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History and Trend of Big Data and Deep Learning

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Introduction to Big Data and Distributed Deep Learning on Big Data using Spark

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History and Trend of Big Data and Deep Learning

  1. 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. 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. 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. 4. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Universities in Los Angeles West North
  5. 5. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Universities in Los Angeles
  6. 6. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA California State University Los Angeles
  7. 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. 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. 9. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Myself: Partners for Services
  10. 10. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Myself: Collaborations
  11. 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. 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. 13. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Data Handling Systems: Traditional Way
  14. 14. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Data Handling: Traditional Way Becomes too Expensive
  15. 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. 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 1409년(태종 9) 최해산(崔海山), 아버지 최무선(崔茂宣) [출처] 조선의 비밀 병기 : 총통기 화차(銃筒機火車)|작성자 도심
  17. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Big Data Data Analysis & Visualization Sentiment Map of Alphago Positive Negative
  27. 27. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA K-Election 2017 (April 29 – May 9)
  28. 28. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA IoT of Smart Factory 28
  29. 29. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA IoT of Smart Factory (Cont’d) 29
  30. 30. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Businesses popular in 5 miles of CalStateLA, USC , UCLA
  31. 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. 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. 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. 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. 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. 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. 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. 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. 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  …
  40. 40. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA From Neural Networks to Deep Learning Deep learning – Different types of architectures Generative Adversarial Networks (GAN) Convolutional Neural Networks (CNN) Neural Networks (NN) 7 © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Recurrent Neural Networks (RNN) & Long-Short Term Memory (LSTM) Ref: SAP Enterprise Deep Learning with TensorFlow
  41. 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. 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. 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. 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. 45. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Deep Learning with Spark What if we combine Deep Learning and Spark?
  46. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 56. Big Data AI Center (BigDAI / HiPIC) Jongwook Woo CalStateLA Experiment Environment: Traditional Systems and Big Data
  57. 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. 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. 59. Big Data AI Center (BigDAI / HiPIC) Jongwook Woo CalStateLA Traffic Dashboard: Big Data Analysis Peak Peak
  60. 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. 61. Big Data AI Center (BigDAI / HiPIC) Jongwook Woo CalStateLA Video-Simulation of Traffic in LA (captured from users' devices)
  62. 62. Big Data AI Center (BigDAI / HiPIC) Jongwook Woo CalStateLA Video-Simulation of Traffic in LA (reported by app users)
  63. 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. 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. 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. 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. 67. Big Data AI Center (BigDAI / HiPIC) Jongwook Woo CalStateLA Summary: Performance
  68. 68. Big Data AI Center (BigDAI / HiPIC) Jongwook Woo CalStateLA Summary: Mean Absolute Error
  69. 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. 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. 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. 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. 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. 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. 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. 76. Big Data Artificial Intelligence Center (BigDAI) Jongwook Woo CalStateLA Questions?
  77. 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. 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

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