Bigdata Machine Learning Platform

•

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Mk Kim

Datos y análisis

2BICubeTM
Bigdata Machine Learning Platform
Sqoop Flume
HDFS (Hadoop Distributed File System)
Zookeeper
Yarn
(2.0)
MapReduce
Hive
Pig
Mahout
Mashup
RDB
Preprocess Classify Cluster Associate Visual
BICube
RDB Mashup SNS Crawler
Authentication
CubeManager
Log Crawler
Machine Learning Engine
Spark
Spark
SQL
Spark
Stream GraphXML
Storm Kafka Akka
BioinfoManipul
Mesos
BICubeTM
Architecture

3BICubeTM
Bigdata Machine Learning Platform
•MultiLinearRegression, KoreanMorpheme
•Logistic Regression, SVM, Adaboost, Neural Network
•RandomForest, NaiveBayssian, Timeseries Analysis
•Kmeans, LDA, HMM, PCA, SVD, CRF, TDA
•Covariance, Correlation, ROC, LSA, EigenFace Image
•CF Recommender, ConfusionMatrix, AUC
•Bagging, Wagging, ArcX4,LogitBoost,ModestAdaboost
•SAMME,BinaryClibration, IsonicCalibration...
•Discriminant Adaptive Nearist Neighbor
•Hierachical agglomerative clustering
•Kernel Recursive Least Square,Radial Base Network
•Local scaled density base clustering, EMGaussianMixture
•Sequential minimum optimization,Dual coordinate Descent
•T-Test, F-Test, G-Test, Chi-square-Test Anova etc….
BICubeTM
Algorithms

4BICubeTM
Bigdata Machine Learning Platform
Document-Topic Matirx ViewerDocument-Topic Matirx Viewer
Latent Dirichlet Allocation on Hadoop

5BICubeTM
Bigdata Machine Learning Platform
Word-Topic Matirx ViewerWord-Topic Matirx Viewer
Latent Dirichlet Allocation on Hadoop

6BICubeTM
Bigdata Machine Learning Platform
Document Similarity Visualization

7BICubeTM
Bigdata Machine Learning Platform
Document Similarity Matrix

8BICubeTM
Bigdata Machine Learning Platform
Word Cloud from SNS

9BICubeTM
Bigdata Machine Learning Platform
Ontology from Unstructured Document

10BICubeTM
Bigdata Machine Learning Platform
Confusion Matrix for Accuracy Maesure

11BICubeTM
Bigdata Machine Learning Platform
Latent Semantic Visualization

12BICubeTM
Bigdata Machine Learning Platform
Latent Semantic Visualization

13BICubeTM
Bigdata Machine Learning Platform
3D Visualization on GPU3D Visualization on GPU
Unstructured Document Clustering

14BICubeTM
Bigdata Machine Learning Platform
Association Rule from MBA

15BICubeTM
Bigdata Machine Learning Platform
Computer Vision on Hadoop
Image Analysis from Hadoop

16BICubeTM
Bigdata Machine Learning Platform
Face Recognition from Hadoop

17BICubeTM
Bigdata Machine Learning Platform
OLAP Analysis from Data Warehouse

18BICubeTM
Bigdata Machine Learning Platform
GEO Analysis for location data

19BICubeTM
Bigdata Machine Learning Platform
PDF Parser on Hadoop HDFS

20BICubeTM
Bigdata Machine Learning Platform
Trade Market Prediction
Recurrent Neural Network
Feedfoward Neural Network(Elman)
Counterpropagation Neural Network
Hopfield Neural Network
Boltzman Neural Network
Neat Neural Network
Som Neural Network
Art 1 Neural Network
Bam Neural Network
Adaline Neural Network
Stock Market Analysis using ANN

21BICubeTM
Bigdata Machine Learning Platform
Log Analizer for machine data

22BICubeTM
Bigdata Machine Learning Platform
Mash-up data from multi-datasource

23BICubeTM
Bigdata Machine Learning Platform
Data Visualization

24BICubeTM
Bigdata Machine Learning Platform
Data Visualization

25BICubeTM
Bigdata Machine Learning Platform
Parallel cordinate & Facet Analysis

26BICubeTM
Bigdata Machine Learning Platform
PointCloud on GPU for Topological Data AnalysisPointCloud on GPU for Topological Data Analysis
3D Point Cloud for TDA

27BICubeTM
Bigdata Machine Learning Platform
Module Manager Add components by yourself–
Module Manager by DIY type

28BICubeTM
Bigdata Machine Learning Platform
Process Manager- Process UI Design Manager
UI Manager by DIY type

BICubeTM
Thank You
BICubeTM
http:www.bicube.co.kr
daengky@bicube.co.kr

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Pre-aggregation is a powerful analytics technique as long as the measures being computed are reaggregable. Counts reaggregate with SUM, minimums with MIN, maximums with MAX, etc. The odd one out is distinct counts, which are not reaggregable. Traditionally, the non-reaggregability of distinct counts leads to an implicit restriction: whichever system computes distinct counts has to have access to the most granular data and touch every row at query time. Because of this, in typical analytics architectures, where fast query response times are required, raw data has to be duplicated between Spark and another system such as an RDBMS. This talk is for everyone who computes or consumes distinct counts and for everyone who doesn’t understand the magical power of HyperLogLog (HLL) sketches. We will break through the limits of traditional analytics architectures using the advanced HLL functionality and cross-system interoperability of the spark-alchemy open-source library, whose capabilities go beyond what is possible with OSS Spark, Redshift or even BigQuery. We will uncover patterns for 1000x gains in analytic query performance without data duplication and with significantly less capacity. We will explore real-world use cases from Swoop’s petabyte-scale systems, improve data privacy when running analytics over sensitive data, and even see how a real-time analytics frontend running in a browser can be provisioned with data directly from Spark.

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In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

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In this deck from FOSDEM'19, Christoph Angerer from NVIDIA presents: Rapids - Data Science on GPUs. "The next big step in data science will combine the ease of use of common Python APIs, but with the power and scalability of GPU compute. The RAPIDS project is the first step in giving data scientists the ability to use familiar APIs and abstractions while taking advantage of the same technology that enables dramatic increases in speed in deep learning. This session highlights the progress that has been made on RAPIDS, discusses how you can get up and running doing data science on the GPU, and provides some use cases involving graph analytics as motivation. GPUs and GPU platforms have been responsible for the dramatic advancement of deep learning and other neural net methods in the past several years. At the same time, traditional machine learning workloads, which comprise the majority of business use cases, continue to be written in Python with heavy reliance on a combination of single-threaded tools (e.g., Pandas and Scikit-Learn) or large, multi-CPU distributed solutions (e.g., Spark and PySpark). RAPIDS, developed by a consortium of companies and available as open source code, allows for moving the vast majority of machine learning workloads from a CPU environment to GPUs. This allows for a substantial speed up, particularly on large data sets, and affords rapid, interactive work that previously was cumbersome to code or very slow to execute. Many data science problems can be approached using a graph/network view, and much like traditional machine learning workloads, this has been either local (e.g., Gephi, Cytoscape, NetworkX) or distributed on CPU platforms (e.g., GraphX). We will present GPU-accelerated graph capabilities that, with minimal conceptual code changes, allows both graph representations and graph-based analytics to achieve similar speed ups on a GPU platform. By keeping all of these tasks on the GPU and minimizing redundant I/O, data scientists are enabled to model their data quickly and frequently, affording a higher degree of experimentation and more effective model generation. Further, keeping all of this in compatible formats allows quick movement from feature extraction, graph representation, graph analytic, enrichment back to the original data, and visualization of results. RAPIDS has a mission to build a platform that allows data scientist to explore data, train machine learning algorithms, and build applications while primarily staying on the GPU and GPU platforms." Learn more: https://rapids.ai/ and https://fosdem.org/2019/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

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Context-aware apps - apps that know who you are, where you are, and what you've done - have been all the rage the last few years. Facebook's news feeds, Google Now, and Amazon Recommendations are examples of context-aware applications. Over the last few years, advancements in machine learning, big data, NoSQL, and API technologies has drastically reduced the complexity of building such apps, but requires a brand new approach system architecture. This presentation covers: Lambda architecture and Microservices - two new architectural styles to build contextual apps at scale How companies like Twitter and Netflix have implemented lambda architecture and microservices for recommendations, targeting, and more How Apigee uses both new architectures to implement predictive analytics through Insights (our big data predictive analytics product)

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Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line. Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together. Reasons to attend: Learn how AWS can help you process and make better use of your data with meaningful insights. Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions. Learn about real time data processing with Amazon Kinesis.

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