DOCSLIB.ORG

Temporal Graph Record Linkage and K-Safe Approximate Match ______

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Temporal Graph Record Linkage and K-Safe Approximate Match ______ TEMPORAL GRAPH RECORD LINKAGE AND K-SAFE APPROXIMATE MATCH _____________________________________________________________ A Dissertation Submitted to the Temple University Graduate Board _____________________________________________________________ In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY _____________________________________________________________ By Joseph Jupin December 2016 Examining Committee Members: Dr. Justin Yuan Shi, Advisory Chair, CIS Department Dr. Yuhong Guo, CIS Department Dr. Edward Dragut, CIS Department Dr. Bo Ji, CIS Department Dr. Boleslaw K. Szymanski, External Member, RPI CS Department © Copyright 2016 By Joseph Jupin All rights reserved ii ABSTRACT Temporal Graph Record Linkage and k-Safe Approximate Match: Graph Aggregated Temporal and Relational Data Enhanced Record Linkage with Iterative Matching and Hash Optimized Edit-Distance String Comparison By Joseph Jupin email: [email protected] Temple University, 2016 Committee: Dr. Justin Yuan Shi, Advisor and Chair Dr. Yuhong Guo Dr. Edward Dragut Dr. Bo Ji Dr. Boleslaw K. Szymanski Since the advent of electronic data processing, organizations have accrued vast amounts of data contained in multiple databases with no reliable global unique identifier. These databases were developed by different departments for different purposes at different times. Organizing and analyzing these data for human services requires linking records from all sources. RL (Record Linkage) is a process that connects records that are related to the identical or a sufficiently similar entity from multiple heterogeneous databases. RL is a data and compute intensive, mission critical process. The process must be efficient enough to process big data and effective enough to provide accurate matches. We have evaluated an RL system that is currently in use by a local health and human services department. We found that they were using the typical approach that was iii offered by Fellegi and Sunter with tuple-by-tuple processing, using the Soundex as the primary approximate string matching method. The Soundex has been found to be unreliable both as a phonetic and as an approximate string matching method. We found that their data, in many cases, has more than one value per field, suggesting that the data were queried from a 5NF data base. Consider that if a woman has been married 3 times, she may have up to 4 last names on record. This query process produced more than one tuple per database/entity apparently generating a Cartesian product of this data. In many cases, more than a dozen tuples were observed for a single database/entity. This approach is both ineffective and inefficient. An effective RL method should handle this multi-data without redundancy and use edit-distance for approximate string matching. However, due to high computational complexity, edit-distance will not scale well with big data problems. We developed two methodologies for resolving the aforementioned issues: PSH and ALIM. PSH – The Probabilistic Signature Hash is a composite method that increases the speed of Damerau-Levenshtein edit-distance. It combines signature filtering, probabilistic hashing, length filtering and prefix pruning to increase the speed of edit- distance. It is also lossless because it does not lose any true positive matches. ALIM – Aggregate Link and Iterative Match is a graph-based record linkage methodology that uses a multi-graph to store demographic data about people. ALIM performs string matching as records are inserted into the graph. ALIM eliminates data redundancy and stores the relationships between data. We tested PSH for string comparison and found it to be approximately 6,000 times faster than DL. We tested it against the trie-join methods and found that they are up to 6.26 iv times faster but lose between 10 and 20 percent of true positives. We tested ALIM against a method currently in use by a local health and human services department and found ALIM to produce significantly more matches (even with more restrictive match criteria) and that ALIM ran more than twice as fast. ALIM handles the multi-data problem and PSH allows the use of edit-distance comparison in this RL model. ALIM is more efficient and effective than a currently implemented RL system. This model can also be expanded to perform social network analysis and temporal data modeling. For human services, temporal modeling can reveal how policy changes and treatments affect clients over time and social network analysis can determine the effects of these on whole families by facilitating family linkage. v ACKNOWLEDGEMENTS I would like to thank my advisor, committee chair and associate chair of the CIS department, Dr. Justin Yuan Shi for all of his help and guidance with this project. Dr. Shi provided me with this research problem after being contacted by a local health department to assess their Record Linkage system. Through his efforts, we had the very rare privilege of access to a real system with real data. This access is usually very restricted due to HIPPA privacy regulations. He was always there to help me when meeting with health department officials and in developing a methodology to assess and report in the health departments RL system. He has been very patient and has invested time and effort into our project. He always made time to meet and discuss strategies and solutions to problems that would arise during the development of the solution to or problem. I also thank the other members of my dissertation committee, Dr. Yuhong Guo, Dr. Edward Dragut, Dr. Bo Ji and Dr. Boleslaw K. Szymanski for their time and consideration and for agreeing to participate and review in my work. I am glad to have had faculty with experience and interest in data problems accept involvement in my project. I would also like to thank the members of my former project, analyzing data from the ProDES project. Dr. Zoran Obradovic was my former advisor and committee chair. He provided me with experience working on data collected from multiple sources and all the issues that these data represent. Dr. Philip W. Harris was our primary investigator and provided the problem and the data along with valuable domain advice for working with vi Criminal Justice and Social Sciences data. Dr. Alan Izenman, Dr. Jeremy Mennis and Dr. Heidi Grunwald were other members of the group that also provided insight into processing data, analyzing data and reporting on results obtained from our research. My fellow graduate students: Brian Lockwood and Yilian Qin. Hope that their post Temple careers are both successful and satisfying. I would also like to thank officials from the Philadelphia Department of Health. Dr. Donald Schwarz, Deputy Mayor of Health and Opportunity, Susan Kretsge, Chief of Staff for the Office of Health and Opportunity, Swamy Bodige, Analyst for the Office of Health and Opportunity, LaVern Wright, Program Director for Integrated Systems for the Office of Health and Opportunity, and Dr. Zheng Wen, IFI, for their advice and support in assessing and understanding their RL system. I would also thank the City of Philadelphia’s Office of Health and Opportunity for grant support for this project. vii TABLE OF CONTENTS ABSTRACT .................................................................................................................................... iii ACKNOWLEDGEMENTS ............................................................................................................ vi LIST OF FIGURES ......................................................................................................................... x LIST OF TABLES ......................................................................................................................... xii LIST OF EQUATIONS ................................................................................................................. xiv I PREFACE ...................................................................................................................................... 1 1 INTRODUCTION TO RECORD LINKAGE .......................................................................... 2 1.1 Challenges and Principals of Record Linkage ................................................................... 4 1.2 Proposed Methodology ...................................................................................................... 5 1.3 Envisioned Contributions ................................................................................................... 6 1.4 Organization of Dissertation .............................................................................................. 7 1.5 Notation and Formalization ............................................................................................... 8 2 BACKGROUND ...................................................................................................................... 9 2.1 Standardization ................................................................................................................ 13 2.2 Blocking ........................................................................................................................... 14 2.3 Comparing and Matching ................................................................................................ 17 2.4 Field-level Comparison .................................................................................................... 18 2.5 Survey of Existing RL Methods......................................................................................
Load more

Recommended publications
  • Hardware Pattern Matching for Network Traffic Analysis in Gigabit
    Technische Universitat¨ Munchen¨ Fakultat¨ fur¨ Informatik Diplomarbeit in Informatik Hardware Pattern Matching for Network Traffic Analysis in Gigabit Environments Gregor M. Maier Aufgabenstellerin: Prof. Anja Feldmann, Ph. D. Betreuerin: Prof. Anja Feldmann, Ph. D. Abgabedatum: 15. Mai 2007 Ich versichere, dass ich diese Diplomarbeit selbst¨andig verfasst und nur die angegebe- nen Quellen und Hilfsmittel verwendet habe. Datum Gregor M. Maier Abstract Pattern Matching is an important task in various applica- tions, including network traffic analysis and intrusion detec- tion. In modern high speed gigabit networks it becomes un- feasible to search for patterns using pure software implemen- tations, due to the amount of data that must be searched. Furthermore applications employing pattern matching often need to search for several patterns at the same time. In this thesis we explore the possibilities of using FPGAs for hardware pattern matching. We analyze the applicability of various pattern matching algorithms for hardware imple- mentation and implement a Rabin-Karp and an approximate pattern matching algorithm in Endace’s network measure- ment cards using VHDL. The implementations are evalu- ated and compared to pure software matching solutions. To demonstrate the power of hardware pattern matching, an example application for traffic accounting using hardware pattern matching is presented as a proof-of-concept. Since some systems like network intrusion detection systems an- alyze reassembled TCP streams, possibilities for hardware TCP reassembly combined with hardware pattern matching are discussed as well. Contents vii Contents List of Figures ix 1 Introduction 1 1.1 Motivation . 1 1.2 Related Work . 2 1.3 About Endace DAG Network Monitoring Cards .
    [Show full text]
  • Comprehensive Examinations in Computer Science 1872 - 1878
    COMPREHENSIVE EXAMINATIONS IN COMPUTER SCIENCE 1872 - 1878 edited by Frank M. Liang STAN-CS-78-677 NOVEMBER 1078 (second Printing, August 1979) COMPUTER SCIENCE DEPARTMENT School of Humanities and Sciences STANFORD UNIVERSITY COMPUTER SC l ENCE COMPREHENSIVE EXAMINATIONS 1972 - 1978 b Y the faculty and students of the Stanford University Computer Science Department edited by Frank M. Liang Abstract Since Spring 1972, the Stanford Computer Science Department has periodically given a "comprehensive examination" as one of the qualifying exams for graduate students. Such exams generally have consisted of a six-hour written test followed by a several-day programming problem. Their intent is to make it possible to assess whether a student is sufficiently prepared in all the important aspects of computer science. This report presents the examination questions from thirteen comprehensive examinations, along with their solutions. The preparation of this report has been supported in part by NSF grant MCS 77-23738 and in part by IBM Corporation. Foreword This report probably contains as much concentrated computer science per page as any document In existence - it is the result of thousands of person-hours of creative work by the entire staff of Stanford's Computer Science Department, together with dozens of h~ghly-talented students who also helped to compose the questions. Many of these questions have never before been published. Thus I think every person interested in computer science will find it stimulating and helpful to study these pages. Of course, the material is so concentrated it is best not taken in one gulp; perhaps the wisest policy would be to keep a copy on hand in the bathroom at all times, for those occasional moments when inspirational reading is desirable.
    [Show full text]
  • Trusted Research Environments (TRE) a Strategy to Build Public Trust and Meet Changing Health Data Science Needs
    Trusted Research Environments (TRE) A strategy to build public trust and meet changing health data science needs Green Paper v2.0 dated 21 July 2020 – For sign off Table of Contents Executive Summary .................................................................................................................................................... 3 Status of the document .............................................................................................................................................. 5 Overview .................................................................................................................................................................... 6 Purpose ........................................................................................................................................................................... 6 Background .................................................................................................................................................................... 6 The case for TREs providing access to health data through safe settings ...................................................................... 8 Requirements for a Trusted Research Environment...................................................................................................10 Safe people ................................................................................................................................................................... 10 Safe projects ................................................................................................................................................................
    [Show full text]
  • A Personalized Cloud-Based Traffic Redundancy Elimination for Smartphones Vivekgautham Soundararaj Clemson University, [email protected]
    Clemson University TigerPrints All Theses Theses 5-2016 TailoredRE: A Personalized Cloud-based Traffic Redundancy Elimination for Smartphones Vivekgautham Soundararaj Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Recommended Citation Soundararaj, Vivekgautham, "TailoredRE: A Personalized Cloud-based Traffic Redundancy Elimination for Smartphones" (2016). All Theses. 2387. https://tigerprints.clemson.edu/all_theses/2387 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. TailoredRE: A Personalized Cloud-based Traffic Redundancy Elimination for Smartphones A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Computer Engineering by Vivekgautham Soundararaj May 2016 Accepted by: Dr. Haiying Shen, Committee Chair Dr. Rong Ge Dr. Walter Ligon Abstract The exceptional rise in usages of mobile devices such as smartphones and tablets has contributed to a massive increase in wireless network traffic both Cellular (3G/4G/LTE) and WiFi. The unprecedented growth in wireless network traffic not only strain the battery of the mobile devices but also bogs down the last-hop wireless access links. Interestingly, a significant part of this data traffic exhibits high level of redundancy in them due to re- peated access of popular contents in the web. Hence, a good amount of research both in academia and in industries has studied, analyzed and designed diverse systems that attempt to eliminate redundancy in the network traffic.
    [Show full text]
  • Branchclust Tutorial
    BranchClust A Phylogenetic Algorithm for Selecting Gene Families Version 1.01 Tutorial http://www.bioinformatics.org/branchclust Copyright © Maria Poptsova and J. Peter Gogarten 2006-2007 Tutorial Selection of orthologous families with BranchClust is a multi-stage process that includes processing BLAST results, assembling superfamilies, alignment, reconstruction of phylogenetic trees, and applying the BranchClust method itself. Here we describe step-by- step procedures used for selection of orthologous families for a set of different taxa. All perl scripts described here are freely available and can be downloaded as one archive from the web-site: http://www.bioinformatics.org/branchclust/ All following procedures could be divided into 6 major stages: I. Downloading complete genomes and collection of significant hits II. Creation of taxa identification table III. Assemblage of superfamilies IV. Reconstruction of superfamily trees. V. Selection of orthologous families with BranchClust VI. Visualization of results with TreeDyn For all the programs described below, it is assumed that they are run from inside one directory. Perl-scripts create output files that are further used as input by other programs so that each step should be performed in order described in the tutorial. The scripts and programs were tested under both Mac OSX Darwin and Debian Linux operating sytems. Modules or standalone programs such as BioPerl, blastall, clustalw and treedyn need to be installed in the system. These programs are available from BioPerl – http://www.bioperl.org Blastall - http://www.ncbi.nlm.nih.gov/BLAST/download.shtml Clustalw - http://bips.u-strasbg.fr/fr/Documentation/ClustalX/ TreeDyn - http://www.treedyn.org I. Downloading complete genomes and collection of significant hits 1.
    [Show full text]
  • Approximate Pattern Matching with Index Structures
    Technische Universität München Fakultät für Informatik Lehrstuhl für Effiziente Algorithmen a Approximatec e Pattern Matching b with Index Structuresd f Johannes Krugel a c b d c e d f e e e f a d e b c f Technische Universität München Fakultät für Informatik Lehrstuhl für Effiziente Algorithmen Approximate Pattern Matching with Index Structures Johannes A. Krugel Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Helmut Seidl Prüfer der Dissertation: 1. Univ.-Prof. Dr. Ernst W. Mayr 2. Univ.-Prof. Dr. Stefan Kramer, Johannes Gutenberg-Universität Mainz Die Dissertation wurde am 06.05.2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 19.01.2016 angenommen. ii Zusammenfassung Ziel dieser Arbeit ist es, einen Überblick über das praktische Verhalten von Indexstrukturen und Algorithmen zur approximativen Textsuche (approximate pattern matching, APM) zu geben, abhängig von den Eigenschaften der Eingabe. APM ist die Suche nach Zeichenfolgen in Texten oder biologischen Sequenzen unter Berücksichtigung von Fehlern (wie z. B. Rechtschreibfehlern oder genetischen Mutationen). In der Offline-Variante dieses Problems kann der Text vorverarbeitet werden, um eine Indexstruktur aufzubauen bevor die Suchanfragen beantwortet werden. Diese Arbeit beschreibt und diskutiert praktisch relevante Indexstrukturen, Ähnlichkeitsmaÿe und Suchalgorithmen für APM. Wir schlagen einen neuen effizienten Suchalgorithmus für Suffixbäume im externen Speicher vor. Im Rahmen der Arbeit wurden mehrere Indexstrukturen und Algorithmen für APM implementiert und in einer Softwarebibliothek bereitgestellt; die Implementierungen sind effizient, stabil, generisch, getestet und online verfügbar.
    [Show full text]
  • How to Think Like a Computer Scientist
    How to Think Like a Computer Scientist Logo Version ii How to Think Like a Computer Scientist Logo Version Allen B. Downey Guido Gay Version 1.0 October 30, 2003 Copyright °c 2003 Allen B. Downey, Guido Gay. History: March 6, 2003: Allen B. Downey, How to Think Like a Computer Scientist. Java Version, fourth edition. October 30, 2003: Allen B. Downey, Guido Gay, How to Think Like a Com- puter Scientist. Logo Version, first edition. Permission is granted to copy, distribute, and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later ver- sion published by the Free Software Foundation; with Invariant Sections being “Preface”, with no Front-Cover Texts, and with no Back-Cover Texts. A copy of the license is included in the appendix entitled “GNU Free Documentation License.” The GNU Free Documentation License is available from www.gnu.org or by writing to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. The original form of this book is LATEX source code. Compiling this LATEX source has the effect of generating a device-independent representation of the book, which can be converted to other formats and printed. The LATEX source for this book is available from http://ibiblio.org/obp/thinkCS/ Prefazione Logo is a programming language mostly used as a teaching tool in primary education. If you know about its turtle graphics commands — such as repeat 10 [repeat 5 [fd 30 lt 72] lt 36] — you may be curious about its other capabilities.
    [Show full text]
  • A Best-First Anagram Hashing Filter for Approximate String Matching
    A best-first anagram hashing filter for approximate string matching with generalized edit distance Malin AHLBERG Gerlof BOUMA Språkbanken / Department of Swedish University of Gothenburg [email protected], [email protected] ABSTRACT This paper presents an efficient method for approximate string matching against a lexicon. We define a filter that for each source word selects a small set of target lexical entries, from which the best match is then selected using generalized edit distance, where edit operations can be assigned an arbitrary weight. The filter combines a specialized hash function with best-first search. Our work extends and improves upon a previously proposed hash-based filter, developed for matching with uniform-weight edit distance. We evaluate an approximate matching system implemented with the new best-first filter, by conducting several experiments on a historical corpus and a set of weighted rules taken from the literature. We present running times and discuss how performance varies using different stopping criteria and target lexica. The results show that the filter is suitable for large rule sets and million word corpora, and encourage further development. KEYWORDS: Approximate string matching, generalized edit distance, anagram hash, spelling variation, historical corpora. Proceedings of COLING 2012: Posters, pages 13–22, COLING 2012, Mumbai, December 2012. 13 1 Introduction A common task in text processing is to match tokens in running text to a dictionary, for instance to see if we recognize a token as an existing word or to retrieve further information about the token, like part-of-speech or distributional statistics. Such matching may be approximate: the dictionary entry that we are looking for might use a slightly different spelling than the token at hand.
    [Show full text]
  • The Answer Question in Question Answering Systems Engenharia
    The Answer Question in Question Answering Systems Andr´eFilipe Esteves Gon¸calves Disserta¸c~aopara obten¸c~aodo Grau de Mestre em Engenharia Inform´aticae de Computadores J´uri Presidente: Prof. Jos´eManuel Nunes Salvador Tribolet Orientador: Profa Maria Lu´ısaTorres Ribeiro Marques da Silva Coheur Vogal: Prof. Bruno Emanuel da Gra¸caMartins Novembro 2011 Acknowledgements I am extremely thankful to my Supervisor, Prof. Lu´ısaCoheur and also my colleague Ana Cristina Mendes (who pratically became my Co-Supervisor) for all their support throughout this journey. I could not have written this thesis without them, truly. I dedicate this to everyone that has inspired me and motivates me on a daily basis over the past year (or years) { i.e. my close friends, my chosen family. It was worthy. Thank you, you are my heroes. Resumo No mundo atual, os Sistemas de Pergunta Resposta tornaram-se uma resposta v´alidapara o problema da explos~aode informa¸c~aoda Web, uma vez que con- seguem efetivamente compactar a informa¸c~aoque estamos `aprocura numa ´unica resposta. Para esse efeito, um novo Sistema de Pergunta Resposta foi criado: Just.Ask. Apesar do Just.Ask j´aestar completamente operacional e a dar respostas corretas a um certo n´umerode quest~oes,permanecem ainda v´ariosproblemas a endere¸car. Um deles ´ea identifica¸c~aoerr´oneade respostas a n´ıvel de extra¸c~ao e clustering, que pode levar a que uma resposta errada seja retornada pelo sistema. Este trabalho lida com esta problem´atica,e ´ecomposto pelas seguintes tare- fas: a) criar corpora para melhor avaliar o sistema e o seu m´odulode respostas; b) apresentar os resultados iniciais do Just.Ask, com especial foco para o seu m´odulode respostas; c) efetuar um estudo do estado da arte em rela¸c~oesen- tre respostas e identifica¸c~aode par´afrases de modo a melhorar a extra¸c~aodas respostas do Just.Ask; d) analizar erros e detectar padr~oesde erros no mdulo de extra¸c~aode respostas do sistema; e) apresentar e implementar uma solu¸c~ao para os problemas detectados.
    [Show full text]
  • Comparing Namedcapture with Other R Packages for Regular Expressions by Toby Dylan Hocking
    CONTRIBUTED RESEARCH ARTICLE 328 Comparing namedCapture with other R packages for regular expressions by Toby Dylan Hocking Abstract Regular expressions are powerful tools for manipulating non-tabular textual data. For many tasks (visualization, machine learning, etc), tables of numbers must be extracted from such data before processing by other R functions. We present the R package namedCapture, which facilitates such tasks by providing a new user-friendly syntax for defining regular expressions in R code. We begin by describing the history of regular expressions and their usage in R. We then describe the new features of the namedCapture package, and provide detailed comparisons with related R packages (rex, stringr, stringi, tidyr, rematch2, re2r). Introduction Today regular expression libraries are powerful and widespread tools for text processing. A regular expression pattern is typically a character string that defines a set of possible matches in some other subject strings. For example the pattern o+ matches one or more lower-case o characters; it would match the last two characters in the subject foo, and it would not match in the subject bar. The focus of this article is regular expressions with capture groups, which are used to extract subject substrings. Capture groups are typically defined using parentheses. For example, the pattern [0-9]+ matches one or more digits (e.g. 123 but not abc), and the pattern [0-9]+-[0-9]+ matches a range of integers (e.g. 9-5). The pattern ([0-9]+)-([0-9]+) will perform matching identically, but provides access by number/index to the strings matched by the capturing sub-patterns enclosed in parentheses (group 1 matches 9, group 2 matches 5).
    [Show full text]
  • The Power of Prediction: Cloud Bandwidth and Cost Reduction
    The Power of Prediction: Cloud Bandwidth and Cost Reduction ∗ † Eyal Zohar Israel Cidon Osnat (Ossi) Mokryn Technion - Israel Institute of Technion - Israel Institute of Tel Aviv Academic College Technology Technology [email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we present PACK (Predictive ACKs), a novel end-to- Cloud computing offers its customers an economical and con- end Traffic Redundancy Elimination (TRE) system, designed for venient pay as you go service model, known also as usage-based cloud computing customers. pricing [6]. Cloud customers1 pay only for the actual use of com- Cloud-based TRE needs to apply a judicious use of cloud re- puting resources, storage and bandwidth, according to their chang- sources so that the bandwidth cost reduction combined with the ad- ing needs, utilizing the cloud’s scalable and elastic computational ditional cost of TRE computation and storage would be optimized. capabilities. Consequently, cloud customers, applying a judicious PACK’s main advantage is its capability of offloading the cloud- use of the cloud’s resources, are motivated to use various traffic re- server TRE effort to end-clients, thus minimizing the processing duction techniques, in particular Traffic Redundancy Elimination costs induced by the TRE algorithm. (TRE), for reducing bandwidth costs. Unlike previous solutions, PACK does not require the server to Traffic redundancy stems from common end-users’ activities, continuously maintain clients’ status. This makes PACK very suit- such as repeatedly accessing, downloading, distributing and modi- able for pervasive computation environments that combine client fying the same or similar information items (documents, data, web mobility and server migration to maintain cloud elasticity.
    [Show full text]
  • Necessary to Conduct Most Analyses Commercial Software Typically Have (Very Bad) Installers Open Source Software Typically Have
    installing software necessary to conduct most analyses commercial software typically have (very bad) installers open source software typically have (bad) installers often (very) poorly documented tested on a limited number of configurations use the tested configuration if yours doesnt work dependencies are not always listed or ambiguously listed one of the most frustrating things about POSIX package managers installs executable (usually binary) and configuration files greatly simplifies installation and upgrades depends upon the (usually volunteer) package maintainers apt the Debian wrapper for dpkg used to install, update, remove, and purge packages will install dependencies for the target package http://packages.ubuntu.com/ if apt fails, try aptitude (the industrial strength version) apt sudo atp update updates the package cache sudo atp upgrade installs most upgrades sudo atp autoremove removes unneeded packages apt-cache search x searches repository for x apt-cache show x information for package x sudo apt install x installs package x sudo apt remove x removes package x sudo apt purge x removes package x and its files apt general use sudo atp update sudo atp upgrade sudo apt install scripts (interpreted code) simple scripts are placed in a directory listed in $PATH e.g. $HOME/bin dependencies must be satisfied interpreter interpreter extensions external programs environmental variables some interpreters (e.g. Perl, Python, JavaScript) have their own package management systems compiling… convert source code (text) into a (binary) executable file ./configure script that produces an appropriate MakeFile not required for all programs should indicate missing dependencies can set various options specify the (non–standard) location of a dependency set program specific options e.g.
    [Show full text]