Qt for Device Creation
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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. -
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 -
OM-Cube Project
OM-Cube project V. Hiribarren, N. Marchand, N. Talfer [email protected] - [email protected] - [email protected] Abstract. The OM-Cube project is composed of several components like a minimal operating system, a multi- media player, a LCD display and an infra-red controller. They should be chosen to fit the hardware of an em- bedded system. Several other similar projects can provide information on the software that can be chosen. This paper aims to examine the different available tools to build the OM-Multimedia machine. The main purpose is to explore different ways to build an embedded system that fits the hardware and fulfills the project. 1 A Minimal Operating System The operating system is the core of the embedded system, and therefore should be chosen with care. Because of its popu- larity, a Linux based system seems the best choice, but other open systems exist and should be considered. After having elected a system, all unnecessary components may be removed to get a minimal operating system. 1.1 A Linux Operating System Using a Linux kernel has several advantages. As it’s a popular kernel, many drivers and documentation are available. Linux is an open source kernel; therefore it enables anyone to modify its sources and to recompile it. Using Linux in an embedded system requires adapting the kernel to the hardware and to the system needs. A simple method for building a Linux embed- ded system is to create a partition on a development host and to mount it on a temporary mount point. This partition is filled as one goes along and then, the final distribution is put on the target host [Fich02] [LFS]. -
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'> -
Institutionen För Datavetenskap Department of Computer and Information Science
Institutionen för datavetenskap Department of Computer and Information Science Final thesis Simulation of Set-top box Components on an X86 Architecture by Implementing a Hardware Abstraction Layer by Faruk Emre Sahin Muhammad Salman Khan LITH-IDA-EX—10/050--SE 2010-12-25 Linköpings universitet Linköpings universitet SE-581 83 Linköping, Sweden 581 83 Linköping Linköping University Department of Computer and Information Science Final Thesis Simulation of Set-top box Components on an X86 Architecture by Implementing a Hardware Abstraction Layer by Faruk Emre Sahin Muhammad Salman Khan LITH-IDA-EX—10/050—SE 2010-12-25 Supervisors: Fredrik Hallenberg, Tomas Taleus R&D at Motorola (Linköping) Examiner: Prof. Dr. Christoph Kessler Dept. Of Computer and Information Science at Linköpings universitet Abstract The KreaTV Application Development Kit (ADK) product of Motorola en- ables application developers to create high level applications and browser plugins for the IPSTB system. As a result, customers will reduce develop- ment time, cost and supplier dependency. The main goal of this thesis was to port this platform to a standard Linux PC to make it easy to trace the bugs and debug the code. This work has been done by implementing a hardware abstraction layer(HAL)for Linux Operating System. HAL encapsulates the hardware dependent code and HAL APIs provide an abstraction of underlying architecture to the oper- ating system and to application software. So, the embedded platform can be emulated on a standard Linux PC by implementing a HAL for it. We have successfully built the basic building blocks of HAL with some performance degradation. -
Openbricks Embedded Linux Framework - User Manual I
OpenBricks Embedded Linux Framework - User Manual i OpenBricks Embedded Linux Framework - User Manual OpenBricks Embedded Linux Framework - User Manual ii Contents 1 OpenBricks Introduction 1 1.1 What is it ?......................................................1 1.2 Who is it for ?.....................................................1 1.3 Which hardware is supported ?............................................1 1.4 What does the software offer ?............................................1 1.5 Who’s using it ?....................................................1 2 List of supported features 2 2.1 Key Features.....................................................2 2.2 Applicative Toolkits..................................................2 2.3 Graphic Extensions..................................................2 2.4 Video Extensions...................................................3 2.5 Audio Extensions...................................................3 2.6 Media Players.....................................................3 2.7 Key Audio/Video Profiles...............................................3 2.8 Networking Features.................................................3 2.9 Supported Filesystems................................................4 2.10 Toolchain Features..................................................4 3 OpenBricks Supported Platforms 5 3.1 Supported Hardware Architectures..........................................5 3.2 Available Platforms..................................................5 3.3 Certified Platforms..................................................7 -
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 -
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. -
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. -
Audio Video Graphics Working Group Session
Audio Video Graphics Working Group Session San Jose meeting Pieter van de Meulen WG chair 26 January, 2005 CE Linux Forum Technical Conference 1 AVG WG – todays objective: Audio Video 2D 3D Renesas, Mitsubishi, Conexant DirectFB Philips, Samsung UH API P hilips Multi-FB …. OpenGL …. …. …. 26 January, 2005 CE Linux Forum Technical Conference 2 Recall: 25th/26th Presentations • DirectFB - Dennis Oliver Kropp; Convergence ● http://www.directfb.org/ • OpenGL ES, OpenVG and OpenMAX - Ed Plowman; ARM ● http://www.khronos.org/ • Graphics APIS for Linux - Matsubara, Hagiwara, Hisao Munakata; Renesas • Creating GTK+ based UI's for embedded devices - Markku Ursin, Movial ● http://www.gtk.org/ • Linux DVB - Michael Hunold; Convergence ● http://www.linuxtv.org/ • UHAPI (AV streaming) tutorial - John Vugts; Philips/Samsung ● http://www.uhapi.org/ 26 January, 2005 CE Linux Forum Technical Conference 3 Recall: Jan. 25th Demos • FB (Multi-framebuffer) ● Philips: PNX8550 running Linux; dual framebuffer and AV accel. • DirectFB ● Conexant: DVD processor running CELF Linux with DirectFB. ● Mitsubishi: ARIB plane model emulated on DirectFB window system ● Mitsubishi: MPEG4 Player (via GTK+) running on Renesas SH-4 ● Renesas: GTK+ and GUI without X11 • UHAPI (AV streaming): ● Philips open source demo on PC • DTV ● Toshiba America/Europe: DTV reference solution and Home Gateway. • 3D graphics ●Renesas: demo by SI-Electronics on SH-4 and Power VR ●Pioneer: OpenGL 26 January, 2005 CE Linux Forum Technical Conference 4 Linux APIs (2004/6 status) & CCEELLFF VV11..00 -
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. -
Programmable Image-Based Light Capture for Previsualization
ii Abstract Previsualization is a class of techniques for creating approximate previews of a movie sequence in order to visualize a scene prior to shooting it on the set. Often these techniques are used to convey the artistic direction of the story in terms of cinematic elements, such as camera movement, angle, lighting, dialogue, and char- acter motion. Essentially, a movie director uses previsualization (previs) to convey movie visuals as he sees them in his ”minds-eye”. Traditional methods for previs include hand-drawn sketches, Storyboards, scaled models, and photographs, which are created by artists to convey how a scene or character might look or move. A recent trend has been to use 3D graphics applications such as video game engines to perform previs, which is called 3D previs. This type of previs is generally used prior to shooting a scene in order to choreograph camera or character movements. To visualize a scene while being recorded on-set, directors and cinematographers use a technique called On-set previs, which provides a real-time view with little to no processing. Other types of previs, such as Technical previs, emphasize accurately capturing scene properties but lack any interactive manipulation and are usually employed by visual effects crews and not for cinematographers or directors. This dissertation’s focus is on creating a new method for interactive visualization that will automatically capture the on-set lighting and provide interactive manipulation of cinematic elements to facilitate the movie maker’s artistic expression, validate cine- matic choices, and provide guidance to production crews. Our method will overcome the drawbacks of the all previous previs methods by combining photorealistic ren- dering with accurately captured scene details, which is interactively displayed on a mobile capture and rendering platform.