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Empirical Study on the Usage of Graph Query Languages in Open Source Java Projects
Empirical Study on the Usage of Graph Query Languages in Open Source Java Projects Philipp Seifer Johannes Härtel Martin Leinberger University of Koblenz-Landau University of Koblenz-Landau University of Koblenz-Landau Software Languages Team Software Languages Team Institute WeST Koblenz, Germany Koblenz, Germany Koblenz, Germany [email protected] [email protected] [email protected] Ralf Lämmel Steffen Staab University of Koblenz-Landau University of Koblenz-Landau Software Languages Team Koblenz, Germany Koblenz, Germany University of Southampton [email protected] Southampton, United Kingdom [email protected] Abstract including project and domain specific ones. Common applica- Graph data models are interesting in various domains, in tion domains are management systems and data visualization part because of the intuitiveness and flexibility they offer tools. compared to relational models. Specialized query languages, CCS Concepts • General and reference → Empirical such as Cypher for property graphs or SPARQL for RDF, studies; • Information systems → Query languages; • facilitate their use. In this paper, we present an empirical Software and its engineering → Software libraries and study on the usage of graph-based query languages in open- repositories. source Java projects on GitHub. We investigate the usage of SPARQL, Cypher, Gremlin and GraphQL in terms of popular- Keywords Empirical Study, GitHub, Graphs, Query Lan- ity and their development over time. We select repositories guages, SPARQL, Cypher, Gremlin, GraphQL based on dependencies related to these technologies and ACM Reference Format: employ various popularity and source-code based filters and Philipp Seifer, Johannes Härtel, Martin Leinberger, Ralf Lämmel, ranking features for a targeted selection of projects. -
Return of Organization Exempt from Income
OMB No. 1545-0047 Return of Organization Exempt From Income Tax Form 990 Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung benefit trust or private foundation) Open to Public Department of the Treasury Internal Revenue Service The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2011 calendar year, or tax year beginning 5/1/2011 , and ending 4/30/2012 B Check if applicable: C Name of organization The Apache Software Foundation D Employer identification number Address change Doing Business As 47-0825376 Name change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial return 1901 Munsey Drive (909) 374-9776 Terminated City or town, state or country, and ZIP + 4 Amended return Forest Hill MD 21050-2747 G Gross receipts $ 554,439 Application pending F Name and address of principal officer: H(a) Is this a group return for affiliates? Yes X No Jim Jagielski 1901 Munsey Drive, Forest Hill, MD 21050-2747 H(b) Are all affiliates included? Yes No I Tax-exempt status: X 501(c)(3) 501(c) ( ) (insert no.) 4947(a)(1) or 527 If "No," attach a list. (see instructions) J Website: http://www.apache.org/ H(c) Group exemption number K Form of organization: X Corporation Trust Association Other L Year of formation: 1999 M State of legal domicile: MD Part I Summary 1 Briefly describe the organization's mission or most significant activities: to provide open source software to the public that we sponsor free of charge 2 Check this box if the organization discontinued its operations or disposed of more than 25% of its net assets. -
Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin
Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin Harsh Thakkar1, S¨orenAuer1;2, Maria-Esther Vidal2 1 Smart Data Analytics Lab (SDA), University of Bonn, Germany 2 TIB & Leibniz University of Hannover, Germany [email protected], [email protected] Abstract. Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differ- ently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into an unforeseen race of developing new task-specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present-day graph query languages are focused towards flex- ible graph pattern matching (aka sub-graph matching), whereas graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph-based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language, and machine provide a common platform for supporting any graph computing sys- tem (such as an OLTP graph database or OLAP graph processors). In this extended report, we present a formalization of graph pattern match- ing for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra. Keywords: Graph Pattern Matching, Graph Traversal, Gremlin, Graph Alge- bra 1 Introduction Upon observing the evolution of information technology, we can observe a trend arXiv:1908.06265v2 [cs.DB] 7 Sep 2019 from data models and knowledge representation techniques being tightly bound to the capabilities of the underlying hardware towards more intuitive and natural methods resembling human-style information processing. -
Licensing Information User Manual
Oracle® Database Express Edition Licensing Information User Manual 18c E89902-02 February 2020 Oracle Database Express Edition Licensing Information User Manual, 18c E89902-02 Copyright © 2005, 2020, Oracle and/or its affiliates. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or “commercial computer software documentation” pursuant to the applicable Federal -
Large Scale Querying and Processing for Property Graphs Phd Symposium∗
Large Scale Querying and Processing for Property Graphs PhD Symposium∗ Mohamed Ragab Data Systems Group, University of Tartu Tartu, Estonia [email protected] ABSTRACT Recently, large scale graph data management, querying and pro- cessing have experienced a renaissance in several timely applica- tion domains (e.g., social networks, bibliographical networks and knowledge graphs). However, these applications still introduce new challenges with large-scale graph processing. Therefore, recently, we have witnessed a remarkable growth in the preva- lence of work on graph processing in both academia and industry. Querying and processing large graphs is an interesting and chal- lenging task. Recently, several centralized/distributed large-scale graph processing frameworks have been developed. However, they mainly focus on batch graph analytics. On the other hand, the state-of-the-art graph databases can’t sustain for distributed Figure 1: A simple example of a Property Graph efficient querying for large graphs with complex queries. Inpar- ticular, online large scale graph querying engines are still limited. In this paper, we present a research plan shipped with the state- graph data following the core principles of relational database systems [10]. Popular Graph databases include Neo4j1, Titan2, of-the-art techniques for large-scale property graph querying and 3 4 processing. We present our goals and initial results for querying ArangoDB and HyperGraphDB among many others. and processing large property graphs based on the emerging and In general, graphs can be represented in different data mod- promising Apache Spark framework, a defacto standard platform els [1]. In practice, the two most commonly-used graph data models are: Edge-Directed/Labelled graph (e.g. -
Maven by Example I
Maven by Example i Maven by Example Ed. 0.7 Maven by Example ii Contents 1 Introducing Apache Maven1 1.1 Maven. What is it?....................................1 1.2 Convention Over Configuration...............................2 1.3 A Common Interface....................................3 1.4 Universal Reuse through Maven Plugins..........................3 1.5 Conceptual Model of a “Project”..............................4 1.6 Is Maven an alternative to XYZ?..............................5 1.7 Comparing Maven with Ant................................6 2 Installing Maven 10 2.1 Verify your Java Installation................................ 10 2.2 Downloading Maven.................................... 11 2.3 Installing Maven...................................... 11 Maven by Example iii 2.3.1 Installing Maven on Linux, BSD and Mac OS X................. 11 2.3.2 Installing Maven on Microsoft Windows...................... 12 2.3.2.1 Setting Environment Variables..................... 12 2.4 Testing a Maven Installation................................ 13 2.5 Maven Installation Details................................. 13 2.5.1 User-Specific Configuration and Repository.................... 14 2.5.2 Upgrading a Maven Installation.......................... 15 2.6 Uninstalling Maven..................................... 15 2.7 Getting Help with Maven.................................. 15 2.8 About the Apache Software License............................ 16 3 A Simple Maven Project 17 3.1 Introduction......................................... 17 3.1.1 Downloading -
Introduction to Graph Database with Cypher & Neo4j
Introduction to Graph Database with Cypher & Neo4j Zeyuan Hu April. 19th 2021 Austin, TX History • Lots of logical data models have been proposed in the history of DBMS • Hierarchical (IMS), Network (CODASYL), Relational, etc • What Goes Around Comes Around • Graph database uses data models that are “spiritual successors” of Network data model that is popular in 1970’s. • CODASYL = Committee on Data Systems Languages Supplier (sno, sname, scity) Supply (qty, price) Part (pno, pname, psize, pcolor) supplies supplied_by Edge-labelled Graph • We assign labels to edges that indicate the different types of relationships between nodes • Nodes = {Steve Carell, The Office, B.J. Novak} • Edges = {(Steve Carell, acts_in, The Office), (B.J. Novak, produces, The Office), (B.J. Novak, acts_in, The Office)} • Basis of Resource Description Framework (RDF) aka. “Triplestore” The Property Graph Model • Extends Edge-labelled Graph with labels • Both edges and nodes can be labelled with a set of property-value pairs attributes directly to each edge or node. • The Office crew graph • Node �" has node label Person with attributes: <name, Steve Carell>, <gender, male> • Edge �" has edge label acts_in with attributes: <role, Michael G. Scott>, <ref, WiKipedia> Property Graph v.s. Edge-labelled Graph • Having node labels as part of the model can offer a more direct abstraction that is easier for users to query and understand • Steve Carell and B.J. Novak can be labelled as Person • Suitable for scenarios where various new types of meta-information may regularly -
Scripting for the Java Platform
Scripting for the Java Platform Christopher M. Judd President/Consultant Judd Solutions, LLC Christopher M. Judd • President/Consultant of Judd Solutions • Central Ohio Java User Group (COJUG) coordinator Agenda • Java Scripting Overview •Examples –API – Script Shell – Java EE Debugging • Alternative Java Scripting Engines – Configuring –Creating • Closing Thoughts • Resources •Q&A Java is the greatest language ever invented Developer’s tools Every developer’s toolbox should contain a static typed language like Java or C# and a dynamically typed scripting language like JavaScript, Ruby or Groovy. Java Scripting • Java Scripting support added in Java SE 6 • JSR 223: Scripting for the Java Platform • Java Virtual Machine – Executes “language-neutral” bytecode – Rich class library Java JavaScript Groovy – Multi-platform JVM •Features JavaScript Groovy – API to evaluate scripts – Embedded JavaScript engine (Rhino 1.6R2) – Scripting engine discovery mechanism – Java Scripting command-line interpreter (jrunscript) Reasons for Scripting •Flexibility • Simplicity (Domain Specific Language) • Interpreted • Development productivity •Dynamic typing • Expressive syntax •FUN Scripting Uses • Configuration •Customization • Automation • Debugging • Templating •Unit Testing •Prototyping • Web Scripting • Data transport Scripting Options •JavaScript – Rhino – www.mozilla.org/rhino/ •Groovy (JSR-241) – groovy.codehaus.org •Python –Jython –www.jython.org •Ruby –JRuby–jruby.codehaus.org •TCL –Jacl–tcljava.sourceforge.net • Java (JSR-274) – BeanShell www.beanshell.org -
The Query Translation Landscape: a Survey
The Query Translation Landscape: a Survey Mohamed Nadjib Mami1, Damien Graux2,1, Harsh Thakkar3, Simon Scerri1, Sren Auer4,5, and Jens Lehmann1,3 1Enterprise Information Systems, Fraunhofer IAIS, St. Augustin & Dresden, Germany 2ADAPT Centre, Trinity College of Dublin, Ireland 3Smart Data Analytics group, University of Bonn, Germany 4TIB Leibniz Information Centre for Science and Technology, Germany 5L3S Research Center, Leibniz University of Hannover, Germany October 2019 Abstract Whereas the availability of data has seen a manyfold increase in past years, its value can be only shown if the data variety is effectively tackled —one of the prominent Big Data challenges. The lack of data interoperability limits the potential of its collective use for novel applications. Achieving interoperability through the full transformation and integration of diverse data structures remains an ideal that is hard, if not impossible, to achieve. Instead, methods that can simultaneously interpret different types of data available in different data structures and formats have been explored. On the other hand, many query languages have been designed to enable users to interact with the data, from relational, to object-oriented, to hierarchical, to the multitude emerging NoSQL languages. Therefore, the interoperability issue could be solved not by enforcing physical data transformation, but by looking at techniques that are able to query heterogeneous sources using one uniform language. Both industry and research communities have been keen to develop such techniques, which require the translation of a chosen ’universal’ query language to the various data model specific query languages that make the underlying data accessible. In this article, we survey more than forty query translation methods and tools for popular query languages, and classify them according to eight criteria. -
Session 6 - Main Theme J2EE Component-Based Computing Environments
Application Servers G22.3033-011 Session 6 - Main Theme J2EE Component-Based Computing Environments Dr. Jean-Claude Franchitti New York University Computer Science Department Courant Institute of Mathematical Sciences 1 Agenda Component Technologies Database Technology Review EJB Component Model J2EE Services JNDI, JMS, JTS, CMP/BMP/JDBC, JavaMail, etc. J2EE Web Architectures Security in J2EE Application Servers Structured Applications Design Tips Summary Readings Assignment #5 2 1 Summary of Previous Session COM and COM+ Introduction to .Net Component Technologies Object Management Architectures Java-Based Application Servers Windows Services Summary Readings Assignment #5 3 Additional References Intranet Architectures and Performance Report http://www.techmetrix.com/lab/benchcenter/archiperf/archiper ftoc.shtml#TopOfPage RMI FAQ http://java.sun.com/products/javaspaces/faqs/rmifaq.html CORBA beyond the firewall http://www.bejug.org/new/pages/articles/corbaevent/orbix/ Web Object Integration (vision document) http://www.objs.com/survey/web-object-integration.htm 4 2 Application Servers Architectures Application Servers for Enhanced HTML (traditional) a.k.a., Page-Based Application Servers Mostly Used to Support Standalone Web Applications New Generation Page-Based Script-Oriented App. Servers First Generation Extensions (e.g., Microsoft IIS with COM+/ASP) Servlet/JSP Environments XSP Environment Can now be used as front-end to enterprise applications Hybrid development environments Distributed Object -
Abkürzungs-Liste ABKLEX
Abkürzungs-Liste ABKLEX (Informatik, Telekommunikation) W. Alex 1. Juli 2021 Karlsruhe Copyright W. Alex, Karlsruhe, 1994 – 2018. Die Liste darf unentgeltlich benutzt und weitergegeben werden. The list may be used or copied free of any charge. Original Point of Distribution: http://www.abklex.de/abklex/ An authorized Czechian version is published on: http://www.sochorek.cz/archiv/slovniky/abklex.htm Author’s Email address: [email protected] 2 Kapitel 1 Abkürzungen Gehen wir von 30 Zeichen aus, aus denen Abkürzungen gebildet werden, und nehmen wir eine größte Länge von 5 Zeichen an, so lassen sich 25.137.930 verschiedene Abkür- zungen bilden (Kombinationen mit Wiederholung und Berücksichtigung der Reihenfol- ge). Es folgt eine Auswahl von rund 16000 Abkürzungen aus den Bereichen Informatik und Telekommunikation. Die Abkürzungen werden hier durchgehend groß geschrieben, Akzente, Bindestriche und dergleichen wurden weggelassen. Einige Abkürzungen sind geschützte Namen; diese sind nicht gekennzeichnet. Die Liste beschreibt nur den Ge- brauch, sie legt nicht eine Definition fest. 100GE 100 GBit/s Ethernet 16CIF 16 times Common Intermediate Format (Picture Format) 16QAM 16-state Quadrature Amplitude Modulation 1GFC 1 Gigabaud Fiber Channel (2, 4, 8, 10, 20GFC) 1GL 1st Generation Language (Maschinencode) 1TBS One True Brace Style (C) 1TR6 (ISDN-Protokoll D-Kanal, national) 247 24/7: 24 hours per day, 7 days per week 2D 2-dimensional 2FA Zwei-Faktor-Authentifizierung 2GL 2nd Generation Language (Assembler) 2L8 Too Late (Slang) 2MS Strukturierte -
Apache Buildr in Action a Short Intro
Apache Buildr in Action A short intro BED 2012 Dr. Halil-Cem Gürsoy, adesso AG 29.03.12 About me ► Round about 12 Years in the IT, Development and Consulting ► Before that development in research (RNA secondary structures) ► Software Architect @ adesso AG, Dortmund ► Main focus on Java Enterprise (Spring, JEE) and integration projects > Build Management > Cloud > NoSQL / BigData ► Speaker and Author 29.03.12 2 Scala für Enterprise-Applikationen Agenda ► Why another Build System? ► A bit history ► Buildr catchwords ► Tasks ► Dependency management ► Testing ► Other languages ► Extending 3 Apache Buildr in Action – BED-Con 2012 Any aggressive Maven fanboys here? http://www.flickr.com/photos/bombardier/19428000/4 Apache Buildr in Action – BED-Con 2012 Collected quotes about Maven “Maven is such a pain in the ass” http://appwriter.com/what-if-maven-was-measured-cost-first-maven-project 5 Apache Buildr in Action – BED-Con 2012 Maven sucks... ► Convention over configuration > Inconsistent application of convention rules > High effort needed to configure ► Documentation > Which documentation? (ok, gets better) ► “Latest and greatest” plugins > Maven @now != Maven @yesterday > Not reproducible builds! ► Which Bugs are fixed in Maven 3? 6 Apache Buildr in Action – BED-Con 2012 Other buildsystems ► Ant > Still good and useful, can do everything... but XML ► Gradle > Groovy based > Easy extensible > Many plugins, supported by CI-Tools ► Simple Build Tool > In Scala for Scala (but does it for Java, too) 7 Apache Buildr in Action – BED-Con 2012 Apache