This document discusses Hivemall, a machine learning library for Apache Hive and Spark. It was developed by Makoto Yui as a personal research project to make machine learning easier for SQL developers. Hivemall implements various machine learning algorithms like logistic regression, random forests, and factorization machines as user-defined functions (UDFs) for Hive, allowing machine learning tasks to be performed using SQL queries. It aims to simplify machine learning by abstracting it through the SQL interface and enabling parallel and interactive execution on Hadoop.
Writing Apache Spark and Apache Flink Applications Using Apache BahirLuciano Resende
Big Data is all about being to access and process data in various formats, and from various sources. Apache Bahir provides extensions to distributed analytic platforms providing them access to different data sources. In this talk we will introduce you to Apache Bahir and its various connectors that are available for Apache Spark and Apache Flink. We will also go over the details of how to build, test and deploy an Spark Application using the MQTT data source for the new Apache Spark 2.0 Structure Streaming functionality.
Fast, Scalable Graph Processing: Apache Giraph on YARNDataWorks Summit
Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph processing using in-memory or disk-based data sets, loosely following the model of Google`s Pregel. Many recent advances have left Giraph more robust, efficient, fast, and able to accept a variety of I/O formats typical for graph data in and out of the Hadoop ecosystem. Giraph's recent port to a pure YARN platform offers increased performance, fine-grained resource control, and scalability that Giraph atop Hadoop MRv1 cannot, while paving the way for ports to other platforms like Apache Mesos. Come see whats on the roadmap for Giraph, what Giraph on YARN means, and how Giraph is leveraging the power of YARN to become a more robust, usable, and useful platform for processing Big Graph datasets.
The Apache Way - Building Open Source Community in China - Luke HanLuke Han
My presentation at ApacheCon 2016 NA, talking about our practices to build open source community (Apache Kylin) in China, about the challenge, the culture different, the language and so on.
Also have a overview about Open Source in China, about the changing happening now there.
It's good reference for people have interesting to extend their community in China, to engage more Chinese even Asia developers to double their open source community and adoption.
Extending Pandas using Apache Arrow and NumbaUwe Korn
With the latest release of Pandas the ability to extend it with custom dtypes was introduced. Using Apache Arrow as the in-memory storage and Numba for fast, vectorized computations on these memory regions, it is possible to extend Pandas in pure Python while achieving the same performance of the built-in types. In the talk we implement a native string type as an example.
Machine learning in the enterprise is an iterative process. Data scientists will tweak or replace their learning algorithm in a small data sample until they find an approach that works for the business problem and then apply the Analytics to the full data set. Apache SystemML is a new system that accelerates this kind of exploratory algorithm development for large-scale machine learning problems. SystemML provides a high-level language to quickly implement and run machine learning algorithms on Spark. SystemML’s cost-based optimizer takes care of low-level decisions about how to use Spark’s parallelism, allowing users to focus on the algorithm and the real-world problem that the algorithm is trying to solve. This talk will introduce you to SystemML and get you started building declarative analytics with SystemML using a simple Zeppelin notebook and running on Apache Spark environment.
PayPal prvoides an online transfer money network. Each payment flow connects senders and receivers into a giant network where each sender/receiver is a node and each transaction is an edge. Traditionally, the risk score of a transaction is computed based on the characteristics of the involved sender/receiver/transaction. In this talk, we will describe a novel network inference approach to calculate transaction risk score that also includes the risk profile of neighboring senders and receivers using Apache Giraph. The approach reveals additional risk insights not possible with the traditional method. We leverage Hadoop to support a graph computation involving hundreds of millions of nodes and edges.
Data Science Languages and Industry AnalyticsWes McKinney
September 19, 2015 talk at Berkeley Institute for Data Science. On how comparatively poor JSON / structured data tools pose a challenge for the data science languages (Python, R, Julia, etc.).
Apache Kylin general introduction, including background, business needs and technical challenges, theory and architecture, features and some tech detail. Following with performance and benchmark, finally, ecosystem and roadmap.
More detail, please visit http://kylin.io or follow @ApacheKylin.
Logs are one of the most important sources to monitor and reveal some significant events of interest. In this presentation, we introduced an implementation of log streams processing architecture based on Apache Flink. With fluentd, different kinds of emitted logs are collected and sent to Kafka. After having processed by Flink, we try to build a dash board utilizing elasticsearch and kibana for visualization.
Writing Apache Spark and Apache Flink Applications Using Apache BahirLuciano Resende
Big Data is all about being to access and process data in various formats, and from various sources. Apache Bahir provides extensions to distributed analytic platforms providing them access to different data sources. In this talk we will introduce you to Apache Bahir and its various connectors that are available for Apache Spark and Apache Flink. We will also go over the details of how to build, test and deploy an Spark Application using the MQTT data source for the new Apache Spark 2.0 Structure Streaming functionality.
Fast, Scalable Graph Processing: Apache Giraph on YARNDataWorks Summit
Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph processing using in-memory or disk-based data sets, loosely following the model of Google`s Pregel. Many recent advances have left Giraph more robust, efficient, fast, and able to accept a variety of I/O formats typical for graph data in and out of the Hadoop ecosystem. Giraph's recent port to a pure YARN platform offers increased performance, fine-grained resource control, and scalability that Giraph atop Hadoop MRv1 cannot, while paving the way for ports to other platforms like Apache Mesos. Come see whats on the roadmap for Giraph, what Giraph on YARN means, and how Giraph is leveraging the power of YARN to become a more robust, usable, and useful platform for processing Big Graph datasets.
The Apache Way - Building Open Source Community in China - Luke HanLuke Han
My presentation at ApacheCon 2016 NA, talking about our practices to build open source community (Apache Kylin) in China, about the challenge, the culture different, the language and so on.
Also have a overview about Open Source in China, about the changing happening now there.
It's good reference for people have interesting to extend their community in China, to engage more Chinese even Asia developers to double their open source community and adoption.
Extending Pandas using Apache Arrow and NumbaUwe Korn
With the latest release of Pandas the ability to extend it with custom dtypes was introduced. Using Apache Arrow as the in-memory storage and Numba for fast, vectorized computations on these memory regions, it is possible to extend Pandas in pure Python while achieving the same performance of the built-in types. In the talk we implement a native string type as an example.
Machine learning in the enterprise is an iterative process. Data scientists will tweak or replace their learning algorithm in a small data sample until they find an approach that works for the business problem and then apply the Analytics to the full data set. Apache SystemML is a new system that accelerates this kind of exploratory algorithm development for large-scale machine learning problems. SystemML provides a high-level language to quickly implement and run machine learning algorithms on Spark. SystemML’s cost-based optimizer takes care of low-level decisions about how to use Spark’s parallelism, allowing users to focus on the algorithm and the real-world problem that the algorithm is trying to solve. This talk will introduce you to SystemML and get you started building declarative analytics with SystemML using a simple Zeppelin notebook and running on Apache Spark environment.
PayPal prvoides an online transfer money network. Each payment flow connects senders and receivers into a giant network where each sender/receiver is a node and each transaction is an edge. Traditionally, the risk score of a transaction is computed based on the characteristics of the involved sender/receiver/transaction. In this talk, we will describe a novel network inference approach to calculate transaction risk score that also includes the risk profile of neighboring senders and receivers using Apache Giraph. The approach reveals additional risk insights not possible with the traditional method. We leverage Hadoop to support a graph computation involving hundreds of millions of nodes and edges.
Data Science Languages and Industry AnalyticsWes McKinney
September 19, 2015 talk at Berkeley Institute for Data Science. On how comparatively poor JSON / structured data tools pose a challenge for the data science languages (Python, R, Julia, etc.).
Apache Kylin general introduction, including background, business needs and technical challenges, theory and architecture, features and some tech detail. Following with performance and benchmark, finally, ecosystem and roadmap.
More detail, please visit http://kylin.io or follow @ApacheKylin.
Logs are one of the most important sources to monitor and reveal some significant events of interest. In this presentation, we introduced an implementation of log streams processing architecture based on Apache Flink. With fluentd, different kinds of emitted logs are collected and sent to Kafka. After having processed by Flink, we try to build a dash board utilizing elasticsearch and kibana for visualization.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1VhSzmy.
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs. Filmed at qconlondon.com.
Robert Metzger is a PMC member at the Apache Flink project and a cofounder and software engineer at data Artisans. He is the author of many Flink components including the Kafka and YARN connectors.
Hadoop con 2016_9_10_王經篤(Jing-Doo Wang)Jing-Doo Wang
With the state-of-the-art computation mode, Map&Reduce,
This talk will present a novel approach to speed up the computation of extracting maximal repeats from tagged sequences and meanwhile computing the class frequency distribution of these repeats. An USA patent based on above approach is being applied as "Wang, Ching-Tu. Method for Extracting Maximal Repeat Patterns and Computing Frequency Distribution Tables. Patent Application Serial Number 15/208,994. 13 July 2016."
Yarn Resource Management Using Machine Learningojavajava
HadoopCon 2016 In Taiwan - How to maximum the utilization of Hadoop computing power is the biggest challenge for Hadoop administer. In this talk I will explain how we use Machine Learning to build the prediction model for the computing power requirements and setting up the MapReduce scheduler parameters dynamically, to fully utilize our Hadoop cluster computing power.
How to plan a hadoop cluster for testing and production environmentAnna Yen
Athemaster wants to share our experience to plan Hardware Spec, server initial and role deployment with new Hadoop Users. There are 2 testing environments and 3 production environments for case study.
This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source.
With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing.
In this talk, you will learn about:
1. What is Apache Flink stack and how it fits into the Big Data ecosystem?
2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment?
3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark.
4. Who is using Apache Flink?
5. Where to learn more about Apache Flink?
Hadoop or Spark: is it an either-or proposition? By Slim BaltagiSlim Baltagi
Hadoop or Spark: is it an either-or proposition? An exodus away from Hadoop to Spark is picking up steam in the news headlines and talks! Away from marketing fluff and politics, this talk analyzes such news and claims from a technical perspective.
In practical ways, while referring to components and tools from both Hadoop and Spark ecosystems, this talk will show that the relationship between Hadoop and Spark is not of an either-or type but can take different forms such as: evolution, transition, integration, alternation and complementarity.
Presto & differences between popular SQL engines (Spark, Redshift, and Hive)Holden Ackerman
This is a presentation given at a Big Data Boulder / Denver Meetup event by Ashish Dubey, a Senior Solutions Architect at Qubole.
The following slides cover a background of Presto and its architecture, and how it differs in both performance and cost from traditional Hadoop / Hive for Adhoc queries as well as SparkSQL, Impala, Tez, and Redshift.
There are also several slides about how Qubole has been involved with the open-source Apache Presto project, along with performance optimizing contributions.
Qubole is a big data analytics software that has solved many headaches around the traditional model of big data (Hadoop, Spark, Presto) and cloud computing in popular IaaS providers: AWS, Google Cloud, Microsoft Azure, and Oracle BMC.
Detailed presentation on big data hadoop +Hadoop Project Near Duplicate Detec...Ashok Royal
Bigdata Hadoop, Its components and a Hadoop project is described in Details.
Visit http://hadoop-beginners.blogspot.com to see Hadoop Tutorials.
Thanks for the visit. :)
Unified Batch and Real-Time Stream Processing Using Apache FlinkSlim Baltagi
This talk was given at Capital One on September 15, 2015 at the launch of the Washington DC Area Apache Flink Meetup. Apache flink is positioned at the forefront of 2 major trends in Big Data Analytics:
- Unification of Batch and Stream processing
- Multi-purpose Big Data Analytics frameworks
In these slides, we will also find answers to the burning question: Why Apache Flink? You will also learn more about how Apache Flink compares to Hadoop MapReduce, Apache Spark and Apache Storm.
How do you analyze a Petabyte of data?
The Spark Python API or PySpark exposes the Spark programming model to Python. Apache® Spark™ is open-source and is one of the most popular Big Data frameworks for scaling up your tasks in a cluster. It was developed to utilize distributed, in-memory data structures to improve data processing speeds for massive amounts of data.
We’ll also look into Spark SQL - Apache Spark’s module for working with structured data and MLlib - Apache Spark’s scalable machine learning library.
What will you learn?
Perform Big Data analysis with PySpark
Use SQL queries with DataFrames by using the Spark SQL module
Use Machine learning with MLlib library
Apache Hivemall is a scalable machine learning library for Apache Hive, Apache Spark, and Apache Pig.
Hivemall provides a number of machine learning functionalities across classification, regression, ensemble learning, and feature engineering through UDFs/UDAFs/UDTFs of Hive.
We have released the first Apache release (v0.5.0-incubating) on Mar 5, 2018 and the project plans to release v0.5.2 in Q2, 2018.
We will first give a quick walk-through of features, usages, what's new in v0.5.0, and future roadmaps of Apache Hivemall. Next, we will introduce Hivemall on Apache Spark in depth such as DataFrame integration and Spark 2.3 supports in Hivemall.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
2. Ø 2015.04~ Research Engineer at Treasure Data,
Inc.
• My mission is developing ML-as-a-Service in a Hadoop-as-
a-service company
Ø 2010.04-2015.03 Senior Researcher at National
Institute of Advanced Industrial Science and
Technology, Japan. 産業技術総合研究所
• Developed Hivemall as a personal research project
Ø 2009.03 Ph.D. in Computer Science from NAIST
• Majored in Parallel Data Processing, not ML then
Ø Visiting scholar in CWI, Amsterdam and Univ. Edinburgh
Little about me ..
2016/09/09 HadoopCon 16, Taipei 2
3. 2016/09/09 HadoopCon 16, Taipei 3
Hiro Yoshikawa
CEO
Kaz Ota
CTO
Sada Furuhashi
Chief Architect
Open source business
veteran
Founder - world’s
largest Hadoop group
Invented Fluentd,
Messagepack
TODAY
100+ Employees, 30M+ funding
2015
New office in Seoul, Korea
2013
New office in Tokyo, Japan
2012
Founded in Mountain View, CA
Investors
Jerry Yang
Yahoo! Founder
Bill Tai
Angel Investor
Yukihiro Matsumoto
Ruby Inventor
Sierra Ventures - Tim Guleri
Entrerprise Software
Scale Ventures - Andy Vitus
B2B SaaS
Treasure Data
5. 2016/09/09 HadoopCon 16, Taipei 5
Microsoft Operation Management Suite and Google Cloud Platform
(Kubernates) are using Fluentd for log collection
Point
Our technology users
6. 2016/09/09 HadoopCon 16, Taipei 6
Microsoft Operation Management Suite and Google Cloud Platform
(Kubernates) are using Fluentd for log collection
Point
Our technology users
15. List of supported Algorithms
Classification
✓ Perceptron
✓ Passive Aggressive (PA, PA1,
PA2)
✓ Confidence Weighted (CW)
✓ Adaptive Regularization of
Weight Vectors (AROW)
✓ Soft Confidence Weighted
(SCW)
✓ AdaGrad+RDA
✓ Factorization Machines
✓ RandomForest Classification
15
Regression
✓Logistic Regression (SGD)
✓AdaGrad (logistic loss)
✓AdaDELTA (logistic loss)
✓PA Regression
✓AROW Regression
✓Factorization Machines
✓RandomForest Regression
SCW is a good first choice
Try RandomForest if SCW does
not work
Logistic regression is good for
getting a probability of a positive
class
Factorization Machines is good
where features are sparse and
categorical ones
2016/09/09 HadoopCon 16, Taipei
19. • CTR prediction of Ad click logs
• Algorithm: Logistic regression
• Freakout Inc., Smartnews, and more
• Gender prediction of Ad click logs
• Algorithm: Classification
• Scaleout Inc.
• Item/User recommendation
• Algorithm: Recommendation
• Wish.com, GMO pepabo
Industry use cases of Hivemall
19
Problem: Recommendation using hot-item is hard in hand-crafted
product market because each creator sells few single items (will
soon become out-of-stock)
2016/09/09 HadoopCon 16, Taipei
minne.com
20. • CTR prediction of Ad click logs
• Algorithm: Logistic regression
• Freakout Inc., Smartnews, and more
• Gender prediction of Ad click logs
• Algorithm: Classification
• Scaleout Inc.
• Item/User recommendation
• Algorithm: Recommendation
• Wish.com, GMO pepabo
• Value prediction of Real estates
• Algorithm: Regression
• Livesense
Industry use cases of Hivemall
202016/09/09 HadoopCon 16, Taipei
21. • CTR prediction of Ad click logs
• Algorithm: Logistic regression
• Freakout Inc., Smartnews, and more
• Gender prediction of Ad click logs
• Algorithm: Classification
• Scaleout Inc.
• Item/User recommendation
• Algorithm: Recommendation
• Wish.com, GMO pepabo
• Value prediction of Real estates
• Algorithm: Regression
• Livesense
• User score calculation
• Algrorithm: Regression
• Klout
Industry use cases of Hivemall
21
bit.ly/klout-hivemall
2016/09/09 HadoopCon 16, Taipei
Influencer marketing
klout.com
36. Create external table e2006tfidf_train (
rowid int,
label float,
features ARRAY<STRING>
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '¥t'
COLLECTION ITEMS TERMINATED BY ",“
STORED AS TEXTFILE LOCATION '/dataset/E2006-tfidf/train';
How to use Hivemall - Data preparation
Define a Hive table for training/testing data
362016/09/09 HadoopCon 16, Taipei
40. How to use Hivemall - Training
CREATE TABLE lr_model AS
SELECT
feature,
avg(weight) as weight
FROM (
SELECT logress(features,label,..)
as (feature,weight)
FROM train
) t
GROUP BY feature
Training by logistic regression
map-only task to learn a prediction model
Shuffle map-outputs to reduces by feature
Reducers perform model averaging
in parallel
402016/09/09 HadoopCon 16, Taipei
41. How to use Hivemall - Training
CREATE TABLE news20b_cw_model1 AS
SELECT
feature,
voted_avg(weight) as weight
FROM
(SELECT
train_cw(features,label)
as (feature,weight)
FROM
news20b_train
) t
GROUP BY feature
Training of Confidence Weighted Classifier
Vote to use negative or positive
weights for avg
+0.7, +0.3, +0.2, -0.1, +0.7
Training for the CW classifier
412016/09/09 HadoopCon 16, Taipei
43. How to use Hivemall - Prediction
CREATE TABLE lr_predict
as
SELECT
t.rowid,
sigmoid(sum(m.weight)) as prob
FROM
testing_exploded t LEFT OUTER JOIN
lr_model m ON (t.feature = m.feature)
GROUP BY
t.rowid
Prediction is done by LEFT OUTER JOIN
between test data and prediction model
No need to load the entire model into memory
432016/09/09 HadoopCon 16, Taipei
44. Real-time prediction
Machine
Learning
Batch Training on Hadoop
Online Prediction on RDBMS
Prediction
Model
Label
Feature
Vector
Feature Vector
Label
Export
prediction model
44
bit.ly/hivemall-rtp
2016/09/09 HadoopCon 16, Taipei
55. ü ChangeFinder
• Efficient algorithm for finding change point and outliers
from timeseries data
55
Coming New Features - already merged in Master
J. Takeuchi and K. Yamanishi, “A Unifying Framework for Detecting
Outliers and Change Points from Time Series,” IEEE transactions on
Knowledge and Data Engineering, pp.482-492, 2006.
2016/09/09 HadoopCon 16, Taipei
56. ü ChangeFinder
• Efficient algorithm for finding change point and outliers
from timeseries data
56
Coming New Features - already merged in Master
J. Takeuchi and K. Yamanishi, “A Unifying Framework for Detecting
Outliers and Change Points from Time Series,” IEEE transactions on
Knowledge and Data Engineering, pp.482-492, 2006.
2016/09/09 HadoopCon 16, Taipei