Joe Celko's Data, Measurements and Standards in SQL
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A4 Paper Format / International Standard Paper Sizes
A4 paper format / International standard paper sizes International standard paper sizes by Markus Kuhn Standard paper sizes like ISO A4 are widely used all over the world today. This text explains the ISO 216 paper size system and the ideas behind its design. The ISO paper size concept In the ISO paper size system, the height-to-width ratio of all pages is the square root of two (1.4142 : 1). In other words, the width and the height of a page relate to each other like the side and the diagonal of a square. This aspect ratio is especially convenient for a paper size. If you put two such pages next to each other, or equivalently cut one parallel to its shorter side into two equal pieces, then the resulting page will have again the same width/height ratio. The ISO paper sizes are based on the metric system. The square-root-of-two ratio does not permit both the height and width of the pages to be nicely rounded metric lengths. Therefore, the area of the pages has been defined to have round metric values. As paper is usually specified in g/m², this simplifies calculation of the mass of a document if the format and number of pages are known. ISO 216 defines the A series of paper sizes based on these simple principles: ● The height divided by the width of all formats is the square root of two (1.4142). ● Format A0 has an area of one square meter. ● Format A1 is A0 cut into two equal pieces. -
Introduction to Measurements & Error Analysis
Introduction to Measurements & Error Analysis The Uncertainty of Measurements Some numerical statements are exact: Mary has 3 brothers, and 2 + 2 = 4. However, all measurements have some degree of uncertainty that may come from a variety of sources. The process of evaluating this uncertainty associated with a measurement result is often called uncertainty analysis or error analysis. The complete statement of a measured value should include an estimate of the level of confidence associated with the value. Properly reporting an experimental result along with its uncertainty allows other people to make judgments about the quality of the experiment, and it facilitates meaningful comparisons with other similar values or a theoretical prediction. Without an uncertainty estimate, it is impossible to answer the basic scientific question: “Does my result agree with a theoretical prediction or results from other experiments?” This question is fundamental for deciding if a scientific hypothesis is confirmed or refuted. When we make a measurement, we generally assume that some exact or true value exists based on how we define what is being measured. While we may never know this true value exactly, we attempt to find this ideal quantity to the best of our ability with the time and resources available. As we make measurements by different methods, or even when making multiple measurements using the same method, we may obtain slightly different results. So how do we report our findings for our best estimate of this elusive true value? The most common way to show the range of values that we believe includes the true value is: measurement = best estimate ± uncertainty (units) Let’s take an example. -
Software Testing
Software Testing PURPOSE OF TESTING CONTENTS I. Software Testing Background II. Software Error Case Studies 1. Disney Lion King 2. Intel Pentium Floating Point Division Bug 3. NASA Mars Polar Lander 4. Patriot Missile Defense System 5. Y2K Bug III. What is Bug? 1. Terms for Software Failure 2. Software Bug: A Formal Definition 3. Why do Bugs occur? and cost of bug. 4. What exactly does a Software Tester do? 5. What makes a good Software Tester? IV. Software Development Process 1. Product Components 2. What Effort Goes into a Software Product? 3. What parts make up a Software Product? 4. Software Project Staff V. Software Development Lifecycle Models 1. Big Bang Model 2. Code and Fix Model 3. Waterfall Model 4. Spiral Model VI. The Realities of Software Testing VII. Software Testing Terms and Definition 1. Precision and Accuracy 2. Verification and Validation 3. Quality Assurance and Quality Control Anuradha Bhatia Software Testing I. Software Testing Background 1. Software is a set of instructions to perform some task. 2. Software is used in many applications of the real world. 3. Some of the examples are Application software, such as word processors, firmware in an embedded system, middleware, which controls and co-ordinates distributed systems, system software such as operating systems, video games, and websites. 4. All of these applications need to run without any error and provide a quality service to the user of the application. 5. The software has to be tested for its accurate and correct working. Software Testing: Testing can be defined in simple words as “Performing Verification and Validation of the Software Product” for its correctness and accuracy of working. -
Xerox® Travel Scanner 150 User’S Guide
One Touch 4.6 August 2012 05-0840-100 Xerox® Travel Scanner 150 User’s Guide Design © 2012 Xerox Corporation. All rights reserved. XEROX®, XEROX and Design® and DocuMate® are registered trademarks of Xerox Corporation in the United States and/or other countries. BR2702 Content © 2012 Visioneer, Inc. All rights reserved. The Visioneer brand name and OneTouch® logo are registered trademarks of Visioneer, Inc. Copyright protection claimed includes all forms of matters of copyrightable materials and information now allowed by statutory or judicial law or hereinafter granted, including without limitation, material generated from the software programs which are displayed on the screen such as styles, templates, icons, screen displays, looks, etc. Reproduction, adaptation, or translation without prior written permission is prohibited, except as allowed under the copyright laws. The PaperPort® and OmniPage® brand name and logo are registered trademarks of Nuance Communications, Inc. Adobe®, Adobe® Acrobat®, Adobe® Reader®, and the Adobe® PDF logo are registered trademarks of Adobe Systems Incorporated in the United States and/or other countries. The Adobe PDF logo will appear in this product’s software, and full access to Adobe software features is only available if an Adobe product is installed on your computer. Microsoft is a U.S. registered trademark of Microsoft Corporation. Windows™ is a trademark and SharePoint® is a registered trademark of Microsoft Corporation. ZyINDEX is a registered trademark of ZyLAB International, Inc. ZyINDEX toolkit portions, Copyright © 1990-1998, ZyLAB International, Inc. Document Version: 05-0840-100 (August 2012). All Rights Reserved. All other products mentioned herein may be trademarks of their respective companies and are hereby acknowledged. -
Breathe London-NPL-Statistical Methods for Characterisation Of
Statistical methods for performance characterisation of lower-cost sensors J D Hayward, A Forbes, N A Martin, S Bell, G Coppin, G Garcia and D Fryer 1.1 Introduction This appendix summarises the progress of employing the Breathe London monitoring data together with regulated reference measurements to investigate the application of machine learning tools to extract information with a view to quantifying measurement uncertainty. The Breathe London project generates substantial data from the instrumentation deployed, and the analysis requires a comparison against the established reference methods. The first task was to develop programs to extract the various datasets being employed from different providers and to store them in a stan- dardised format for further processing. This data curation allowed us to audit the data, establishing errors in the AQMesh data and the AirView cars as well as monitor any variances in the data sets caused by conditions such as fog/extreme temperatures. This includes writing a program to parse the raw AirView data files from the high quality refer- ence grade instruments installed in the mobile platforms (Google AirView cars), programs to commu- nicate with and download data from the Applications Program Interfaces (APIs) provided by both Air Monitors (AQMesh sensor systems) and Imperial College London (formerly King’s College London) (London Air Quality Network), and a program that automatically downloads CSV files from the DE- FRA website, which contains the air quality data from the Automatic Urban Rural Network (AURN). This part of the development has had to overcome significant challenges including the accommo- dation of different pollutant concentration units from the different database sources, different file formats, and the incorporation of data flags to describe the data ratification status. -
EXPERIMENTAL METHOD Linear Dimension, Weight and Density
UNIVERSITI TEKNIKAL No Dokumen: No. Isu/Tarikh: MALAYSIA MELAKA SB/MTU/T1/BMCU1022/1 4/06-07-2009 EXPERIMENTAL METHOD No. Semakan/Tarikh Jumlah Muka Surat Linear Dimension, Weight and Density 4/09-09-2009 4 OBJECTIVE Investigation of different types of equipment for linear dimension measurements and to study measurement accuracy and precision using commonly used measuring equipments. LEARNING OUTCOME At the end of this laboratory session, students should be able to 1. Identify various dimensional measuring equipments commonly used in a basic engineering laboratory. 2. Apply dimensional measuring equipments to measure various objects accurately. 3. Compare the measured data measured using different measuring equipments. 4. Determine the measured dimensional difference using different equipments correctly. 5. Answer basic questions related to the made measurement correctly. 6. Present the measured data in form of report writing according to the normal technical report standard and make clear conclusions of the undertaken tasks. THEORY Basic physical quantity is referred to a quantity which is measurable such as weight, length and time. These quantities could be combined and formed what one called the derived quantities. Some of the examples of derived quantities include parameter such as area, volume, density, velocity, force and many others. Measurement is a comparison process between standard quantities (true value) with measured quantity. Measurement apparatus complete with unit and dimension must be used in order to make a good and correct measurement. Accuracy means how close the measured value to the standard (true) value. Precision of measurement refers to repeatable values of the measurement and the results showed that its uncertainty is of the lowest degree. -
Long Term Archiving of Digital Data on Microfilm
Int. J. Electronic Governance, Vol. 3, No. 3, 2010 237 Long-term archiving of digital data on microfilm Steffen W. Schilke* Gemeinsame IT-Stelle der hessischen Justiz Friedrich-Ebert-Straße 28 D-61118 Bad Vilbel, Germany Postal Address: Oberlandesgericht Frankfurt am Main, D-60256 Frankfurt am Main, Germany E-mail: [email protected] *Corresponding author Andreas Rauber Department of Software Technology and Interactive Systems, Vienna University of Technology Favoritenstr., 9-11/188, A-1040 Vienna, Austria E-mail: [email protected] Abstract: E-government applications have to archive data or documents for long retention periods of 100 years or more. This requires to store digital data on stable media, and to ensure that the file formats can be read by available software. Both applications as well as media technology have only short life spans. Thus, data has to be migrated at frequent intervals onto new data carriers and to new file formats. However, original file versions usually need to be retained permanently. In terms of cost, stability and technology independence, microfilm storage offers a promising solution for off-line storage. This paper reports on a feasibility study analysing encoding techniques that allow digital data to be saved onto microfilm, testing data recovery as well as cost issues. Keywords: digital preservation; bit-stream preservation; long-term storage; microfilming. Reference to this paper should be made as follows: Schilke, S.W. and Rauber, A. (2010) ‘Long-term archiving of digital data on microfilm’, Int. J. Electronic Governance, Vol. 3, No. 3, pp.237–253. Biographical notes: Steffen Walter Schilke is working in the field of archiving and document management for more than a decade. -
Guidelines for the Design and Preparation of User Documentation for Application Software
ISO/IEC JTC 1/SC 7 Date: 2003-03-04 ISO/IEC FDIS 18019:2003(E) ISO/IEC JTC 1/SC 7/WG 2 Secretariat: Software and systems engineering — Guidelines for the design and preparation of user documentation for application software Élément introductif — Élément central — Élément complémentaire Document type: International Standard Document subtype: Document stage: (50) Approval Document language: E Macintosh HD2:Users:cinzia:Documents:AccessAbility SIG work:ISO-IEC_FDIS_18019_(E).doc STD Version 2.1 ISO/IEC FDIS 18019:2003(E) Copyright notice This ISO document is a Draft International Standard and is copyright-protected by ISO. Except as permitted under the applicable laws of the user's country, neither this ISO draft nor any extract from it may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, photocopying, recording or otherwise, without prior written permission being secured. Requests for permission to reproduce should be addressed to either ISO at the address below or ISO's member body in the country of the requester. ISO copyright office Case postale 56 • CH-1211 Geneva 20 Tel. + 41 22 749 01 11 Fax + 41 22 749 09 47 E-mail [email protected] Web www.iso.org Reproduction may be subject to royalty payments or a licensing agreement. Violators may be prosecuted. iii © ISO/IEC 2003 — All rights reserved ISO/IEC FDIS 18019:2003(E) Contents Page Foreword....................................................................................................................................................................ix -
Implementation and Performance Analysis of Precision Time Protocol on Linux Based System-On-Chip Platform
Implementation and Performance Analysis of Precision Time Protocol on Linux based System-On-Chip Platform Mudassar Ahmed [email protected] MASTERPROJECT Kiel University of Applied Science MSc. Information Engineering in Kiel im Mai 2018 Declaration I hereby declare and confirm that this project is entirely the result of my own original work. Where other sources of information have been used, they have been indicated as such and properly acknowledged. I further declare that this or similar work has not been submitted for credit elsewhere. Kiel, May 9, 2018 Mudassar Ahmed [email protected] i Contents Declaration i Abstract iv List of Symbols and Abbreviations vii 1 Introduction 1 1.1 Research Objectives and Goals . 2 1.2 Approach . 2 1.3 Outline . 2 2 Literature Review 3 2.1 Time Synchronization . 3 2.2 Time Synchronization Technologies . 4 2.3 Overview of IEEE 1588 Precision Time Protocol (PTP) . 4 2.3.1 Scope of PTP Standard: . 5 2.3.2 Protocol Standard Messages . 5 2.3.3 Protocol Standard Devices . 6 2.3.4 Message Exchange and Delay Computation . 7 2.3.5 Protocol Hierarchy Establishment Mechanism . 9 3 PTP Infrastructure in Linux 11 3.1 Timestamping Mechanisms . 11 3.1.1 Software Timestamping . 11 3.1.2 Hardware Timestamping . 12 3.1.3 Linux kernel Support for Timestamping . 13 3.2 PTP Clock Infrastructure and Control API . 14 4 Design and Implementation 16 4.1 Tools and technologies . 16 4.1.1 LinuxPTP . 16 4.1.2 PTPd . 18 4.1.3 stress-ng . 18 4.1.4 iPerf . -
Understanding Measurement Uncertainty in Certified Reference Materials 02 Accuracy and Precision White Paper Accuracy and Precision White Paper 03
→ White Paper Understanding measurement uncertainty in certified reference materials 02 Accuracy and Precision White Paper Accuracy and Precision White Paper 03 The importance of measurement uncertainty. This paper provides an overview of the importance of measurement uncertainty in the manufacturing and use of certified reference gases. It explains the difference between error and uncertainty, accuracy and precision. No measurement can be made with 100 percent accuracy. There is Every stage of the process, every measurement device used always a degree of uncertainty, but for any important measurement, introduces some uncertainty. Identifying and quantifying each source it’s essential to identify every source of uncertainty and to quantify the of uncertainty in order to present a test result with a statement uncertainty introduced by each source. quantifying the uncertainty associated with the result can be challenging. But no measurement is complete unless it is accompanied In every field of commerce and industry, buyers require certified by such a statement. measurements to give them assurances of the quality of goods supplied: the tensile strength of steel; the purity of a drug; the heating This white paper will provide an overview of sources of measurement power of natural gas, for example. uncertainty relating to the manufacture and use of certified reference gases. It will explain how uncertainty reported with a test result can Suppliers rely on test laboratories, either their own or third parties, be calculated and it will explain the important differences between to perform the analysis or measurement necessary to provide these uncertainty, accuracy and precision. assurances. Those laboratories in turn rely on the accuracy and precision of their test instruments, the composition of reference materials, and the rigour of their procedures, to deliver reliable and accurate measurements. -
ECE3340 Introduction to the Limit of Computer Numerical Capability – Precision and Accuracy Concepts PROF
ECE3340 Introduction to the limit of computer numerical capability – precision and accuracy concepts PROF. HAN Q. LE Computing errors …~ 99 – 99.9% of computing numerical errors are likely due to user’s coding errors (syntax, algorithm)… and the tiny remaining portion may be due to user’s lack of understanding how numerical computation works… Hope this course will help you on this problems Homework/Classwork 1 CHECK OUT THE PERFORMANCE OF YOUR COMPUTER CW Click open this APP Click to ask your machine the smallest number it can handle (this example shows 64-bit x86 architecture) Log base 2 of the number Do the same for the largest HW number Click to ask the computer the smallest relative difference between two number that it can tell apart. This is called machine epsilon If you use an x86 64-bit CPU, the above is what you get. HW-Problem 1 Use any software (C++, C#, MATLAB, Excel,…) but not Mathematica (because it is too smart and can handle the test below) to do this: 1. Let’s denote xmax be the largest number your computer can handle in the APP test. Let x be a number just below xmax , such as ~0.75 xmax. (For example, I choose x=1.5*10^308). Double it (2*x) and print the result. 2. Let xmin be your computer smallest number. Choose x just above it. Then find 0.5*x and print output. Here is an example what happens in Excel: Excel error: instead of giving the correct answer 3*10^308, Number just slightly less than it gives error output. -
Accurate Measurement of Small Execution Times – Getting Around Measurement Errors Carlos Moreno, Sebastian Fischmeister
1 Accurate Measurement of Small Execution Times – Getting Around Measurement Errors Carlos Moreno, Sebastian Fischmeister Abstract—Engineers and researchers often require accurate example, a typical mitigation approach consists of executing measurements of small execution times or duration of events in F multiple times, as shown below: a program. Errors in the measurement facility can introduce time start = get_current_time() important challenges, especially when measuring small intervals. Repeat N times: Mitigating approaches commonly used exhibit several issues; in Execute F particular, they only reduce the effect of the error, and never time end = get_current_time() eliminate it. In this letter, we propose a technique to effectively // execution time = (end - start) / N eliminate measurement errors and obtain a robust statistical The execution time that we obtain is subject to the mea- estimate of execution time or duration of events in a program. surement error divided by N; by choosing a large enough The technique is simple to implement, yet it entirely eliminates N, we can make this error arbitrarily low. However, this the systematic (non-random) component of the measurement error, as opposed to simply reduce it. Experimental results approach has several potential issues: (1) repeating N times confirm the effectiveness of the proposed method. can in turn introduce an additional unknown overhead, if coded as a for loop. Furthermore, this is subject to uncertainty in I. MOTIVATION that the compiler may or may not implement loop unrolling, meaning that the user could be unaware of whether this Software engineers and researchers often require accurate overhead is present; (2) the efficiency of the error reduction measurements of small execution times or duration of events in process is low; that is, we require a large N (thus, potentially a program.