Converting Relational to Graph Databases

Total Page:16

File Type:pdf, Size:1020Kb

Converting Relational to Graph Databases d a e f t f ed ili t a a a i t li l e fi i d f f a f a Riccardo c Roberto De Antonio i c p i o p Torlone Virgilio Maccioni t o t a a a u u u t t t h h h o o o r r r Converting Relational to Graph Databases g in 23 June 2013 ed ce ro n p whe New York, USA In wh where ere GRADES 2013 affiliated workshop Relational Database Migration select * from T SQLwhere T.A1 = v1 GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 R2G: Features ● Data migration ● Query translation ● Automatic non-naïve approach ● Try to minimize the memory accesses GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Graph Modeling of Relational DB ● Full Schema Paths: FR.fuser → US.uid → US.uname FR.fuser → FR.fblog → BG.bid → BG.bname FR.fuser → FR.fblog → BG.bid → BG.admin → US.uid → US.uname ... GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Basic Concepts • Joinable tuples t1 ∈ R1 and t2 ∈ R2: ● there is a foreign key constraint between R1.A and R2.B and t1[A] = t2[B]. • Unifiability of data values t1[A] and t2[B]: ● (i) t1=t2 and both A and B do not belong to a multi- attribute key; ● (ii) t1 and t2 are joinable and A belongs to a multi- attribute key; ● (iii) t1 and t2 are joinable, A and B do not belong to a multi-attribute key and there is no other tuple t3 that is joinable with t2. GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Data Migration (1) ● Identify unifiable data exploiting schema and constraints FR.fuser US.uid US.uname n1 FR.fuser : u01 GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Data Migration (2) ● Identify unifiable data exploiting schema and constraints FR.fuser US.uid US.uname n1 FR.fuser : u01 US.uid : u01 GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Data Migration (3) ● Identify unifiable data exploiting schema and constraints FR.fuser US.uid US.uname n1 FR.fuser : u01 US.uid : u01 US.uname : Date GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Data Migration (4) ● Identify unifiable data exploiting schema and constraints GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Query Translation XQuery Gremlin GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Experimental Results GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Conclusion • Automatic data mapping • Conjunctive query translation into a path traversal query • Independent from a specific GDBMS • Efficient exploitation of Graph Database Features GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Future Work • Consider frequent queries to migrate data • Consider wider range of queries than CQ • Improve compactness of the graph database GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013 Thanks For The Attention ... demo presentation during the following interactive session! GRADES 2013 Converting Relational to Graph Databases New York, 23-06-2013.
Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [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]
  • 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
    [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]
  • Full-Graph-Limited-Mvn-Deps.Pdf
    org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version}
    [Show full text]
  • An Efficient and Scalable Platform for Java Source Code Analysis Using Overlaid Graph Representations
    Received March 10, 2020, accepted April 7, 2020, date of publication April 13, 2020, date of current version April 29, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2987631 An Efficient and Scalable Platform for Java Source Code Analysis Using Overlaid Graph Representations OSCAR RODRIGUEZ-PRIETO1, ALAN MYCROFT2, AND FRANCISCO ORTIN 1,3 1Department of Computer Science, University of Oviedo, 33007 Oviedo, Spain 2Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, U.K. 3Department of Computer Science, Cork Institute of Technology, Cork 021, T12 P928 Ireland Corresponding author: Francisco Ortin ([email protected]) This work was supported in part by the Spanish Department of Science, Innovation and Universities under Project RTI2018-099235-B-I00. The work of Oscar Rodriguez-Prieto and Francisco Ortin was supported by the University of Oviedo through its support to official research groups under Grant GR-2011-0040. ABSTRACT Although source code programs are commonly written as textual information, they enclose syntactic and semantic information that is usually represented as graphs. This information is used for many different purposes, such as static program analysis, advanced code search, coding guideline checking, software metrics computation, and extraction of semantic and syntactic information to create predictive models. Most of the existing systems that provide these kinds of services are designed ad hoc for the particular purpose they are aimed at. For this reason, we created ProgQuery, a platform to allow users to write their own Java program analyses in a declarative fashion, using graph representations. We modify the Java compiler to compute seven syntactic and semantic representations, and store them in a Neo4j graph database.
    [Show full text]
  • Neo4j.Com Daniel Howard – Senior Researcher 111 E 5Th Avenue, San Mateo, CA 94401, USA Tel: +1 855 636 4532 Email: [email protected] Neo4j
    InPhilip Howard – ResearchBrief Director, Information Management www.neo4j.com Daniel Howard – Senior Researcher 111 E 5th Avenue, San Mateo, CA 94401, USA Tel: +1 855 636 4532 Email: [email protected] Neo4j The company CREATIVITY SCALE Neo4j Inc (previously Neo Technologies) was founded in 2000 in Sweden although it is now based in the United States. Outside of these two countries the company also has offices in the UK, Germany, France and Japan. The company’s eponymous product is available in both Community and Enterprise Editions and is available both on-premises and via Google, Amazon and Microsoft Azure cloud platforms. The company has a significant partner base, as illustrated in Figure 1. Notable amongst these are Pitney Bowes, which embeds Neo4j within its EXECUTION TECHNOLOGY MDM offering. It is also worth mentioning Structr. org, which is an open source graph-based (Neo4j) The image in this Mutable Quadrant is derived from 13 high level metrics, the more the image covers a section the better. low code development and runtime environment for Execution metrics relate to the company, Technology to the mobile and web applications. product, Creativity to both technical and business innovation and Scale covers the potential business and market impact. supports immediate consistency. Most users (see below) employ Cypher or OpenCypher (the open source version), which is the declarative language developed by Neo4j. It is notable that SAP, Redis, Memgraph and others have adopted OpenCypher and it is also being used within several open source projects including Cypher for Apache Spark, and Cypher for Gremlin, as well as in research projects like InGraph for streaming queries.
    [Show full text]
  • Technology Overview
    Big Data Technology Overview Term Description See Also Big Data - the 5 Vs Everyone Must Volume, velocity and variety. And some expand the definition further to include veracity 3 Vs Know and value as well. 5 Vs of Big Data From Wikipedia, “Agile software development is a group of software development methods based on iterative and incremental development, where requirements and solutions evolve through collaboration between self-organizing, cross-functional teams. Agile The Agile Manifesto It promotes adaptive planning, evolutionary development and delivery, a time-boxed iterative approach, and encourages rapid and flexible response to change. It is a conceptual framework that promotes foreseen tight iterations throughout the development cycle.” A data serialization system. From Wikepedia, Avro Apache Avro “It is a remote procedure call and serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.” BigInsights Enterprise Edition provides a spreadsheet-like data analysis tool to help Big Insights IBM Infosphere Biginsights organizations store, manage, and analyze big data. A scalable multi-master database with no single points of failure. Cassandra Apache Cassandra It provides scalability and high availability without compromising performance. Cloudera Inc. is an American-based software company that provides Apache Hadoop- Cloudera Cloudera based software, support and services, and training to business customers. Wikipedia - Data Science Data science The study of the generalizable extraction of knowledge from data IBM - Data Scientist Coursera Big Data Technology Overview Term Description See Also Distributed system developed at Google for interactively querying large datasets. Dremel Dremel It empowers business analysts and makes it easy for business users to access the data Google Research rather than having to rely on data engineers.
    [Show full text]
  • Formalizing Gremlin Pattern Matching Traversals in an Integrated Graph Algebra
    Formalizing Gremlin Pattern Matching Traversals in an Integrated Graph Algebra 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 Algebra 1 Introduction Upon observing the evolution of information technology, we can observe a trend from data models and knowledge representation techniques be- ing tightly bound to the capabilities of the underlying hardware towards more intuitive and natural methods resembling human-style information processing.
    [Show full text]
  • Identifying and Evaluating Software Architecture Erosion
    SCUOLA DI DOTTORATO UNIVERSITÀ DEGLI STUDI DI MILANO-BICOCCA Department of Informatics Systems and Communication PhD program Computer Science Cycle XXX IDENTIFYING AND EVALUATING SOFTWARE ARCHITECTURE EROSION Surname Roveda Name Riccardo Registration number 723299 Tutor: Prof. Alberto Ottavio Leporati Supervisor: Prof. Francesca Arcelli Fontana Coordinator: Prof. Stefania Bandini ACADEMIC YEAR 2016/2017 CONTENTS 1 introduction1 1.1 Main contributions of the thesis . 2 1.2 Research Questions . 4 1.3 Publications . 5 1.3.1 Published papers . 5 1.3.2 Submitted papers . 6 1.3.3 To be submitted papers . 6 1.3.4 Published papers not strictly related to the thesis . 6 2 related work8 2.1 Architectural smells definitions . 8 2.2 Architectural smells detection . 19 2.3 Code smells and architectural smells correlations . 20 2.3.1 Code smells correlations . 20 2.3.2 Code smells and architectural smell correlations . 21 2.4 Studies on software quality prediction and evolution . 22 2.4.1 Studies on software quality prediction . 22 2.4.2 Studies on software quality evolution . 23 2.5 Technical Debt Indexes . 23 2.5.1 CAST . 24 2.5.2 inFusion . 25 2.5.3 Sonargraph . 26 2.5.4 SonarQube . 26 2.5.5 Structure101 .......................... 27 2.6 Architectural smell refactoring . 27 3 experience reports on the detection of architectu- ral issues through different tools 29 3.1 Tools for evaluating code and architectural issues . 29 3.2 Tool support for evaluating architectural debt . 31 3.2.1 Evaluating the results inspection of the tools . 32 3.2.2 Evaluating the extracted data by the tools .
    [Show full text]