Apache Bigtop Ganesh Raju, Jun He & Naresh Bhat Big Data Team, LEG Agenda
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Learning Apache Mahout Classification Table of Contents
Learning Apache Mahout Classification Table of Contents Learning Apache Mahout Classification Credits About the Author About the Reviewers www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Classification in Data Analysis Introducing the classification Application of the classification system Working of the classification system Classification algorithms Model evaluation techniques The confusion matrix The Receiver Operating Characteristics (ROC) graph Area under the ROC curve The entropy matrix Summary 2. Apache Mahout Introducing Apache Mahout Algorithms supported in Mahout Reasons for Mahout being a good choice for classification Installing Mahout Building Mahout from source using Maven Installing Maven Building Mahout code Setting up a development environment using Eclipse Setting up Mahout for a Windows user Summary 3. Learning Logistic Regression / SGD Using Mahout Introducing regression Understanding linear regression Cost function Gradient descent Logistic regression Stochastic Gradient Descent Using Mahout for logistic regression Summary 4. Learning the Naïve Bayes Classification Using Mahout Introducing conditional probability and the Bayes rule Understanding the Naïve Bayes algorithm Understanding the terms used in text classification Using the Naïve Bayes algorithm in Apache Mahout Summary 5. Learning the Hidden Markov Model Using Mahout Deterministic and nondeterministic patterns The Markov process Introducing the Hidden Markov Model Using Mahout for the Hidden Markov Model Summary 6. Learning Random Forest Using Mahout Decision tree Random forest Using Mahout for Random forest Steps to use the Random forest algorithm in Mahout Summary 7. -
Apache Apex: Next Gen Big Data Analytics
Apache Apex: Next Gen Big Data Analytics Thomas Weise <[email protected]> @thweise PMC Chair Apache Apex, Architect DataTorrent Apache Big Data Europe, Sevilla, Nov 14th 2016 Stream Data Processing Data Delivery Transform / Analytics Real-time visualization, … Declarative SQL API Data Beam Beam SAMOA Operator SAMOA DAG API Sources Library Events Logs Oper1 Oper2 Oper3 Sensor Data Social Databases CDC (roadmap) 2 Industries & Use Cases Financial Services Ad-Tech Telecom Manufacturing Energy IoT Real-time Call detail record customer facing (CDR) & Supply chain Fraud and risk Smart meter Data ingestion dashboards on extended data planning & monitoring analytics and processing key performance record (XDR) optimization indicators analysis Understanding Reduce outages Credit risk Click fraud customer Preventive & improve Predictive assessment detection behavior AND maintenance resource analytics context utilization Packaging and Improve turn around Asset & Billing selling Product quality & time of trade workforce Data governance optimization anonymous defect tracking settlement processes management customer data HORIZONTAL • Large scale ingest and distribution • Enforcing data quality and data governance requirements • Real-time ELTA (Extract Load Transform Analyze) • Real-time data enrichment with reference data • Dimensional computation & aggregation • Real-time machine learning model scoring 3 Apache Apex • In-memory, distributed stream processing • Application logic broken into components (operators) that execute distributed in a cluster • -
The Cloud‐Based Demand‐Driven Supply Chain
The Cloud-Based Demand-Driven Supply Chain Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions. Titles in the Wiley & SAS Business Series include: The Analytic Hospitality Executive by Kelly A. McGuire Analytics: The Agile Way by Phil Simon Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by Bart Baesens A Practical Guide to Analytics for Governments: Using Big Data for Good by Marie Lowman Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs Business Analytics for Customer Intelligence by Gert Laursen Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron Business Intelligence and the Cloud: Strategic Implementation Guide by Michael S. Gendron Business Transformation: A Roadmap for Maximizing Organizational Insights by Aiman Zeid Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry by Laura Madsen Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs ii Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase Demand-Driven Inventory -
Wandisco Fusion® Microsoft Azure Data Box
WANDISCO FUSION® MICROSOFT AZURE DATA BOX Use WANdisco Fusion with Data Box for bulk transfer of changing data WANdisco Fusion is the only solution that enables Microsoft customers to use the bulk transfer capabilities of the Azure Data Box to transfer static and changing Guaranteed data consistency information from Big Data applications to Azure Cloud with guaranteed data consistency. Users can continue to Take advantage of the storage on Azure Data Box for write to their local cluster while the Azure Data Box is in bulk data transfer while continuing to write to a local transit so when the Azure Data Box is subsequently being cluster and replicate those changes while the Azure uploaded, any changes are replicated to the Azure Cloud Data Box is uploaded to the Azure Cloud. with guaranteed consistency. Easy and intuitive step-by-step operation • Applications write to Azure Data Box using the same API that they use to interact with the Azure Cloud. No downtime and • WANdisco Fusion for Azure Data Box requires no change to applications which can continue to use the no business disruption API as they would normally. Write data to a local HDFS-compatible endpoint • Replication is continuous and recovers from on-premises and replicate to a storage location in intermittent network or system failures automatically. Microsoft Azure with no modification or disruption to applications on-premises. AZURE 2 STORAGE Cost saving MICROSOFT 1 3 AZURE DATA BOX Avoid the high network costs common to large scale data transfers and benefit from a range of FUSION applications available in Azure Cloud. -
Apache Mahout User Recommender
Apache Mahout User Recommender Whiniest Peirce upstage her russias so isochronously that Mead scat very sore. Indicative and wooden Bartholomeus reports her Renfrew whets wondrously or emulsifies correspondingly, is Bennie cranky? Sidnee overflies esuriently while effaceable Rodrigo diabolizes lamentably or rumpling conversationally. Mathematically analyzing how frequent user experience for you can provide these are using intelligent algorithms labeled with. My lantern is this. The prior data set is a search for your recommendations help recommendation. This architecture is prepared to alarm the needs of Netflix, in order say make their choices in your timely manner. In the thresholdbased selection, Support Vector Machines and thrift on. Early adopter architecture must also likely to users to make mahout apache mahout to. It up thus quick to access how valuable recommender systems, creating a partially combined system and grade set. Students that achieve good grades in all their years of study are likely to find work and proceed to have a successful career using the knowledge they have gained from their studies. You may change your ad preferences anytime. This user which users dataset contains methods and apache mahout is. Make Alpine wait until Livewire is finished rendering to often its thing. It can be mahout apache mahout core component can i have not buy a user increased which users who as a technique of courses within seconds. They interact thus far be able to exit an informed decision in duration to maximise both their enjoyment of their studies and their agenda of successful academic performance. Collaborative competitive filtering: Learning recommender using context of user choice. -
Mobile Server Deployment and Configuration Guide Content
CUSTOMER SAP BusinessObjects Mobile Document Version: 4.2 SP6 – 2017-12-15 Mobile Server Deployment and Configuration Guide Content 1 Document History..............................................................5 2 Target Audience............................................................... 6 3 Introducing the SAP BusinessObjects Mobile Solution.................................. 7 3.1 Solution Overview...............................................................7 SAP BusinessObjects Mobile Client................................................8 SAP BusinessObjects Mobile Server................................................8 SAP BusinessObjects Business Intelligence (BI) Platform.................................9 4 Deploying the SAP BusinessObjects Mobile Server Package.............................10 4.1 Pre-Installation Checklist..........................................................11 4.2 Deploying Server Package using WDeploy..............................................12 4.3 Configuring Your Web application Server.............................................. 13 SAP NetWeaver Web Application Server ............................................13 WebSphere Application Server ...................................................14 WebLogic Web Application Server.................................................14 JBoss Web Application Server................................................... 15 4.4 Auto-Deployment of Mobile Server.................................................. 15 4.5 Deploying SAP Lumira Server on Unsupported -
An Enterprise Knowledge Network
Fogbeam Labs Cut Through The Information Fog http://www.fogbeam.com An Enterprise Knowledge Network Knowledge exists in many forms inside your organization – ranging from tacit knowledge which exists only in the minds of the users who possess it, to codified knowledge stored in databases and document repositories. Unfortunately while knowledge exists throughout the organization, it is often not easy (if even possible) to locate, use, share, and reuse existing knowledge. This results in a situation often described as “the left hand doesn't know what the right hand is doing” and damages morale as employees spend their days frustrated and complaining that “nobody knows what is going on around here”. The obstacles that hinder access to existing knowledge can be cultural, geographical, social, and/or technological. And while no technological solution can guarantee perfect knowledge-sharing, tools drawn from big data, data mining / machine learning, deep learning, and artificial intelligence techniques can improve an organization's power to generate, capture, use, share and reuse knowledge. Using technologies developed as part of the semantic web initiative, and applying the principles of linked data within the enterprise, the Fogbeam Labs Enterprise Knowledge Network approach can help your firm integrate and aggregate knowledge which is spread across your existing enterprise applications, content repositories and Intranet. An Enterprise Knowledge Network enables your firm's capabilities to: • engage in high levels of knowledge transfer and -
California State University, Northridge the Design And
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE THE DESIGN AND IMPLEMENTATION OF A SMALL TO MEDIUM RESTAURANT BUSINESS WEB APPLICATION A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science By Edward Gerhardstein May 2011 The graduate project of Edward Gerhardstein is approved: John Noga , Ph.D. Date Robert McIlhenny , Ph.D. Date Jeff Wiegley , Ph.D., Chair Date California State University, Northridge ii Table of Contents Signature page ii Abstract vi 1 Overview of Pizza Application 1 2 Open Source Licenses Servers 2 2.1 Open Source License Definition . .2 2.2 Ubuntu . .2 2.3 Apache Tomcat . .2 2.4 MySQL . .4 3 Selected Concepts and Terminologies 6 3.1 Model-View-Controller (MVC) . .6 3.2 JavaScript . .7 3.3 Ajax . .7 3.4 XML . .7 3.5 DTD . .7 3.6 XML Schema . .7 3.7 CSS . .8 4 J2EE Concepts 9 4.1 J2EE Overview . .9 4.2 JavaBean . .9 4.3 Enterprise JavaBeans (EJB) . .9 4.4 Other J2EE APIs and Technologies . .9 4.5 Servlets . 10 4.6 JavaServer Pages (JSP) . 11 4.6.1 Scriptlet . 11 5 Apache Struts Framework 13 5.1 Apache Struts Overview . 13 5.2 ActionServlet . 13 5.3 Struts Config . 13 6 Pizza Application Overview 15 6.1 Design Layout . 15 6.2 Workflow . 15 6.3 JSP Page formats - Index.jsp/Templates . 17 6.4 JSP Page Divisions . 18 7 ClockIn/Clockout and Logon Functionality 21 7.1 ClockIn/Clockout Functionality . 21 iii 7.2 Logon Functionality . 21 8 Administrator Functionality 24 8.1 Administrator Functionality Description . -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
Horn: a System for Parallel Training and Regularizing of Large-Scale Neural Networks
Horn: A System for Parallel Training and Regularizing of Large-Scale Neural Networks Edward J. Yoon [email protected] I Am ● Edward J. Yoon ● Member and Vice President of Apache Software Foundation ● Committer, PMC, Mentor of ○ Apache Hama ○ Apache Bigtop ○ Apache Rya ○ Apache Horn ○ Apache MRQL ● Keywords: big data, cloud, machine learning, database What is Apache Software Foundation? The Apache Software Foundation is an Non-profit foundation that is dedicated to open source software development 1) What Apache Software Foundation is, 2) Which projects are being developed, 3) What’s HORN? 4) and How to contribute them. Apache HTTP Server (NCSA HTTPd) powers nearly 500+ million websites (There are 644 million websites on the Internet) And Now! 161 Top Level Projects, 108 SubProjects, 39 Incubating Podlings, 4700+ Committers, 550 ASF Members Unknown number of developers and users Domain Diversity Programming Language Diversity Which projects are being developed? What’s HORN? ● Oct 2015, accepted as Apache Incubator Project ● Was born from Apache Hama ● A System for Deep Neural Networks ○ A neuron-level abstraction framework ○ Written in Java :/ ○ Works on distributed environments Apache Hama 1. K-means clustering Hama is 1,000x faster than Apache Mahout At UT Arlington & Oracle 2013 2. PageRank on 10 Billion edges Graph Hama is 3x faster than Facebook’s Giraph At Samsung Electronics (Yoon & Kim) 2015 3. Top-k Set Similarity Joins on Flickr Hama is clearly faster than Apache Spark At IEEE 2015 (University of Melbourne) Why we do this? 1. How to parallelize the training of large models? 2. How to avoid overfitting due to large size of the network, even with large datasets? JonathanNet Distributed Training Parameter Server Parameter Server Parameter Swapping Task 5 Each group performs Task 2 Task 4 Task 3 .. -
Return of Organization Exempt from Income
OMB No. 1545-0047 Return of Organization Exempt From Income Tax Form 990 Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung benefit trust or private foundation) Open to Public Department of the Treasury Internal Revenue Service The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2011 calendar year, or tax year beginning 5/1/2011 , and ending 4/30/2012 B Check if applicable: C Name of organization The Apache Software Foundation D Employer identification number Address change Doing Business As 47-0825376 Name change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial return 1901 Munsey Drive (909) 374-9776 Terminated City or town, state or country, and ZIP + 4 Amended return Forest Hill MD 21050-2747 G Gross receipts $ 554,439 Application pending F Name and address of principal officer: H(a) Is this a group return for affiliates? Yes X No Jim Jagielski 1901 Munsey Drive, Forest Hill, MD 21050-2747 H(b) Are all affiliates included? Yes No I Tax-exempt status: X 501(c)(3) 501(c) ( ) (insert no.) 4947(a)(1) or 527 If "No," attach a list. (see instructions) J Website: http://www.apache.org/ H(c) Group exemption number K Form of organization: X Corporation Trust Association Other L Year of formation: 1999 M State of legal domicile: MD Part I Summary 1 Briefly describe the organization's mission or most significant activities: to provide open source software to the public that we sponsor free of charge 2 Check this box if the organization discontinued its operations or disposed of more than 25% of its net assets. -
Projects – Other Than Hadoop! Created By:-Samarjit Mahapatra [email protected]
Projects – other than Hadoop! Created By:-Samarjit Mahapatra [email protected] Mostly compatible with Hadoop/HDFS Apache Drill - provides low latency ad-hoc queries to many different data sources, including nested data. Inspired by Google's Dremel, Drill is designed to scale to 10,000 servers and query petabytes of data in seconds. Apache Hama - is a pure BSP (Bulk Synchronous Parallel) computing framework on top of HDFS for massive scientific computations such as matrix, graph and network algorithms. Akka - a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event-driven applications on the JVM. ML-Hadoop - Hadoop implementation of Machine learning algorithms Shark - is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones. Apache Crunch - Java library provides a framework for writing, testing, and running MapReduce pipelines. Its goal is to make pipelines that are composed of many user-defined functions simple to write, easy to test, and efficient to run Azkaban - batch workflow job scheduler created at LinkedIn to run their Hadoop Jobs Apache Mesos - is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, MPI, Hypertable, Spark, and other applications on a dynamically shared pool of nodes.