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Applications using the Class Frequency Distribution of Maximal - - PowerPoint PPT Presentation

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2019/7/19. 23 AI Summer Program : Hadoop Map&Reduce Programming for Big Traffic Data Management Applications using the Class Frequency Distribution of Maximal Repeats from Tagged Sequential Data. (Dr. Jing-Doo Wang)

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Applications using the Class Frequency Distribution of Maximal Repeats from Tagged Sequential Data.

王經篤 博士 (Dr. Jing-Doo Wang) 亞洲大學 （Asia University）

2019/7/19. 23 AI Summer Program : Hadoop Map&Reduce Programming for Big Traffic Data Management

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Chinese proverbs: 『老王』賣瓜

http://www.9ht.com/xue/44228.html http://www.pxmart.com.tw/px/ingredients.px?id=2592

Is it sweet and juicy?

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Outline

  • Introduction

– What is “Sequential Data”? – A scalable approach of Maximal Repeat Extraction

  • Applications with Tagged Sequential Data

– Analyzing Text Archaeology. – Extracting Significant Travel Time Interval – Mining for Biomarker. – Improving Quality Control.

  • Future Works
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What is “Sequential Data”?

  • Textual Data : News, Journal Articles, etc.

From:https://www.udn.com/news/story/7266/2834500 https://www.ncbi.nlm.nih.gov/pubmed/24372032 http://edition.cnn.com/2017/11/22/health/jfk-assassination-back-pain/index.html

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What is “Sequential Data”?

  • Genomic Sequences

From:http://blogs.nature.com/naturejobs/2015/10/08 /big-data-the-impact-of-the-human-genome-project/

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What is “Sequential Data”?

  • Traffic Transportation

https://attach.mobile01.com/640x480/attach/201312/ mobile01-b004e8fd829e35140b3de0d91e847953.jpg

https://tptis2015.blogspot.tw/2017/10/blog-post.html https://tptis2015.blogspot.tw/2015/07/300-brt.html

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7

Product Traceability

****************************************

www.iconarchive.com http://technews.tw/2016/04/11/tsmc-and-largan/ http://www.slideshare.net/5045033/ss-1002323

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It‘s a big data problem !

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How to mine from these “sequential data”?

http://clipart- library.com/clipart/kiKB8qLRT.htm http://clipart- library.com/clipart/6Tr5BGG7c.htm

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How to mine from these “sequential data”?

From: http://globe-views.com/dcim/dreams/mine/mine-03.jpg

?

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It’s a Big Data problem!

http://www.mining.com/wp-content/uploads/2015/06/Veladero-Mine.jpg http://haphazardstuffblog.com/wp- content/uploads/2012/01/Big-truck.jpg

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What kind of “features” extracted from Sequential Data?

  • http://www.quickanddirtytips.com/sites/

default/files/images/2499/question- mark2.jpg

http://images.slideplayer.com/16/5176005/slides/slide_2.jpg

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What kind of “Mineral” do you want (mine)?

https://media1.britannica.com/eb-media/71/143171-049-53725C29.jpg https://www.popsci.com/features/how-to-be-an-expert-in- anything/images/feature_video.jpg

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Outline

  • Introduction

– What is “Sequential Data”? – A scalable approach of Maximal Repeat Extraction

  • Applications with Tagged Sequential Data

– Analyzing Text Archaeology. – Extracting Significant Travel Time Interval – Mining for Biomarker. – Improving Quality Control.

  • Future Works
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Journal of Supercomputing, April 2016

https://link.springer.com/article/10.1007/s11227-016- 1713z?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst

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Why use “Maximal Repeats ” as features?

  • Dictionary

– How to identify new words or phrases? – e.g. “just do it”, “洪荒之力”。

  • N-gram (K-mers)

– 2-gram, 3-gram,…,5-grams. (Google Ngram viewer) – The value of “N” is limited.

  • Maximal Repeat

– The length of maximal repeat is variable.

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17

“xabcyiiizabcqabcyrxar”

  • ab
  • bc
  • abc
  • abcy

Not Maximal repeat Pattern

Example: Maximal Repeat Pattern

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Distinctive Pattern Mining(1)

S3:********************$*********** S5:********************$*********** S7:*****&*******%****************** S1:******************************** S9:*****&*******%****************** S11:******************************** S10:*********#****?************@**** S8:******************************** S6:*********#****?************@**** S4:*****&*******%****************** S2:*********#****?***********@***** Sequences Classes These Classes are labeled by Domain Experts

jdwang@asia.edu.tw

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Distinctive Pattern Mining(2)

******************************** ******************************** ******************************** *********#****?************@**** *********#****?************@**** *********#****?************@**** *****&*******%****************** *****&*******%****************** *****&*******%****************** ********************$*********** ********************$***********

Classes

jdwang@asia.edu.tw

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Distinctive Pattern Mining(3)

Maximal Repeats

#****? @**** &*******% $********** *****

jdwang@asia.edu.tw

Class Frequency Distribution

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Applying for U.S.A. Patent

From: https://www.google.com/patents/US20170255634

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Patent Publication Date :

  • Sep. 7, 2017

http://haphazardstuffblog.com/wp- content/uploads/2012/01/Big- truck.jpg

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Outline

  • Introduction

– What is “Sequential Data”? – A scalable approach of Maximal Repeat Extraction

  • Applications with Tagged Sequential Data

– Analyzing Text Archaeology. – Extracting Significant Travel Time Interval – Mining for Biomarker. – Improving Quality Control.

  • Future Works
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Applications with Tagged Sequential Data

  • Analyzing Trend Analysis via Text Archaeology.
  • Extracting Significant Travel Time Interval from

Gantry Timestamped Sequences.

  • Mining for Biomarker from Genomic Sequences.
  • Improving Quality Control via Product Traceability.
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From: http://www.mdpi.com/2076-3417/7/9/878

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Superhighway

From: http://chiangchiafeng.tian.yam.com/posts/70456997

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e-Tag

http://news.u-car.com.tw/article/16077

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中華民國國道（高速公路）的電子收費系統 （Electronic Toll Collection，簡稱ETC）

From: https://i.ytimg.com/vi/1ML2FFS2dJg/maxresdefault.jpg

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https://attach.mobile01.com/640x480/attach/201312/mobile01-b004e8fd829e35140b3de0d91e847953.jpg

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Gantry Sequences Of different Vehicle Types (VT)

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Gantry Timestamp Sequences with Timestamps

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Gantry Timestamp Sequences with TimeStamps for different Vehicles Type

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Significant Time Intervals of Vehicles

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http://www.7car.tw/articles/read/25927

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https://buzzorange.com/wp- content/uploads/2015/04/640_4a486 dc48d6f1414404627e1c45f1cf9.jpg http://news.ltn.com.tw/photo/society/breakingnews/10883 61_1

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05F0055N,13:33 05F0438N,13:06

05F0287N,13:15

05F0309N,13:13 05F0528N,13:00

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Significant Time Intervals

  • f Vehicles

05F0528N_13_M1_00 05F0438N_13_M1_06 05F0309N_13_M1_13 05F0287N_13_M1_15 05F0055N_13_M1_33 ##4 ##5 ## (2016-11-15_Mon_41#1#1) (2016-11-29_Mon_41#1#1) (2016-12-09_Thu_31#1#1) (2016-12-20_Mon_31#1#1)

Significant Time intervals Class Frequency Distribution

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Weekday vs. 24 Hours/per day

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Vehicle Types vs. 24 Hours/per day

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Significant Patterns of Travel Time Intervals of Vehicles

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Outline

  • Introduction

– What is “Sequential Data”? – A scalable approach of Maximal Repeat Extraction

  • Applications with Tagged Sequential Data

– Analyzing Text Archaeology. – Extracting Significant Travel Time Interval – Mining for Biomarker. – Improving Quality Control.

  • Future Works
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1+5 cluster nodes

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2+ 8 cluster nodes

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Cloud Computing Environment

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Artificial Intelligence

Artificial Intelligence Big Data Cloud Computing Machine Learning

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古希臘的科學 阿基米德撐起地球的支點

From:https://phycat.files.wordpress.com/2015/03/leverbigcorners.gif?w=810

Knownledge ? Relationship? Sequential Data ? Infrastructure (Cloud Computing) Domain Expert ? Labels (Tags)

Leverage principle (槓桿原理)

(Maximal Repeat Extraction with Class Frequency Distribution)

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插圖：紀玲玉

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Acknowledgements （Precision Medicine）

  • Jeffrey J.P. Tsai ( 亞洲大學 蔡進發 校長)

計劃名稱：以生醫大數據分析為基礎的精準癌症醫療研究(2/3) 計畫編號: MOST 106-2632-E-468-002 計畫執行起迄: 106/08/01~107/07/31

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Acknowledgements （Bioinformatics）

  • Charles C.N. Wang
  • Wen-Ling Chan
  • Jan-Gowth Chang
  • Tsung-Chi Chen
  • Yi-Chun Wang
  • Rouh-Mei Hu
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Acknowledgements （Traffic Information Analysis）

  • 黃銘崇 主任
  • 潘信宏 教授
  • 連耀南 教授
  • 何承遠 教授
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Acknowledgements （Big-Data: Hadoop Computing）

Jazz Wang (王耀聰) Philip Lin ( 林奇暻) wei-chiu chuang （莊偉赳）

  • Apache Hadoop Committer/PMC

member

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Acknowledgements

  • Hadoop Cluster Set Up and Consulting

– SYSTEX 精誠資訊（2017）

  • Herb Hsu-徐啟超

– Athemaster 炬識科技股份有限公司（2018）

  • Ferrari
  • 亞洲大學 資訊發展處 黃仁德先生
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『老王』賣瓜，自賣自誇

http://www.9ht.com/xue/44228.html http://www.pxmart.com.tw/px/ingredients.px?id=2592

Lao Wang selling melons praises his own goods

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Thanks for your listening!

www.flickr.com www.slideshare.net http://www.pptschool.com/250.html