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History and Trend of Big Data and Deep Learning
1. Jongwook Woo
HiPIC
CalStateLA
Keimyung University
Dec 20 2019
Jongwook Woo, PhD, jwoo5@calstatela.edu
Big Data AI Center (BigDAI)
California State University Los Angeles
History and Trend of
Big Data and Deep Learning
2. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Contents
Myself
Introduction To Big Data
Deep Learning and Big Data
Big Data Predictive Analysis
Summary
3. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Myself
Experience:
Since 2002, Professor at California State University Los Angeles
– PhD in 2001: Computer Science and Engineering at USC
4. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Universities in Los Angeles
West
North
5. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Universities in Los Angeles
6. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
California State University
Los Angeles
7. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Myself: S/W Development Lead
http://www.mobygames.com/game/windows/matrix-online/credits
8. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Collaboration with HDP, CDH, Oracle, Amazon
using Hadoop Big Data
https://www.cloudera.com/more/customers/csula.html
9. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Myself: Partners for Services
10. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Myself: Collaborations
11. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Contents
Myself
Introduction To Big Data
Deep Learning and Big Data
Big Data Predictive Analysis
Summary
12. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
New Technology: Big Data
What is Big Data? Data or Systems?
Large Scale Data?
–Many people only see the data point of view
–3 Vs, 5Vs
Systems?
– YES
13. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Data Handling Systems: Traditional Way
14. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Data Handling: Traditional Way
Becomes too Expensive
15. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Data Handling: Another Way
Not Expensive
From 2017 Korean
Blockbuster Movie,
“The Fortress”
(남한산성)
16. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Data Handling: Another Way
Not Expensive
http://blog.naver.com/PostView.nhn?blogId=dosims&logNo=221127053677
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17. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Data Issues
Cannot handle with the legacy approach
Too big
Non-/Semi-structured data
3 Vs, 4 Vs,…
– Velocity, Volume, Variety
Traditional Systems can handle them
– But Again, Too expensive
Need new systems
Non-expensive
18. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Two Cores in Big Data
How to store Big Data
How to compute Big Data
Google
How to store Big Data
– GFS
– Distributed Systems on non-expensive commodity computers
How to compute Big Data
– MapReduce
– Parallel Computing with non-expensive computers
Own super computers
Published papers in 2003, 2004
19. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Super Computer vs Big Data vs Cloud
Traditional Super Computer
(Parallel File Systems: Lustre, PVFS, GPFS)
Cluster for Store
Big Data (Hadoop, Spark, Distributed Deep Learning)
Cluster for Compute and Store
(Distributed File Systems: HDFS, GFS)
However, Cloud Computing adopts
this separated architecture:
with High Speed N/W and Object
Storage
Cluster for Compute
20. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data: Hadoop
20
Apache Hadoop Project in
Jan, 2006 split from Nutch
Hadoop Founder:
o Doug Cutting
Apache Committer:
Lucene, Nutch, …
21. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Definition: Big Data [W13]
Non-expensive platform that is distributed parallel systems and
that can store a large scale data and process it in parallel
Hadoop
– Non-expensive Super Computer
– More public than the traditional super computers
• You can store and process your applications
– In your university labs, small companies, research centers
Others with storage and computing services
– Spark
• normally integrated into Hadoop with Hadoop community
– NoSQL DB (Cassandra, MongoDB, Redis, Hbase,…)
– ElasticSearch
22. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data: Linearly Scalable
Some people questions that the system to handle 1 ~ 3GB of
data set is not Big Data
Well…. add more servers as more data in the future in Big Data platform
– it is linearly scalable once built
– n time more computing power ideally
Data Size: < 3 GB Data Size: 200 TB >
Add n
servers
23. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data Cluster
Are you ready for research now?
Large Scale Data Set with computing engine: ML, DS
Massive Data Set with
Computing Engines (Hadoop,
Spark)
24. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Experimental Results in AWS [PMBW18]
Execution times
Big Data Science
3 nodes:
–40min – 70mins
11 nodes
–10min – 20mins
25. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data is great for Any Small Business
Your data is the value and Big Data
Customer data
Operational data
You have your specific data
Big Company does not have a specific data as you have
Potentials
Your customer data
– Smart marketing and Sales
– Advertisement
Your operational data
– Efficient operation, For Example, Smart*:
• Smart Factory, Smart City
26. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data Data Analysis & Visualization
Sentiment Map of Alphago
Positive
Negative
27. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
K-Election 2017
(April 29 – May 9)
28. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
IoT of Smart Factory
28
29. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
IoT of Smart Factory (Cont’d)
29
30. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Businesses popular in 5 miles of CalStateLA,
USC , UCLA
31. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Jams and other traffic incidents reported
by users in Dec 2017 – Jan 2018: [DW19a]
32. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Contents
Myself
Introduction To Big Data
Deep Learning and Big Data
Big Data Predictive Analysis
Summary
33. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data Analysis and Prediction
Big Data Analysis
Hadoop, Spark, NoSQL DB, SAP HANA, ElasticSearch,..
Big Data for Data Analysis
– How to store, compute, analyze massive dataset?
Big Data Science
How to predict the future trend and pattern with the massive
dataset? => Machine Learning
34. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Spark
Limitation in MapReduce
Hard to program in Java
Batch Processing
– Not interactive
Disk storage for intermediate data
– Performance issue
Spark by UC Berkley AMP Lab
Started by Matei Zaharia in 2009,
– and open sourced in 2010
In-Memory storage for intermediate data
20 ~ 100 times faster than
– MapReduce
Good in Machine Learning => Big Data Science
– Iterative algorithms
35. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Spark (Cont’d)
Spark ML
Supports Machine Learning libraries
Process massive data set to build prediction models
36. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data Analysis and Prediction Flow
Data Collection
Batch API: Yelp, Google
Streaming: Twitter, Apache
NiFi, Kafka, StereamSets,
Storm
Open Data: Government
Data Storage
HDFS, S3, Object Storage,
NoSQL DB (Couchbase)…
Data Filtering
Hive, Pig
Data Analysis and Science
Hive, Pig, Spark, Deep Learning,
BI Tools (Qlik, Tableau, …)
Data Visualization
Qlik, Excel PowerMap,
Tableau, Looker, …
- Engineering:
- Big Data Engineering
- Big Data Analysis
- Data Visualization
- Research
- Big Data Science Deep Learning
37. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Traditional Data Science
The Gap
Big Data Engineers, Scientists, Analysts, etc.
Gap between Traditional Data Science and Big Data
Communities
38. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Leveraging Big Data Cluster [MCSPBW19, DW19a]
Existing Big Data cluster with massive data set with the
traditional ML
Issues and Solutions: Too
slow in large scale data
migration and single
server fails
Single server for
Python and R
Traditional Machine
Learning
Big Data Cluster
39. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Deep Learning
Machine Learning
Has been popular since Google Tensorflow
Multiple Cores in GPU
– Even with multiple GPUs and CPUs
Parallel Computing
GPU (Nvidia GTX 1660 Ti)
1280 CUDA cores
Deep Learning Libraries
Tensor Flow
PyTorch
Keras
Caffe, Caffe2
Microsoft Cognitive Toolkit (Previously CNTK)
Apache Mxnet
DeepLearning4j
…
41. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Deep Learning
CNN
Image Recognition
Video Analysis
NLP for classification, Prediction
RNN
Time Series Prediction
Speech Recognition/Synthesis
Image/Video Captioning
Text Analysis
– Conversation Q&A
GAN
Media Generation
– Photo Realistic Images
Human Image Synthesis: Fake faces
42. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Data Scale Driving: Deep Learning Process
Deep Learning and Massive Data [3]
“Machine Learning Yearning” Andrew Ng 2016
43. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Deep learning experts
The
Chasm
Big Data Engineers, Scientists, Analysts, etc.
Another Gap between Deep Learning and Big Data
Communities [6]
44. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Leveraging Big Data Cluster
Existing Big Data cluster with massive data set without using
Big Data
Too slow in data
migration and
single server fails
Single GPU
server for Deep
Learning?
Single server for
Python and R
Traditional
Machine Learning?
Big Data Cluster
45. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Deep Learning with Spark
What if we combine Deep Learning and Spark?
46. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Leveraging Big Data Cluster
Existing Big Data cluster
Big Data Engineering
Big Data Analysis
Big Data Science
Distributed Deep Learning
– Integrate Deep Learning to the cluster
Not needs data migration and can leverage the
parallel computing and existing large scale data
Big Data Cluster
47. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Deep Learning with Spark
Deep Learning Pipelines for Apache Spark
Databricks
TensorFlowOnSpark
Yahoo! Inc
BigDL (Distributed Deep Learning Library for Apache Spark)
Intel
DL4J (Deeplearning4j On Spark)
Skymind
Distributed Deep Learning with Keras & Spark
Elephas
48. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data Prediction with DDL
DDL: Distributed Deep Learning
Tensor Flow
Distributed Training and Inference in Spark cluster
DDL
49. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Spark ML and DDL [MKW19]
Deep Learning in Spark cluster
Distributed Deep Learning
DDL
DDL lib
DDL lib
Deep Learning in Spark
50. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Contents
Myself
Introduction To Big Data
Deep Learning and Big Data
Big Data Predictive Analysis
Summary
51. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Azure ML Studio and Spark ML Result Comparison:
Ad Click Fraud Prediction, 7GB data [GLBW19]
TWO-CLASS
DECISION
JUNGLE
(AzureML)
TWO-CLASS
DECISION
FOREST
(AzureML)
DECISION
TREE
CLASSIFIER
(Databricks)
RANDOM
FOREST
CLASSIFIER
(Databricks)
DECISION TREE
CLASSIFIER
(Balanced
Sample Data,
Oracle)
RANDOM
FOREST
CLASSIFIER
(Balanced
Sample Data,
Oracle)
AUC 0.905 0.997 0.815 0.746 0.896 0.893
PRECISION 1.0 0.992 0.822 0.878 0.935 0.934
RECALL 0.001 0.902 0.633 0.495 0.807 0.800
TP 35 47,199 86,683 67,726 111,187 110,220
FP 0 377 18,727 9,408 7,712 7,791
TN 52,306 406,228 7,112,961 7,122,280 545,302 545,223
FN 406,605 5,142 50,074 69,031 26,604 27,571
Run Time 2 hrs 2-3 hrs 22 mins 50 mins 24 sec 2 mins
52. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Big Data Science: Transaction Data Fraud Detection
[PMBW18]
Model Area under
ROC
Precision Recall
DecisionTreeClassifier
RandomForestClassifier 0.909573
LogisticRegression
Size: 470 MB (=> 718MB)
6,362,620 records
Not that large scale data comparing to data set > GB
https://www.kaggle.com/ntnu-testimon/paysim1
3 models in Spark Cluster with different combinations of the
parameters
Times taken: 1 hour with 3 Spark clsters
In theory of Linear Scalability: 2 minutes with 30 Spark clsters
53. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Experimental Results in AWS [PMBW18]
Execution times
3 nodes:
–40min – 70mins
11 nodes
–10min – 20mins
Shows Scalability
54. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Big Data Science in Smart * [DW19a]
Traffic Data Analysis and Prediction using Big Data
Smart*
– Smart Things
• Data collected from Cellphone apps
– Traffic Data from the driver and the cell phone
Data source:
Navigation app traffic data set from LA City Department*
– Information reported by users – Alerts
information captured by user’s device – Jams
*Limited authorization to access the full datasets 100 GB + original;
– Adopted limited dataset to 9 days (Dec 31– Jan 8, 2018)
– ~2GB
55. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Introduction
Provide real-time directions and up-to-date information
Traffic
Accidents
Road closure
Weather hazards
Lurking police vehicles and etc.
We are going to find out:
Areas with high volume of traffic (geography)
Peak-hours
Density of Alerts and Incidents
Traffic volume by road types
Prediction of traffic jam
56. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Experiment Environment:
Traditional Systems and Big Data
57. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
H/W Specification
Hadoop Spark Cluster
Number of nodes 6
OCPUs 12
CPU speed 2.2GHz
Memory 180 GB
Storage 682 GB
58. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Implementation Flow
Big Data Science and AI ML
Local Computer
Raw data
files (JSON)
Geo-Spatial
Visualization (3D map)
Dashboard for Analytics
Big Data Analysis:
Hadoop/Hive
Upload dataset to
HDFS
Parse JSON files using
Pandas
Create tables’ schema
Clean data
Create sample/summary
dataset for prediction and
visualization
Traditional Data
Science: Microsoft
Azure ML Studio
Upload sample dataset
Apply data
transformation
Split dataset for
training and scoring
Train model(s)
Evaluate model(s)
59. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Traffic Dashboard: Big Data Analysis
Peak
Peak
60. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Traffic Dashboard: Big Data Analysis (Cont’d)
Major areas of traffic are:
Downtown Los Angeles
Santa Monica
Hollywood
Freeway (highways)
61. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Video-Simulation of Traffic in LA (captured from users' devices)
62. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Video-Simulation of Traffic in LA (reported by app users)
63. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Features/columns in a dataset
location x, location y X and Y -coordinate of location
date_pst Pacific Time of the publication of traffic report
*date splits into month, day, hour, min, sec, weekday
speed driver’s captured speed in mph
length length of the traffic ahead in the route of user in meters
level jam level: 1 – 5
where (1: almost no jam) and (5: standstill jam)
64. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
MODEL Evaluation: Traditional Data Science
with Azure ML Studio
Model Accuracy Precision Recall AUC ROC
LR 0.662 0.662 1.0 0.571
BDT 0.805 0.832 0.884 0.868
DF 0.832 0.868 0.880 0.885
65. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Summary of Traffic Prediction with
Machine Learning
Model is based on sampled
dataset ~ 1M rows (100 MB):
Sampled using Spark as the data set
is 2GB
Best model - Decision Forest
Accuracy – 0.832
Precision - 0.868
Recall - 0.880
Area under the Curve – 0.885
Confusion Matrix
66. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Distributed Deep Learning in Big Data Cluster
[MKW19]
Predictive Analysis
Prediction of rating
– important measures for purchase and selling
Spark ML: ALS (Alternating Least Squares) algorithm
DDL (Distributed Deep Learning): Neural Collaborative Filtering (NCF)
Dataset : - https://s3.amazonaws.com/amazon-reviews-
pds/tsv/index.txt
Products reviewed between 2005 and 2015 are analyzed
Total product reviews : 9.57 million
File Size : 5.26 GB
67. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Summary: Performance
68. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Summary: Mean Absolute Error
69. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Training and Education
Emerging Technology every moment
IT companies lead the industry not university
How to catch up with?
– Training and Education
Company with new technology
Always deliver training
– Big Data
• Cloudera, Hortonworks
– AI Deep Learning
• Traditional Concept
– Stanford, UC Berkeley, edx, IBM, H2O
70. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Training (Cont’d)
Training by Company
3 - 4days/Week
– $2,500 - $3,000
– Practical
• with theory + hands-on exercise
• Instructor paid well
• Employer send their engineers to learn the new technology in a few
weeks
Education in University
Need an instructor who knows the new technology
– Not easy
• IT companies lead the industry not university
71. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Trained but No Experience with bad management in Korea
Sang-Ryung Battle:
From 2017 Korean
Blockbuster Movie,
“The Fortress”
(남한산성)
72. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Trained Well With Experience and Good management in Japan
Battle of Nagashino,
1575, Japan
73. Big Data AI Center (BigDAI / HiPIC)
Jongwook Woo
CalStateLA
Trained but No Experience with bad management in Korea (Cont’d)
Sang-Ryung Battle:
From 2017 Korean
Blockbuster Movie,
“The Fortress”
(남한산성)
74. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Contents
Myself
Introduction To Big Data
Deep Learning and Big Data
Big Data Predictive Analysis
Summary
75. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Summary
Introduction to Big Data
Definition in terms of platforms
Data and Predictive Analysis in Massive Data Set
Introduction to Deep Learning in Big Data
Distributed Deep Learning
Big Data Predictive Analysis
Big Data Science
Distributed Deep Learning
Education is important
76. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
Questions?
77. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
References
1. [W13] Jongwook Woo, DMKD-00150, “Market Basket Analysis Algorithms with MapReduce”, Wiley
Interdisciplinary Reviews Data Mining and Knowledge Discovery, Oct 28 2013, Volume 3, Issue 6, pp445-452, ISSN
1942-4795
2. [KCWW16] “Big Data Analysis using Spark for Collision Rate Near CalStateLA” , Manik Katyal, Parag Chhadva,
Shubhra Wahi & Jongwook Woo, https://globaljournals.org/GJCST_Volume16/1-Big-Data-Analysis-using-Spark.pdf
3. [PMBW18] Priyanka Purushu, Niklas Melcher, Bhagyashree Bhagwat, Jongwook Woo, "Predictive Analysis of
Financial Fraud Detection using Azure and Spark ML", Asia Pacific Journal of Information Systems (APJIS),
VOL.28│NO.4│December 2018, pp308~319
4. [MCSPBW19] Monika Mishra, Jaydeep Chopde, Maitri Shah, Pankti Parikh, Rakshith Chandan Babu, Jongwook Woo,
"Big Data Predictive Analysis of Amazon Product Review", KSII The 14th Asia Pacific International Conference on
Information Science and Technology (APIC-IST) 2019, pp141-147, ISSN 2093-0542
5. [GLBW19] Neha Gupta, Hai Anh Le, Maria Boldina, Jongwook Woo, "Predicting fraud of AD click using Traditional
and Spark ML", KSII The 14th Asia Pacific International Conference on Information Science and Technology (APIC-
IST) 2019, pp24-28, ISSN 2093-0542
6. [DW19a] Dalyapraz Dauletbak, Jongwook Woo, "Traffic Data Analysis and Prediction using Big Data", KSII The 14th
Asia Pacific International Conference on Information Science and Technology (APIC-IST) 2019, pp127-133, ISSN
2093-0542
7. [SW19] Ruchi Singh and Jongwook Woo, "Applications of Machine Learning Models on Yelp Data", Asia Pacific
Journal of Information Systems (APJIS), Vol.29, No.1, 2019, pp35-49, ISSN 2288-5404
78. Big Data Artificial Intelligence Center (BigDAI)
Jongwook Woo
CalStateLA
References
8. [MKW19] Monika Mishra, Mingoo Kang, Jongwook Woo, “Rating Prediction using Deep Learning and Spark”, The 11th
International Conference on Internet (ICONI 2019), Dec 15-18 2019, Hanoi, Vietnam
9. [DW19b] (Will be Published Dec 2019) Dalyapraz Dauletbak, Jongwook Woo, “Big Data Analysis and Prediction of Traffic in
Los Angeles”, in Transactions on Internet & Information Systems (TIIS)
10. Which Is Deeper - Comparison Of Deep Learning Frameworks On Spark, https://www.slideshare.net/SparkSummit/which-
is-deeper-comparison-of-deep-learning-frameworks-on-spark
11. Accelerating Machine Learning and Deep Learning At Scale with Apache Spark,
https://www.slideshare.net/SparkSummit/accelerating-machine-learning-and-deep-learning-at-scalewith-apache-spark-
keynote-by-ziya-ma
12. Deep Learning with Apache Spark and TensorFlow, https://databricks.com/blog/2016/01/25/deep-learning-with-apache-
spark-and-tensorflow.html
13. Overview of Smart Factory, https://www.slideshare.net/BrendanSheppard1/overview-of-smart-factory-solutions-
68137094/6
14. TensorFrames: Google Tensorflow on Apache Spark, https://www.slideshare.net/databricks/tensorframes-google-
tensorflow-on-apache-spark
15. Deep learning and Apache Spark, https://www.slideshare.net/QuantUniversity/deep-learning-and-apache-spark