DOCSLIB.ORG

KNIME Big Data Workshop

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
KNIME Big Data Workshop KNIME Big Data Workshop Björn Lohrmann KNIME © 2018 KNIME.com AG. All Rights Reserved. Variety, Velocity, Volume • Variety: – Integrating heterogeneous data… – ... and tools • Velocity: – Real time scoring of millions of records/sec – Continuous data streams – Distributed computation • Volume: – From small files... – ...to distributed data repositories – Moving computation to the data © 2018 KNIME.com AG. All Rights Reserved. 2 2 Variety © 2018 KNIME.com AG. All Rights Reserved. 3 The KNIME Analytics Platform: Open for Every Data, Tool, and User Data Scientist Business Analyst KNIME Analytics Platform External Native External Data Data Access, Analysis, and Legacy Connectors Visualization, and Reporting Tools Distributed / Cloud Execution © 2018 KNIME.com AG. All Rights Reserved. 4 Data Integration © 2018 KNIME.com AG. All Rights Reserved. 5 Integrating R and Python © 2018 KNIME.com AG. All Rights Reserved. 6 Modular Integrations © 2018 KNIME.com AG. All Rights Reserved. 7 Other Programming/Scripting Integrations © 2018 KNIME.com AG. All Rights Reserved. 8 Velocity © 2018 KNIME.com AG. All Rights Reserved. 9 Velocity • High Demand Scoring/Prediction: – Scoring using KNIME Server – Scoring with the KNIME Managed Scoring Service • Continuous Data Streams – Streaming in KNIME © 2018 KNIME.com AG. All Rights Reserved. 10 What is High Demand Scoring? • I have a workflow that takes data, applies an algorithm/model and returns a prediction. • I need to deploy that to hundreds or thousands of end users • I need to update the model/workflow periodically © 2018 KNIME.com AG. All Rights Reserved. 11 How to make a scoring workflow • Use JSON Input/Output nodes • Put workflow on KNIME Server -> REST endpoint for scoring © 2018 KNIME.com AG. All Rights Reserved. 12 KNIME Managed Scoring Service • The KNIME Managed Scoring Service is a hosted service that allows provisioning and consuming of scoring workflows via REST. • No need to think about servers, hosting, building services, etc… © 2018 KNIME.com AG. All Rights Reserved. 13 KNIME Managed Scoring Service KNIME handles this… KNIME Scoring Agent Scaling Metric KNIME Scoring Agent Application Client Client Load KNIME applicationClient applicationClient Balancer Scoring application application Agent Scale up/down With demand KNIME Scoring Agent © 2018 KNIME.com AG. All Rights Reserved. 14 Velocity • High Demand Scoring/Prediction: – High Performance Scoring using generic Workflows – High Performance Scoring of Predictive Models • Continuous Data Streams – Streaming in KNIME © 2018 KNIME.com AG. All Rights Reserved. 15 Streaming in KNIME © 2018 KNIME.com AG. All Rights Reserved. 16 Volume © 2018 KNIME.com AG. All Rights Reserved. 17 Moving computation to the data © 2018 KNIME.com AG. All Rights Reserved. 18 Volume • Database Extension • Introduction to Hadoop • KNIME Big Data Connectors • KNIME Extension for Apache Spark • KNIME H2O Sparkling Water Integration • KNIME Workflow Executor for Apache Spark © 2018 KNIME.com AG. All Rights Reserved. 19 Database Extension • Visually assemble complex SQL statements • Connect to almost all JDBC-compliant databases • Harness the power of your database within KNIME © 2018 KNIME.com AG. All Rights Reserved. 20 In-Database Processing • Operations are performed within the database © 2018 KNIME.com AG. All Rights Reserved. 21 Tip • SQL statements are logged in KNIME log file © 2018 KNIME.com AG. All Rights Reserved. 22 Database Port Types Database Connection Port (dark red) • Connection information • SQL statement Database Connection Ports can be connected to Database JDBC Connection Port (light red) Database JDBC Connection Ports • Connection information but not vice versa © 2018 KNIME.com AG. All Rights Reserved. 23 Database Connectors • Nodes to connect to specific Databases – Bundling necessary JDBC drivers – Easy to use – DB specific behavior/capability • Hive and Impala connector • General Database Connector – Can connect to any JDBC source – Register new JDBC driver via preferences page © 2018 KNIME.com AG. All Rights Reserved. 24 Register JDBC Driver Register single jar file JDBC drivers Register new JDBC driver with companion files Open KNIME and go to Increase connection timeout for File -> Preferences long running database operations © 2018 KNIME.com AG. All Rights Reserved. 25 Query Nodes • Filter rows and columns • Join tables/queries • Extract samples • Bin numeric columns • Sort your data • Write your own query • Aggregate your data © 2018 KNIME.com AG. All Rights Reserved. 26 Database GroupBy – Manual Aggregation Returns number of rows per group © 2018 KNIME.com AG. All Rights Reserved. 27 Database GroupBy – DB Specific Aggregation Methods SQLite 7 aggregation functions PostgreSQL 25 aggregation functions © 2018 KNIME.com AG. All Rights Reserved. 28 Volume • Database Extension • Introduction to Hadoop • KNIME Big Data Connectors • KNIME Extension for Apache Spark • KNIME H2O Sparkling Water Integration • KNIME Workflow Executor for Apache Spark © 2018 KNIME.com AG. All Rights Reserved. 30 Apache Hadoop • Open-source framework for distributed storage and processing of large data sets • Designed to scale up to thousands of machines • Does not rely on hardware to provide high availability – Handles failures at application layer instead • First release in 2006 – Rapid adoption, promoted to top level Apache project in 2008 – Inspired by Google File System (2003) paper • Spawned diverse ecosystem of products © 2018 KNIME.com AG. All Rights Reserved. 31 Hadoop Ecosystem Access HIVE Processing MapReduce Tez Spark Resource Management YARN Storage HDFS © 2018 KNIME.com AG. All Rights Reserved. 32 HDFS HIVE MapReduce Tez Spark • Hadoop distributed file system YARN • Stores large files across multiple machines HDFS File File (large!) Blocks (default: 64MB) DataNodes © 2018 KNIME.com AG. All Rights Reserved. 33 HDFS – Data Replication and File Size Data Replication • All blocks of a file are File 1 B B B stored as sequence of 1 2 3 blocks • Blocks of a file are B B NameNode n1 n1 n1 replicated for fault 1 2 B n2 B n2 B n2 tolerance (usually 3 1 1 2 replicas) B n3 B n3 B n3 – Aims: improve data 3 2 3 reliability, availability, and B n4 n4 n4 network bandwidth 3 utilization rack 1 rack 2 rack 3 © 2018 KNIME.com AG. All Rights Reserved. 36 YARN • Cluster resource management system • Two elements – Resource manager (one per cluster): • Knows where worker nodes are located and how many resources they have • Scheduler: Decides how to allocate resources to applications – Node manager (many per cluster): • Launches application containers • Monitor resource usage and report to Resource Manager HIVE MapReduce Tez Spark YARN HDFS © 2018 KNIME.com AG. All Rights Reserved. 38 MapReduce Input Splitting Mapping Shuffling Reducing Result blue, 1 blue, 1 blue red orange red, 1 blue, 1 blue, 3 orange, 1 blue, 1 blue, 3 blue red orange yellow, 1 yellow, 1 yellow blue yellow blue yellow, 1 yellow, 1 blue, 1 orange, 1 blue red,1 blue blue, 1 orange, 1 orange, 1 Map applies a function to each element red, 1 red, 1 For each word emit: word, 1 Reduce aggregates a list of values to one result HIVE For all equal words sum up count MapReduce Tez Spark YARN HDFS © 2018 KNIME.com AG. All Rights Reserved. 41 Hive • SQL-like database on top of files in HDFS • Provides data summarization, query, and analysis • Interprets a set of files as a database table (schema information to be provided) • Translates SQL queries to MapReduce, Tez, or Spark jobs • Supports various file formats: – Text/CSV – SequenceFile HIVE – Avro MapReduce Tez Spark – ORC YARN HDFS – Parquet © 2018 KNIME.com AG. All Rights Reserved. 46 Hive SQL select * from table MAP(...) MapReduce / Tez / Spark REDUCE(...) Var1 Var2 Var3 Y DataNode DataNode DataNode x1 z1 n1 y1 table_1.csv table_2.csv table_3.csv x2 z2 n2 y2 x3 z3 n3 y3 © 2018 KNIME.com AG. All Rights Reserved. 47 Spark • Cluster computing framework for large-scale data processing • Keeps large working datasets in memory between jobs – No need to always load data from disk -> much (!) faster than MapReduce • Programmatic interface – Scala, Java, Python, R – Functional programming paradigm: map, flatmap, filter, reduce, fold, … • Great for: HIVE – Iterative algorithms MapReduce Tez Spark YARN – Interactive analysis HDFS © 2018 KNIME.com AG. All Rights Reserved. 48 Spark – Data Representation DataFrame: Name Surname Age John Doe 35 – Table-like: Collection of rows, organized in columns Jane Roe 29 with names and types … … … – Immutable: • Data manipulation = creating new DataFrame from an existing one by applying a function on it Note: • Earlier versions of KNIME and – Lazily evaluated: Spark used RDDs (resilient • Functions are not executed until an action is triggered, distributed datasets) that requests to actually see the row data • In KNIME, DataFrames are always used in Spark 2 and later. – Distributed: • Each row belongs to exactly one partition • Each partition is held by a Spark Executor © 2018 KNIME.com AG. All Rights Reserved. 49 Spark – Lazy Evaluation • Functions ("transformations") on DataFrames are not executed immediately • Spark keeps record of the transformations for each DataFrame • The actual execution is only triggered once the data is needed • Offers the possibility to optimize the transformation steps Triggers evaluation © 2018 KNIME.com AG. All Rights Reserved. 51 Spark Context • Spark Context – Main entry point for Spark functionality – Represents connection to a Spark cluster – Allocates resources on the cluster Spark Executor Task
Load more

Recommended publications
  • Text Mining Course for KNIME Analytics Platform
    Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt
    [Show full text]
  • Sage for Lattice-Based Cryptography
    Sage for Lattice-based Cryptography Martin R. Albrecht and Léo Ducas Oxford Lattice School Outline Sage Lattices Rings Sage Blurb Sage open-source mathematical software system “Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab.” Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. How to use it command line run sage local webapp run sage -notebook=jupyter hosted webapp https://cloud.sagemath.com 1 widget http://sagecell.sagemath.org 1On SMC you have the choice between “Sage Worksheet” and “Jupyter Notebook”. We recommend the latter. Python & Cython Sage does not come with yet-another ad-hoc mathematical programming language, it uses Python instead. • one of the most widely used programming languages (Google, IML, NASA, Dropbox), • easy for you to define your own data types and methods on it (bitstreams, lattices, cyclotomic rings, :::), • very clean language that results in easy to read code, • a huge number of libraries: statistics, networking, databases, bioinformatic, physics, video games, 3d graphics, numerical computation (SciPy), and pure mathematic • easy to use existing C/C++ libraries from Python (via Cython) Sage =6 Python Sage Python 1/2 1/2 1/2 0 2^3 2^3 8 1 type(2) type(2) <type 'sage.rings.integer.Integer'> <type 'int'> Sage =6 Python Sage Python type(2r) type(2r) <type 'int'> SyntaxError: invalid syntax type(range(10)[0]) type(range(10)[0]) <type 'int'> <type 'int'>
    [Show full text]
  • User Manual QT
    multichannel systems* User Manual QT Information in this document is subject to change without notice. No part of this document may be reproduced or transmitted without the express written permission of Multi Channel Systems MCS GmbH. While every precaution has been taken in the preparation of this document, the publisher and the author assume no responsibility for errors or omissions, or for damages resulting from the use of information contained in this document or from the use of programs and source code that may accompany it. In no event shall the publisher and the author be liable for any loss of profit or any other commercial damage caused or alleged to have been caused directly or indirectly by this document. © 2006-2007 Multi Channel Systems MCS GmbH. All rights reserved. Printed: 2007-01-25 Multi Channel Systems MCS GmbH Aspenhaustraße 21 72770 Reutlingen Germany Fon +49-71 21-90 92 5 - 0 Fax +49-71 21-90 92 5 -11 [email protected] www.multichannelsystems.com Microsoft and Windows are registered trademarks of Microsoft Corporation. Products that are referred to in this document may be either trademarks and/or registered trademarks of their respective holders and should be noted as such. The publisher and the author make no claim to these trademarks. Table Of Contents 1 Introduction 1 1.1 About this Manual 1 2 Important Information and Instructions 3 2.1 Operator's Obligations 3 2.2 Guaranty and Liability 3 2.3 Important Safety Advice 4 2.4 Terms of Use for the program 5 2.5 Limitation of Liability 5 3 First Use 7 3.1
    [Show full text]
  • A Comparison of Open Source Tools for Data Science
    2015 Proceedings of the Conference on Information Systems Applied Research ISSN: 2167-1508 Wilmington, North Carolina USA v8 n3651 __________________________________________________________________________________________________________________________ A Comparison of Open Source Tools for Data Science Hayden Wimmer Department of Information Technology Georgia Southern University Statesboro, GA 30260, USA Loreen M. Powell Department of Information and Technology Management Bloomsburg University Bloomsburg, PA 17815, USA Abstract The next decade of competitive advantage revolves around the ability to make predictions and discover patterns in data. Data science is at the center of this revolution. Data science has been termed the sexiest job of the 21st century. Data science combines data mining, machine learning, and statistical methodologies to extract knowledge and leverage predictions from data. Given the need for data science in organizations, many small or medium organizations are not adequately funded to acquire expensive data science tools. Open source tools may provide the solution to this issue. While studies comparing open source tools for data mining or business intelligence exist, an update on the current state of the art is necessary. This work explores and compares common open source data science tools. Implications include an overview of the state of the art and knowledge for practitioners and academics to select an open source data science tool that suits the requirements of specific data science projects. Keywords: Data Science Tools, Open Source, Business Intelligence, Predictive Analytics, Data Mining. 1. INTRODUCTION Data science is an expensive endeavor. One such example is JMP by SAS. SAS is a primary Data science is an emerging field which provider of data science tools. JMP is one of the intersects data mining, machine learning, more modestly priced tools from SAS with the predictive analytics, statistics, and business price for JMP Pro listed as $14,900.
    [Show full text]
  • A Study of Open Source Data Mining Tools and Its Applications
    Research Journal of Applied Sciences, Engineering and Technology 10(10): 1108-1132, 2015 ISSN: 2040-7459; e-ISSN: 2040-7467 © Maxwell Scientific Organization, 2015 Submitted: January 31, 2015 Accepted: March 12, 2015 Published: August 05, 2015 A Study of Open Source Data Mining Tools and its Applications P. Subathra, R. Deepika, K. Yamini, P. Arunprasad and Shriram K. Vasudevan Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, India Abstract: Data Mining is a technology that is used for the process of analyzing and summarizing useful information from different perspectives of data. The importance of choosing data mining software tools for the developing applications using mining algorithms has led to the analysis of the commercially available open source data mining tools. The study discusses about the following likeness of the data mining tools-KNIME, WEKA, ORANGE, R Tool and Rapid Miner. The Historical Development and state-of-art; (i) The Applications supported, (ii) Data mining algorithms are supported by each tool, (iii) The pre-requisites and the procedures to install a tool, (iv) The input file format supported by each tool. To extract useful information from these data effectively and efficiently, data mining tools are used. The availability of many open source data mining tools, there is an increasing challenge in deciding upon the apace-updated tools for a given application. This study has provided a brief study about the open source knowledge discovery tools with their installation process, algorithms support and input file formats support. Keywords: Data mining, KNIME, ORANGE, Rapid Miner, R-tool, WEKA INTRODUCTION correctness in the classification of the data along with its accuracy.
    [Show full text]
  • Introduction to Data Mining May 2018
    Zupan, Demsar: Introduction to Data Mining May 2018 Introduction to Data Mining Working notes for the hands-on course with Orange Data Mining These notes include Orange Welcome to the course on Introduction to Data Mining! You will workflows and visualizations we see how common data mining tasks can be accomplished without will construct during the course. programming. We will use Orange to construct visual data mining The working notes were workflows. Many similar data mining environments exist, but the prepared by Bla" Zupan and authors of these notes prefer Orange for one simple reason—they Janez Dem#ar with help from the are its authors. For the courses offered by Orange developers, members of the Bioinformatics please visit https://orange.biolab.si/training. Lab in Ljubljana that develop If you haven’t already installed Orange, please download the and maintain Orange. installation package from http://orange.biolab.si. Attribution-NonCommercial-NoDerivs CC BY-NC-ND # University of Ljubljana !1 Zupan, Demsar: Introduction to Data Mining May 2018 Lesson 1: Workflows in Orange Orange workflows consist of components that read, process and visualize data. We call them “widgets.” We place the widgets on a drawing board (the “canvas”). Widgets communicate by sending information along with a communication channel. An output from one widget is used as input to another. A screenshot above shows a We construct workflows by dragging widgets onto the canvas and simple workflow with two connecting them by drawing a line from the transmitting widget to connected widgets and one the receiving widget. The widget’s outputs are on the right and the widget without connections.
    [Show full text]
  • Automatic Classification of Oranges Using Image Processing and Data Mining Techniques
    Automatic classification of oranges using image processing and data mining techniques Juan Pablo Mercol, Juliana Gambini, Juan Miguel Santos Departamento de Computaci´on, Facultad de Ciencias Exactas y Naturales, UBA, Buenos Aires, Argentina [email protected],{jgambini,jmsantos}@dc.uba.ar, Abstract Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetables production lines, the quality assurance is done by trained people who inspect the fruits while they move in a conveyor belt, and classify them in several categories based on visual features. In this paper we present an automatic orange’s classification system, which uses visual inspection to extract features from images captured with a digital camera. With these features train several data mining algorithms which should classify the fruits in one of the three pre-established categories. The data mining algorithms used are five different decision trees (J48, Classification and Regression Tree (CART), Best First Tree, Logistic Model Tree (LMT) and Random For- est), three artificial neural networks (Multilayer Perceptron with Backpropagation, Radial Basis Function Network (RBF Network), Sequential Minimal Optimization for Support Vector Machine (SMO)) and a classification rule (1Rule). The obtained results are encouraging because of the good accuracy achieved by the clas- sifiers and the low computational costs. Keywords: Image processing, Data Mining, Neural Networks, Decision trees, Fruit Qual- ity Assurance, Visual inspection, Artificial Vision. 1 Introduction During the last years, there has been an increase in the need to measure the quality of several products, in order to satisfy customers needs in the industry and services levels.
    [Show full text]
  • C++ GUI Programming with Qt 4, Second Edition by Jasmin Blanchette; Mark Summerfield
    C++ GUI Programming with Qt 4, Second Edition by Jasmin Blanchette; Mark Summerfield Publisher: Prentice Hall Pub Date: February 04, 2008 Print ISBN-10: 0-13-235416-0 Print ISBN-13: 978-0-13-235416-5 eText ISBN-10: 0-13-714397-4 eText ISBN-13: 978-0-13-714397-9 Pages: 752 Table of Contents | Index Overview The Only Official, Best-Practice Guide to Qt 4.3 Programming Using Trolltech's Qt you can build industrial-strength C++ applications that run natively on Windows, Linux/Unix, Mac OS X, and embedded Linux without source code changes. Now, two Trolltech insiders have written a start-to-finish guide to getting outstanding results with the latest version of Qt: Qt 4.3. Packed with realistic examples and in-depth advice, this is the book Trolltech uses to teach Qt to its own new hires. Extensively revised and expanded, it reveals today's best Qt programming patterns for everything from implementing model/view architecture to using Qt 4.3's improved graphics support. You'll find proven solutions for virtually every GUI development task, as well as sophisticated techniques for providing database access, integrating XML, using subclassing, composition, and more. Whether you're new to Qt or upgrading from an older version, this book can help you accomplish everything that Qt 4.3 makes possible. • Completely updated throughout, with significant new coverage of databases, XML, and Qtopia embedded programming • Covers all Qt 4.2/4.3 changes, including Windows Vista support, native CSS support for widget styling, and SVG file generation • Contains
    [Show full text]
  • Data Mining and Knowledge Discovery Petra Kralj Novak November 18, 2019
    INFORMATION AND COMMUNICATION TECHNOLOGIES PhD study programme Data Mining and Knowledge Discovery Petra Kralj Novak November 18, 2019 http://kt.ijs.si/petra_kralj/dmkd3.html 1 Data Mining and Knowledge Discovery Course scope: Prof. dr. Nada Lavrač Introduction, rule learning, relational DM, semantic DM, embeddings Doc. dr. Martin Žnidaršič Ensemble methods, active learning, SVM & neural networks, correlation, causality and false patterns Doc. dr. Petra Kralj Novak Advanced evaluation, regression, advanced clustering Hands-on: Orange, Scikit, Keras Course requirements: Written exam Computational & theoretical tasks Seminar Analyzing your own data 2 Keywords • Data • Attribute (feature), example (instance), attribute-value data, target variable, class, discretization, market basket data • Algorithms • Decision tree induction, entropy, information gain, overfitting, Occam’s razor, decision tree pruning, naïve Bayes classifier, KNN, association rules, classification rules, Laplace estimate, regression tree, model tree, hierarchical clustering, dendrogram, k-means clustering, centroid, Apriori, heuristics vs. exhaustive search, predictive vs. descriptive DM, language bias, artificial neural networks, deep learning, backpropagation,… • Evaluation • Train set, test set, accuracy, confusion matrix, cross validation, true positives, false positives, ROC space, AUC, error, precision, recall, F1, MSE, RMSE, support, confidence 3 Bramer, Max. (2007). Principles of Data Mining. 10.1007/978-1-84628-766-4. 1. Data for Data Mining 2. Introduction to Classification: Naïve Bayes and Nearest Neighbour 3. Using Decision Trees for Classification 4. Decision Tree Induction: Using Entropy for 5. Decision Tree Induction: Using Frequency 7. Continuous Attributes 8. Avoiding Overfitting of Decision Trees 9. More About Entropy 10. Inducing Modular Rules for Classification 11. Measuring the Performance of a Classifier 12. Association Rule Mining I 13.
    [Show full text]
  • Analysis of Heart Disease Using in Data Mining Tools Orange and Weka by Sarangam Kodati & Dr
    Global Journal of Computer Science and Technology: C Software & Data Engineering Volume 18 Issue 1 Version 1.0 Year 2018 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Analysis of Heart Disease using in Data Mining Tools Orange and Weka By Sarangam Kodati & Dr. R. Vivekanandam Sri Satya Sai University Abstract- Health care is an inevitable task to be done in human life. Health concern business has become a notable field in the wide spread area of medical science. Health care industry contains large amount of data and hidden information. Effective decisions are made with this hidden information by applying patient; however, with data mining these tests could be reduced. But there is a lack of analyzing tool according to provide effective test outcomes together with the hidden information, so and such system is developed using data mining algorithms for classifying the data and to detect the heart diseases. Data mining acts so a solution by many healthcare problems. Naïve Bayes, SVM, Random Forest, KNN algorithm is one such data mining method which serves with the diagnosis regarding heart diseases patient. This paper analyzes few parameters and predicts heart diseases, thereby suggests a heart diseases prediction system (HDPS) based total on the data mining approaches. Keywords: data mining, weka, orange, heart disease, data mining classification techniques. GJCST-C Classification: J.3 AnalysisofHeartDiseaseusinginDataMiningToolsOrangeandWeka Strictly as per the compliance and regulations of: © 2018. Sarangam Kodati & Dr. R. Vivekanandam. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non- commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.
    [Show full text]
  • AN QP™/QM™ and the Qt™ GUI Framework
    QP state machine frameworks for Arduino Application Note QP/C++™ and the Qt™ GUI Framework Document Revision K May 2014 Copyright © Quantum Leaps, LLC [email protected] www.state-machine.com Table of Contents 1 Introduction ..................................................................................................................................................... 1 1.1 About Qt .................................................................................................................................................. 1 1.2 About QP/C++™ ...................................................................................................................................... 1 1.3 About QM™ ............................................................................................................................................. 2 1.4 Licensing QP™ ........................................................................................................................................ 3 1.5 Licensing QM™ ....................................................................................................................................... 3 2 Getting Started ................................................................................................................................................ 4 2.1 Installing Qt .............................................................................................................................................. 4 2.2 Installing QP/C++ Baseline Code ...........................................................................................................
    [Show full text]
  • Legate Numpy: Accelerated and Distributed Array Computing
    Legate NumPy: Accelerated and Distributed Array Computing Michael Bauer Michael Garland NVIDIA NVIDIA [email protected] [email protected] ABSTRACT providing a simple and easy to use programming model whose inter- NumPy is a popular Python library used for performing array- face corresponds closely to the mathematical needs of applications. based numerical computations. The canonical implementation of NumPy array objects also act as a common data storage format that NumPy used by most programmers runs on a single CPU core and can be used to share data between other Python libraries, such as only a few operations are parallelized across cores. This restriction Pandas [13], SciPy [9], or h5py [4]. Thus, NumPy has become the to single-node CPU-only execution limits both the size of data foundation upon which many of the most widely used data science that can be processed and the speed with which problems can be and machine learning programming environments are constructed. solved. In this paper we introduce Legate, a programming system While its interface is powerful, NumPy’s implementation is cur- that transparently accelerates and distributes NumPy programs rently limited to a single-node, occasionally multi-threaded, CPU- to machines of any scale and capability typically by changing a only execution model. As datasets continue to grow in size and single module import statement. Legate achieves this by translating programs continue to increase in complexity, there is an ever in- the NumPy application interface into the Legion programming creasing need to solve these problems by harnessing computational model and leveraging the performance and scalability of the Legion resources far beyond what a single CPU-only node can provide.
    [Show full text]