Graph Query Portal

Total Page:16

File Type:pdf, Size:1020Kb

Graph Query Portal Graph Query Portal David Brock and Amit Dayal Client: Prashant Chandrasekar CS4624: Multimedia, Hypertext, and Information Access Professor Edward A. Fox Virginia Tech, Blacksburg, VA 24061 May 2, 2018 1 Table of Contents Table of Figures......................................................................................................................... 3 Introduction ............................................................................................................................... 6 Ontology ................................................................................................................................. 6 Semantic Web ......................................................................................................................... 7 Data Collection Platform ........................................................................................................ 8 Current Process ....................................................................................................................... 9 Requirements........................................................................................................................... 10 Project Deliverables .............................................................................................................. 10 Design ....................................................................................................................................... 10 Implementations ...................................................................................................................... 18 Developer’s Manual ................................................................................................................ 21 About..................................................................................................................................... 21 System Requirements............................................................................................................ 21 Node.js API ........................................................................................................................... 24 Neo4j Usage .......................................................................................................................... 25 Routes ................................................................................................................................... 25 Models................................................................................................................................... 27 Controllers............................................................................................................................. 35 Running the Node Server ...................................................................................................... 46 Accessing the Node Server ................................................................................................... 47 Running the Neo4j Service ................................................................................................... 48 Cypher Data Import and Relationship Creation.................................................................... 49 User’s Manual ......................................................................................................................... 55 /:graphName/participant ...................................................................................................... 55 /:graphName/participant/:id ................................................................................................. 55 /graph/view ........................................................................................................................... 56 /graph/findByPropertyValue ................................................................................................. 56 /graph/describe ..................................................................................................................... 57 /graph/addProperty............................................................................................................... 57 2 /graph/addLabel .................................................................................................................... 57 /graph/addRelationship......................................................................................................... 58 /graph/find............................................................................................................................. 59 /graph/fetchNode................................................................................................................... 59 /:graphName/engagement/:engagementType ....................................................................... 60 /compare/:graphName1/:graphName2/:labelName/:engagementType ............................... 60 /view/log/all........................................................................................................................... 61 /view/log/developer ............................................................................................................... 61 /view/log/graph ..................................................................................................................... 61 /view/log/participant ............................................................................................................. 62 /view/log/log .......................................................................................................................... 62 Commonly Encountered Errors ............................................................................................ 62 Testing ...................................................................................................................................... 63 Timeline ................................................................................................................................... 64 Lessons Learned ...................................................................................................................... 65 Future Work ............................................................................................................................ 67 Acknowledgements ................................................................................................................. 67 References ................................................................................................................................ 68 3 Table of Figures 1. An illustration of an ontology……………………………………………………...….......7 2. Semantic Web-Stack…...…………………………………………...………………….....8 3. Friendica’s ‘wall’ feature…………………………………………….………………..…..9 4. Graph of all vocabularies...………………………………………………………………12 5. MySQL Workbench Reverse Engineering of Friendica’s Database………...……...…...14 6. Version 1 of custom Ontology…………………………………………………………..15 7. Final Version of Ontology………………………………………..……………………...16 8. Fictitious example of a graph interaction using Friendica’s Wall Post feature………….16 9. Neo4j implementation of the interaction in Figure 8…………………………………….17 10. Api to Graph Database Diagram of Project………………………………………….…..19 11. Scenario and test queries…………………………………………………………………20 12. Root directory……………………………………………………………………………22 13. Changes in config file…………………..……………………………………………..…22 14. Logging configuration…………………………….…………………………………..…23 15. Shell Enablement………………………………………...………………...………….…23 16. Putty configuration……………………………………………..……………………...…24 17. Single Route example…………………………………………………...…………….…26 18. Engagement Controller requirement…………………………………….……………….26 19. Fetch Graph Function……………………………………………………………………28 20. doesTypeObjectExisit Function………………………………………………………….28 21. getRegexForEngagementType Function…………………………………...…………....29 22. findAnyNode Function……………………………………………………..……………29 23. findByPropertyValue Function……………………………………………..……………30 24. createNewProperty Function…………………………………………………………….30 25. createNewLabel Function………………………………………………….…………….31 26. runRawQuery Function……………………………………………………..……………31 27. createNewRelationship Function……………………………………………….………..32 28. Participant Model……...……………………………………………………...………….32 29. Multiple Participant Query……………………………………………………....………33 30. Engagement Model Class………………………………..………………………………33 31. Comparative Model Class………....………………………………..................................34 32. Graph Controller Class……………………………………………………………...…...36 33. findNode Controller processing……………………………………………….................37 34. findProppertyValue Controller processing…………………………………………...…38 35. createNewLabel Controller processing……………………………………………….....39 36. createNewProperty Controller processing………………..………………………...........39 37. createNewRelationship Controller processing………………………………….………..41 38. participantController class………..……………………………………...........................42 39. engagementController class………..…………………………………………………….43 4 40. comparativeController class…………...………………………………………………...44 41. logController class……………………………………………………………………….45 42. HTTP request for participant…………………………………………………………….47 43. Sample error message……………………………………………………………………48 44. Neo4j browser command line interface………………….………………………………49 5 Executive Summary Prashant Chandrasekar, a lead developer for the Social Interactome project, has tasked the team with creating a graph representation of the data collected from the social networks involved in that project. The data is currently stored in a MySQL database. The client
Recommended publications
  • ADL Report to Global Forum on Cyberhate 2015
    Report of the Anti-Defamation League on Confronting Cyberhate 5th Global Forum for Combating Anti-Semitism May, 2015 ANTI-DEFAMATION LEAGUE Barry Curtiss-Lusher National Chair Abraham H. Foxman National Director Kenneth Jacobson Deputy National Director Milton S. Schneider President, Anti-Defamation League Foundation CIVIL RIGHTS DIVISION Christopher Wolf Chair Deborah M. Lauter Director Steven M. Freeman Associate Director Eva Vega-Olds Assistant Director Jonathan Vick Assistant Director, Cyberhate Response CENTER ON EXTREMISM Mitch Markow Chair Oren Segal Director Lauren Steinberg Terrorism Analyst For additional and updated resources please see: www.adl.org Copies of this publication are available in the Rita and Leo Greenland Library and Research Center. ©2015 Anti-Defamation League | Printed in the United States of America | All Rights Reserved Anti-Defamation League 605 Third Avenue, New York, NY 10158-3560 www.adl.org Table of Contents PREFACE .........................................................................................................................................................4 INTRODUCTION ..............................................................................................................................................5 CHARTING PROGRESS ....................................................................................................................................6 A NEW CHALLENGE: TERRORIST USE OF SOCIAL MEDIA ............................................................................11 RECOMMENDATIONS
    [Show full text]
  • Seamless Interoperability and Data Portability in the Social Web for Facilitating an Open and Heterogeneous Online Social Network Federation
    Seamless Interoperability and Data Portability in the Social Web for Facilitating an Open and Heterogeneous Online Social Network Federation vorgelegt von Dipl.-Inform. Sebastian Jürg Göndör geb. in Duisburg von der Fakultät IV – Elektrotechnik und Informatik der Technischen Universität Berlin zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften - Dr.-Ing. - genehmigte Dissertation Promotionsausschuss: Vorsitzender: Prof. Dr. Thomas Magedanz Gutachter: Prof. Dr. Axel Küpper Gutachter: Prof. Dr. Ulrik Schroeder Gutachter: Prof. Dr. Maurizio Marchese Tag der wissenschaftlichen Aussprache: 6. Juni 2018 Berlin 2018 iii A Bill of Rights for Users of the Social Web Authored by Joseph Smarr, Marc Canter, Robert Scoble, and Michael Arrington1 September 4, 2007 Preamble: There are already many who support the ideas laid out in this Bill of Rights, but we are actively seeking to grow the roster of those publicly backing the principles and approaches it outlines. That said, this Bill of Rights is not a document “carved in stone” (or written on paper). It is a blog post, and it is intended to spur conversation and debate, which will naturally lead to tweaks of the language. So, let’s get the dialogue going and get as many of the major stakeholders on board as we can! A Bill of Rights for Users of the Social Web We publicly assert that all users of the social web are entitled to certain fundamental rights, specifically: Ownership of their own personal information, including: • their own profile data • the list of people they are connected to • the activity stream of content they create; • Control of whether and how such personal information is shared with others; and • Freedom to grant persistent access to their personal information to trusted external sites.
    [Show full text]
  • Auswertung Onlinebefragung Social Media
    HSD Auswertung Onlinebefragung Social Media Impressum Herausgeber Dezernat Kommunikation & Marketing Redaktion Rebecca Juwick M.A. Dezernat Kommunikation & Marketing Stand Februar 2016 2 Inhalt 1 Einleitung 4 2 Methode 5 3 Ergebnisse 6 Nutzung Social Networks 9 Zielgruppen 15 Funktionen & Inhalte 16 4 Fazit 22 3 Auswertung Social- Media-Umfrage 1 Einleitung Im Rahmen der Social-Media-Projektgruppe sollten die Anforderungen der unterschiedlichen Ziel- gruppen an das Social-Media-Angebot der Hochschule erhoben werden. Um dies möglichst effektiv umsetzen zu können, wurde eine Onlinebefragung für Studierende und Beschäftigte der Hochschule Düsseldorf durchgeführt. Diese liefen in zeitlichem Abstand zueinander jeweils 4 Wochen lang. Im Folgenden wird zuerst das methodische Vorgehen zur Befragung kurz erläutert, bevor die Ergeb- nisse vorgestellt werden. Alle Ergebnisse können den Datei-Anhängen „Auswertung_Studie- rende.xlsx“ und „Auswertung_Beschäftigte.xlsx“ entnommen werden. 4 Auswertung Social-Media-Umfrage 2 Methode Damit möglichst viele Studierende aus allen Fachbereichen und die Beschäftigten der Hochschule, unabhängig vom Standort, an der Erhebung teilnehmen konnten, wurde die Befragung als reine On- linebefragung durchgeführt. Aufgrund der Ähnlichkeit in Bezug auf die avisierten Zielgruppen, sowie der inhaltlichen Fragestellung wurde die Befragung in Kooperation mit der Campus IT, dem Daten- schutzbeauftragten und dem Fachbereich Sozial- und Kulturwissenschaften realisiert. Der Fragebogen wurde mithilfe der Software Unipark umgesetzt und über den entsprechenden Uni- park-Server für mehrere Wochen zur Beantwortung verfügbar gehalten. Der Umfang des Fragebo- gens wurde dabei so gewählt, dass eine Bearbeitungsdauer von 10 bis 15 Minuten – inklusive Brie- fing und Debriefing – nicht überschritten wurde. Zur Gewinnung von Teilnehmenden wurden diese über diverse Kanäle angesprochen. Auf der Start- seite des Internetauftritts der HSD wurde beispielsweise eine Meldung von der Pressestelle einge- stellt.
    [Show full text]
  • The National Hillumat Otti
    THE NATIONALUS009742853B2 HILLUMAT OTTI TIK (12 ) United States Patent ( 10 ) Patent No. : US 9 , 742 ,853 B2 Auerbach (45 ) Date of Patent: Aug. 22 , 2017 ( 54 ) DYNAMIC COMPUTER SYSTEMS AND ( 56 ) References Cited USES THEREOF U . S . PATENT DOCUMENTS (71 ) Applicant : Michael H . Auerbach , Rockville , MD 6 ,850 , 511 B2 2 /2005 Kats et al. (US ) 7 ,002 , 944 B2 2 /2006 Kats et al. (72 ) Inventor : Michael H . Auerbach, Rockville , MD (Continued ) (US ) FOREIGN PATENT DOCUMENTS ( 73 ) Assignee : The Michael Harrison Tretter EP 2151793 2 /2010 Auerbach Trust , New York , NY (US ) WO WO 2010 / 144766 12 / 2010 ( * ) Notice : Subject to any disclaimer, the term of this (Continued ) patent is extended or adjusted under 35 U . S . C . 154 (b ) by 177 days . OTHER PUBLICATIONS Ahtiainen , A . et al. (2009 ) “ Awareness Networking in Wireless (21 ) Appl. No. : 14 /512 ,442 Environments ,” Vehicular Technol. Mag . IEEE 4 ( 3 ) : 48 , 54 . ( 22 ) Filed : Oct. 12 , 2014 (Continued ) Pri Primary Examiner - Kevin Bates (65 ) Prior Publication Data Assistant Examiner — Nazia Naoreen US 2015 /0334044 A1 Nov . 19, 2015 ( 74 ) Attorney , Agent, or Firm — Jeffrey I. Auerbach ; AuerbachSchrot LLC Related U . S . Application Data (60 ) Provisional application No. 62/ 000 ,015 , filed on May (57 ) ABSTRACT 19 , 2014 . The invention concerns computer systems that are specially adapted to propagate content over a dynamic network , (51 ) Int. Cl. substantially in real time, by virtue of the locational prox H04L 12 /911 (2013 . 01) imity of network joined client computers . Preferably, the G06F 1730 ( 2006 .01 ) content will also be proximity -weighted , and more prefer ably also rank -weighted , topic -weighted , time -weighted , (Continued ) query - weighted , vote -weighted , and / or location - weighted .
    [Show full text]
  • Systematic Scoping Review on Social Media Monitoring Methods and Interventions Relating to Vaccine Hesitancy
    TECHNICAL REPORT Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy www.ecdc.europa.eu ECDC TECHNICAL REPORT Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy This report was commissioned by the European Centre for Disease Prevention and Control (ECDC) and coordinated by Kate Olsson with the support of Judit Takács. The scoping review was performed by researchers from the Vaccine Confidence Project, at the London School of Hygiene & Tropical Medicine (contract number ECD8894). Authors: Emilie Karafillakis, Clarissa Simas, Sam Martin, Sara Dada, Heidi Larson. Acknowledgements ECDC would like to acknowledge contributions to the project from the expert reviewers: Dan Arthus, University College London; Maged N Kamel Boulos, University of the Highlands and Islands, Sandra Alexiu, GP Association Bucharest and Franklin Apfel and Sabrina Cecconi, World Health Communication Associates. ECDC would also like to acknowledge ECDC colleagues who reviewed and contributed to the document: John Kinsman, Andrea Würz and Marybelle Stryk. Suggested citation: European Centre for Disease Prevention and Control. Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy. Stockholm: ECDC; 2020. Stockholm, February 2020 ISBN 978-92-9498-452-4 doi: 10.2900/260624 Catalogue number TQ-04-20-076-EN-N © European Centre for Disease Prevention and Control, 2020 Reproduction is authorised, provided the
    [Show full text]
  • Probabilistic Forecasting in Decision-Making: New Methods and Applications
    Probabilistic Forecasting in Decision-Making: New Methods and Applications Xiaojia Guo A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of University College London. UCL School of Management University College London November 10, 2020 2 I, Xiaojia Guo, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indi- cated in the work. 3 To my family for their invaluable support throughout the years. Abstract This thesis develops new methods to generate probabilistic forecasts and applies these methods to solve operations problems in practice. The first chapter introduces a new product life cycle model, the tilted-Gompertz model, which can predict the distribution of period sales and cumulative sales over a product’s life cycle. The tilted-Gompertz model is developed by exponential tilting the Gompertz model, which has been widely applied in modelling human mortality. Due to the tilting parameter, this new model is flexible and capable of describing a wider range of shapes compared to existing life cycle models. In two empirical studies, one on the adoption of new products and the other on search interest in social networking websites, I find that the tilted-Gompertz model performs well on quantile forecast- ing and point forecasting, when compared to other leading life-cycle models. In the second chapter, I develop a new exponential smoothing model that can cap- ture life-cycle trends. This new exponential smoothing model can also be viewed as a tilted-Gompertz model with time-varying parameters.
    [Show full text]
  • 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.
    [Show full text]
  • Social Media and the ISIS Narrative Daniel Cunningham, MA Sean F
    Social Media and the ISIS Narrative Daniel Cunningham, MA Sean F. Everton, PhD Robert Schroeder, MA Department of Defense Analysis Naval Postgraduate School Monterey, CA 93943 March 20, 2015 Abstract The Islamic State of Iraq and Syria (ISIS) has attracted the world’s attention and much of its wrath, primarily because of its rapid expansion in Iraq and Syria, its brutal treatment of religious minorities (e.g., Yazidis, Christians), and its beheadings of hostages from Western countries. At this point, it is unclear whether the group represents a global or a sectarian form of jihadism. Is it similar to al-Qaeda, which seeks to target the far enemy (i.e., Western countries such as the U.S. that some in the Muslim world believe exert a corrupting influence on Islamic countries), or is it more sectarian in that it focuses on targeting what it perceives to be the near enemies of Islam (i.e., “corrupt” Islamic nations)? In this paper we address this debate by examining ISIS’s online presence on the social media platform, Twitter, which serves as a forum for supporters to post and receive messages, images, videos, and links to websites to and from a wide-audience. The speed at which users can transmit and receive information via Twitter suggests that an analysis of ISIS-related user accounts and the key themes and concepts they disseminate can contribute to a better understanding of the group’s overall narrative. We examine ISIS’s online presence by extracting from Twitter the semantic networks of its most influential users. We find that a shift may be occurring in the ISIS narrative, from one that focuses on the near enemy to one that focuses on the far enemy.
    [Show full text]
  • Sept Thèses Sur La Fediverse Et Le Devenir Du Logiciel Libre Par Aymeric Mansoux Et Roel Roscam Abbing
    Sept Thèses sur la Fediverse et le devenir du logiciel libre par Aymeric Mansoux et Roel Roscam Abbing Framalang met à votre disposition la traduction de l’ouvrage en anglais Seven Theses on the Fediverse and the becoming of FLOSS Licence Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International. Traduction Framalang : Claire, dodosan, goofy, jums, Macrico, Mannik, mo, roptat, tykayn, wisi_eu À la rencontre de la Fédiverse Ces dernières années, dans un contexte de critiques constantes et de lassitude généralisée associées aux plates-formes de médias sociaux commerciaux 1, le désir de construire des alternatives s’est renforcé. Cela s’est traduit par une grande variété de projets animés par divers objectifs. Les projets en question ont mis en avant leurs différences avec les médias sociaux des grandes plates-formes, que ce soit par leur éthique, leur structure, les technologies qui les sous-tendent, leurs fonctionnalités, l’accès au code source ou encore les communautés construites autour d’intérêts spécifiques qu’ils cherchent à soutenir. Bien que diverses, ces plates-formes tendent vers un objectif commun : remettre clairement en question l’asservissement à une plate-forme unique dans le paysage des médias sociaux dominants. Par conséquent, ces projets nécessitent différents niveaux de décentralisation et d’interopérabilité en termes d’architecture des réseaux et de circulation de données. Ces plates-formes sont regroupées sous le terme de « Fédiverse », un mot-valise composé de « Fédération » et « univers ». La fédération est un concept qui vient de la théorie politique par lequel divers acteurs qui se constituent en réseau décident de coopérer tous ensemble.
    [Show full text]
  • 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.
    [Show full text]
  • La Fée Diverse Déploie Ses Ailes
    La Fée diverse déploie ses ailes Il n’est pas si fréquent que l’équipe Framalang traduise un article depuis la langue italienne, mais la récapitulation bien documentée de Cagizero était une bonne occasion de faire le point sur l’expansion de la Fediverse, un phénomène dont nous nous réjouissons et que nous souhaitons voir gagner plus d’amplitude encore, tant mieux si l’article ci-dessous est très lacunaire dans un an ! Article original : Mastodon, il Fediverso ed il futuro decentrato delle reti sociali Traduction Framalang : alainmi, Ezelty, goofy, MO Mastodon, la Fediverse et l’avenir des réseaux décentralisés par Cagizero Peu de temps après une première vue d’ensemble de Mastodon il est déjà possible d’ajouter quelques observations nouvelles. Tout d’abord, il faut noter que plusieurs personnes familières de l’usage des principaux médias sociaux commerciaux (Facebook, Twitter, Instagram…) sont d’abord désorientées par les concepts de « décentralisation » et de « réseau fédéré ». En effet, l’idée des médias sociaux qui est répandue et bien ancrée dans les esprits est celle d’un lieu unique, indifférencié, monolithique, avec des règles et des mécanismes strictement identiques pour tous. Essentiellement, le fait même de pouvoir concevoir un univers d’instances séparées et indépendantes représente pour beaucoup de gens un changement de paradigme qui n’est pas immédiatement compréhensible. Dans un article précédent où était décrit le média social Mastodon, le concept d’instance fédérée était comparé à un réseau de clubs ou cercles privés associés entre eux. Certains aspects exposés dans l’article précédent demandent peut-être quelques éclaircissements supplémentaires pour celles et ceux qui abordent tout juste le concept de réseau fédéré.
    [Show full text]
  • Graph Types and Language Interoperation
    March 2019 W3C workshop in Berlin on graph data management standards Graph types and Language interoperation Peter Furniss, Alastair Green, Hannes Voigt Neo4j Query Languages Standards and Research Team 11 January 2019 We are actively involved in the openCypher community, SQL/PGQ standards process and the ISO GQL (Graph Query Language) initiative. In these venues we in Neo4j are working towards the goals of a single industry-standard graph query language (GQL) for the property graph data model. We ​ ​ feel it is important that GQL should inter-operate well with other languages (for property graphs, and for other data models). Hannes is a co-author of the SIGMOD 2017 G-CORE paper and a co-author of the ​ ​ recent book Querying Graphs (Synthesis Lectures on Data Management). ​ ​ Alastair heads the Neo4j query languages team, is the author of the The GQL ​ Manifesto and of Working towards a New Work Item for GQL, to complement SQL ​ ​ PGQ. ​ Peter has worked on the design and early implementations of property graph typing in the Cypher for Apache Spark project. He is a former editor of OSI/TP and OASIS ​ ​ Business Transaction Protocol standards. Along with Hannes and Alastair, Peter has centrally contributed to proposals for SQL/PGQ Property Graph Schema. ​ We would like to contribute to or help lead discussion on two linked topics. ​ ​ Property Graph Types The information content of a classic Chen Entity-Relationship model, in combination with “mixin” multiple inheritance of structured data types, allows a very concise and flexible expression of the type of a property graph. A named (catalogued) graph type is composed of node and edge types.
    [Show full text]