Extraction

MapReduce Framework
Distributed Big Data System based on Hadoop

โ€ฆโ€ฆ

21
๋งต๋ฆฌ๋“€์Šค ๊ฐœ๋… ๋ฐ ์˜ˆ์ œ
๋งต๋ฆฌ๋“€์Šค ์ •์˜
In Wikipedia!
MapReduce is a programming model for processing large data sets with a parallel,
distributed algorithm on a cluster.!
A MapReduce program comprises a Map() procedure that performs filtering and sorting !
and a Reduce() procedure that performs a summary operation.
โ€ข Hadoop์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ๋น…๋ฐ์ดํ„ฐ ํ”Œ๋žซํผ์—์„œ์˜ ๋ฐ์ดํ„ฐ
์ฒ˜๋ฆฌ ํ”„๋กœ์„ธ์Šค!
โ€ข Map/Reduce : Key, Value ๊ตฌ์กฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ
ํ•˜๋Š” ๋ฐ์ดํ„ฐ ๋ถ„์‚ฐ ์ฒ˜๋ฆฌ ๊ตฌ์กฐโ€จ
(ํ”„๋ ˆ์ž„์›Œํฌ)!
โ€ข Google์—์„œ 2004๋…„ ์ตœ์ดˆ ๋ฐœํ‘œ

23
๋งต๋ฆฌ๋“€์Šค ์˜ˆ์ œ - Word count

24
๋งต๋ฆฌ๋“€์Šค ์˜ˆ์ œ - Word count - Mapper

25
๋งต๋ฆฌ๋“€์Šค ์˜ˆ์ œ - Word count - Reducer

26
ํ•˜๋‘ก ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹/๊ธฐ๊ณ„ํ•™์Šต!
์˜คํ”ˆ์†Œ์Šค ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ !
ankus
ankus ๋ชฉ์ 

28
ankus vs mahout

โ€ข

โ€ข

๋ณ„๋„์˜ ์ „์ฒ˜๋ฆฌ ์—†์ด ์ž…๋ ฅ ํŒŒ์ผ ์‚ฌ์šฉ ๊ฐ€๋Šฅ!
๋‹ค์–‘ํ•œ ๋ถ„์„์„ ์œ„ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ์„ ํƒ ๋ถ„์„ ์ˆ˜ํ–‰ ์ง€์›!
์›น ๊ธฐ๋ฐ˜ UI ์ง€์›์œผ๋กœ ์† ์‰ฝ๊ฒŒ ๋ถ„์„ ์ˆ˜ํ–‰ ๊ฐ€๋Šฅ!
๊ตญ๋‚ด ์ˆœ์ˆ˜ 100% ๊ธฐ์ˆ 

โ€ข

ํ•œ์ •๋œ ๊ฐœ์ˆ˜์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ œ๊ณต!

โ€ข

โ€ข

์žฅ์ 

โ€ข
โ€ข

โ€ข

!

๋‹ค์–‘ํ•œ ์ข…๋ฅ˜์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ฐ ์•ˆ์ •๋œ ๋ฒ„์ „ ์ œ๊ณต(2008~)!
์‹œํ€€์Šค ํŒŒ์ผ์„ ์ด์šฉํ•œ ๋น ๋ฅธ ์ˆ˜ํ–‰ ์†๋„ ์ง€์›!

โ€ข

๋‹จ์ 

๋ผ์ด์„ ์Šค

!

โ€ข

์‹œํ€€์Šค ํŒŒ์ผ ํ˜•ํƒœ์˜ ์ž…๋ ฅ ํŒŒ์ผ ์ฒ˜๋ฆฌ ํ•„์š”!
๋ถ„์„ ์ˆ˜ํ–‰ ์‹œ ํŒŒ๋ผ๋ฏธํ„ฐ(์†์„ฑ) ์„ ํƒ ๋ถˆ๊ฐ€๋Šฅ!
CLI ์ค‘์‹ฌ์˜ ๊ฐœ๋ฐœ

โ€ข

Apache License 2.0

โ€ข

Apache License 2.0

29
ankus ์„ค๊ณ„ ๊ตฌ์กฐ

30
ankus ์ฃผ์š” ๊ธฐ๋Šฅ

31
CLI ๊ธฐ๋ฐ˜์˜ ankus ์‹คํ–‰

32
์›น ๊ธฐ๋ฐ˜์˜ ankus ์‹คํ–‰ - 1

33
์›น ๊ธฐ๋ฐ˜์˜ ankus ์‹คํ–‰ - 2

34
ankus ์˜ˆ์ œ - ์œ ์‚ฌ/์ƒ๊ด€๊ณ„์ˆ˜ - Pearson Correlation Coefficient

35
ankus ์˜ˆ์ œ - ์œ ์‚ฌ/์ƒ๊ด€๊ณ„์ˆ˜ - Pearson Correlation Coefficient

ankus framework ๋ฐ๋ชจ

36
ankus ์˜ˆ์ œ - ์œ ์‚ฌ/์ƒ๊ด€๊ณ„์ˆ˜ - Pearson Correlation Coefficient

MR - 1

MR - 2
โ€ฆโ€ฆ. (์ƒ๋žต)

input ๋ฐ์ดํ„ฐ (๋ฌด๋น„๋ Œ์ฆˆ ๋ฐ์ดํ„ฐ์…‹)

์ฒซ๋ฒˆ์งธ MR Job ๊ฒฐ๊ณผ ๋ฐ์ดํ„ฐ

๋งˆ์ง€๋ง‰ MR Job ๊ฒฐ๊ณผ ๋ฐ์ดํ„ฐ

37
ankus ์ปค๋ฎค๋‹ˆํ‹ฐ
โ€ข

์†Œ์Šค์ฝ”๋“œ ๋‹ค์šด๋กœ๋“œ!
โ€ข
โ€ข

โ€ข

http://github.com/suhyunjeon/ankus
http://sourceforge.net/projects/ankus

์œ„ํ‚ค - ์‚ฌ์šฉ์ž/๊ฐœ๋ฐœ์ž ๊ฐ€์ด๋“œ!
โ€ข

โ€ข

์‚ฌ์šฉ์ž ๊ทธ๋ฃน - ํŽ˜์ด์Šค๋ถ!
โ€ข

โ€ข

http://openankus.org

http://www.facebook.com/groups/openankus

์‚ฌ์šฉ์ž ํฌ๋Ÿผ - ๊ตฌ๊ธ€ ๊ทธ๋ฃน์Šค!
โ€ข

http://goo.gl/d8nP81

๋งˆ์ง€๋ง‰ MR Job ๊ฒฐ๊ณผ ๋ฐ์ดํ„ฐ

38
๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค.

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