A Natural Language Interface for Querying Graph Databases Christina
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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. -
Graph Database for Collaborative Communities Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui
Graph Database for Collaborative Communities Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui To cite this version: Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui. Graph Database for Collaborative Communities. Community-Built Databases, Springer, pp.205-234, 2011. hal-00708222 HAL Id: hal-00708222 https://hal.archives-ouvertes.fr/hal-00708222 Submitted on 14 Jun 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Graph Database For collaborative Communities 1, 2 1 Rania Soussi , Marie-Aude Aufaure , Hajer Baazaoui2 1Ecole Centrale Paris, Applied Mathematics & Systems Laboratory (MAS), SAP Business Objects Academic Chair in Business Intelligence 2Riadi-GDL Laboratory, ENSI – Manouba University, Tunis Abstract Data manipulated in an enterprise context are structured data as well as un- structured data such as emails, documents, social networks, etc. Graphs are a natural way of representing and modeling such data in a unified manner (Structured, semi-structured and unstructured ones). The main advantage of such a structure relies in the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of handling data appearing. -
Property Graph Vs RDF Triple Store: a Comparison on Glycan Substructure Search
RESEARCH ARTICLE Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search Davide Alocci1,2, Julien Mariethoz1, Oliver Horlacher1,2, Jerven T. Bolleman3, Matthew P. Campbell4, Frederique Lisacek1,2* 1 Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 2 Computer Science Department, University of Geneva, Geneva, 1227, Switzerland, 3 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 4 Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia * [email protected] Abstract Resource description framework (RDF) and Property Graph databases are emerging tech- nologies that are used for storing graph-structured data. We compare these technologies OPEN ACCESS through a molecular biology use case: glycan substructure search. Glycans are branched Citation: Alocci D, Mariethoz J, Horlacher O, tree-like molecules composed of building blocks linked together by chemical bonds. The Bolleman JT, Campbell MP, Lisacek F (2015) molecular structure of a glycan can be encoded into a direct acyclic graph where each node Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. PLoS ONE 10(12): represents a building block and each edge serves as a chemical linkage between two build- e0144578. doi:10.1371/journal.pone.0144578 ing blocks. In this context, Graph databases are possible software solutions for storing gly- Editor: Manuela Helmer-Citterich, University of can structures and Graph query languages, such as SPARQL and Cypher, can be used to Rome Tor Vergata, ITALY perform a substructure search. Glycan substructure searching is an important feature for Received: July 16, 2015 querying structure and experimental glycan databases and retrieving biologically meaning- ful data. -
Application of Graph Databases for Static Code Analysis of Web-Applications
Application of Graph Databases for Static Code Analysis of Web-Applications Daniil Sadyrin [0000-0001-5002-3639], Andrey Dergachev [0000-0002-1754-7120], Ivan Loginov [0000-0002-6254-6098], Iurii Korenkov [0000-0002-8948-2776], and Aglaya Ilina [0000-0003-1866-7914] ITMO University, Kronverkskiy prospekt, 49, St. Petersburg, 197101, Russia [email protected], [email protected], [email protected], [email protected], [email protected] Abstract. Graph databases offer a very flexible data model. We present the approach of static code analysis using graph databases. The main stage of the analysis algorithm is the construction of ASG (Abstract Source Graph), which represents relationships between AST (Abstract Syntax Tree) nodes. The ASG is saved to a graph database (like Neo4j) and queries to the database are made to get code properties for analysis. The approach is applied to detect and exploit Object Injection vulnerability in PHP web-applications. This vulnerability occurs when unsanitized user data enters PHP unserialize function. Successful exploitation of this vulnerability means building of “object chain”: a nested object, in the process of deserializing of it, a sequence of methods is being called leading to dangerous function call. In time of deserializing, some “magic” PHP methods (__wakeup or __destruct) are called on the object. To create the “object chain”, it’s necessary to analyze methods of classes declared in web-application, and find sequence of methods called from “magic” methods. The main idea of author’s approach is to save relationships between methods and functions in graph database and use queries to the database on Cypher language to find appropriate method calls. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
A Comparison Between Cypher and Conjunctive Queries
A comparison between Cypher and conjunctive queries Jaime Castro and Adri´anSoto Pontificia Universidad Cat´olicade Chile, Santiago, Chile 1 Introduction Graph databases are one of the most popular type of NoSQL databases [2]. Those databases are specially useful to store data with many relations among the entities, like social networks, provenance datasets or any kind of linked data. One way to store graphs is property graphs, which is a type of graph where nodes and edges can have attributes [1]. A popular graph database system is Neo4j [4]. Neo4j is used in production by many companies, like Cisco, Walmart and Ebay [4]. The data model is like a property graph but with small differences. Its query language is Cypher. The expressive power is not known because currently Cypher does not have a for- mal definition. Although there is not a standard for querying graph databases, this paper proposes a comparison between Cypher, because of the popularity of Neo4j, and conjunctive queries which are arguably the most popular language used for pattern matching. 1.1 Preliminaries Graph Databases. Graphs can be used to store data. The nodes represent objects in a domain of interest and edges represent relationships between these objects. We assume familiarity with property graphs [1]. Neo4j graphs are stored as property graphs, but the nodes can have zero or more labels, and edges have exactly one type (and not a label). Now we present the model we work with. A Neo4j graph G is a tuple (V; E; ρ, Λ, τ; Σ) where: 1. V is a finite set of nodes, and E is a finite set of edges such that V \ E = ;. -
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 -
Handling Scalable Approximate Queries Over Nosql Graph Databases: Cypherf and the Fuzzy4s Framework
Castelltort, A. , & Martin, T. (2017). Handling scalable approximate queries over NoSQL graph databases: Cypherf and the Fuzzy4S framework. Fuzzy Sets and Systems. https://doi.org/10.1016/j.fss.2017.08.002 Peer reviewed version License (if available): CC BY-NC-ND Link to published version (if available): 10.1016/j.fss.2017.08.002 Link to publication record in Explore Bristol Research PDF-document This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0165011417303093?via%3Dihub . Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms *Manuscript 1 2 3 4 5 6 7 8 9 Handling Scalable Approximate Queries over NoSQL 10 Graph Databases: Cypherf and the Fuzzy4S Framework 11 12 13 Arnaud Castelltort1, Trevor Martin2 14 1 LIRMM, CNRS-University of Montpellier, France 15 2Department of Engineering Mathematics, University of Bristol, UK 16 17 18 19 20 21 Abstract 22 23 NoSQL databases are currently often considered for Big Data solutions as they 24 25 offer efficient solutions for volume and velocity issues and can manage some of 26 complex data (e.g., documents, graphs). Fuzzy approaches are yet often not 27 28 efficient on such frameworks. Thus this article introduces a novel approach to 29 30 define and run approximate queries over NoSQL graph databases using Scala 31 by proposing the Fuzzy4S framework and the Cypherf fuzzy declarative query 32 33 language. -
GRAPH DATABASE THEORY Comparing Graph and Relational Data Models
GRAPH DATABASE THEORY Comparing Graph and Relational Data Models Sridhar Ramachandran LambdaZen © 2015 Contents Introduction .................................................................................................................................................. 3 Relational Data Model .............................................................................................................................. 3 Graph databases ....................................................................................................................................... 3 Graph Schemas ............................................................................................................................................. 4 Selecting vertex labels .............................................................................................................................. 4 Examples of label selection ....................................................................................................................... 4 Drawing a graph schema ........................................................................................................................... 6 Summary ................................................................................................................................................... 7 Converting ER models to graph schemas...................................................................................................... 9 ER models and diagrams .......................................................................................................................... -
Weaver: a High-Performance, Transactional Graph Database Based on Refinable Timestamps
Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps Ayush Dubey Greg D. Hill Robert Escriva Cornell University Stanford University Cornell University Emin Gun¨ Sirer Cornell University ABSTRACT may erroneously conclude that n7 is reachable from n1, even Graph databases have become a common infrastructure com- though no such path ever existed. ponent. Yet existing systems either operate on offline snap- Providing strongly consistent queries is particularly chal- shots, provide weak consistency guarantees, or use expensive lenging for graph databases because of the unique charac- concurrency control techniques that limit performance. teristics of typical graph queries. Queries such as traversals In this paper, we introduce a new distributed graph data- often read a large portion of the graph, and consequently base, called Weaver, which enables efficient, transactional take a long time to execute. For instance, the average degree graph analyses as well as strictly serializable ACID transac- of separation in the Facebook social network is 3.5 [8], which tions on dynamic graphs. The key insight that allows Weaver implies that a breadth-first traversal that starts at a random to combine strict serializability with horizontal scalability vertex and traverses 4 hops will likely read all 1.59 billion and high performance is a novel request ordering mecha- users. On the other hand, typical key-value and relational nism called refinable timestamps. This technique couples queries are much smaller; the NewOrder transaction in the coarse-grained vector timestamps with a fine-grained timeline TPC-C benchmark [7], which comprises 45% of the frequency oracle to pay the overhead of strong consistency only when distribution, consists of 26 reads and writes on average [21]. -
Document Vs Graph Database
Document Vs Graph Database Is Cornelius tricentennial when Apollo reclothes telescopically? Carbonated Angelo defiling, his mainsheet ridicule paralysed mulishly. Agonistic and farinaceous Jerrold retimed her somniloquists burbles while Heinz rope some nymph tho. Recently due not the variety properties of instance data your database management systems GDBMSs have emerged as real important complement. When and augment to Choose Graph Databases over Relational. Mainly categorized into four types Key-value pair Column-oriented big-based and Document-oriented. Capture store analyze and manage collections of data. Graph databases can he thought through as a subcategory of the document store model in that they store began in documents and don't insist that data drive to a. Neo4J an sturdy and still heavily used graph paper had begun to gain. Since several of bath's data is conduct in relational database format how hot you convert any current journey to save graph database format See a. 5 Graph Databases to Consider ReadWrite. NoSQL databases or non-relational databases can be document based graph databases key-value pairs or wide-column stores NoSQL databases don't. This tape because mercury can arbitrarily decide what should rattle a vertex vs what should adorn a. Graph Databases NOSQL and Neo4j InfoQ. MongoDB and CouchDB are both examples of document stores Graph databases Last is the theater complex non-relational database will It's. An Entity Relationship Diagram commonly used to model data in SQL RDBMS vs. Column-oriented databases and document-oriented databases also belong to gauge family. Nosql Distilled was that NoSQL databases use aggregate data models than. -
The Top 5 Use Cases of Graph Databases Unlocking New Possibilities with Connected Data
The #1 Platform for Connected Data White Paper The Top 5 Use Cases of Graph Databases Unlocking New Possibilities with Connected Data Jim Webber Chief Scientist, Neo Technology & Ian Robinson Senior Engineer, Neo Technology neo4j.com The #1 Platform for Connected Data The Top 5 Use Cases of Graph Databases TABLE OF CONTENTS Introduction 1 The Top 5 Use Cases Fraud Detection 2 of Graph Databases Real-Time Recommendations 4 Unlocking New Possibilities with Connected Data Master Data Jim Webber & Ian Robinson Management 6 Network & IT Introduction Operations 8 “Big data” grows bigger every year, but today’s enterprise leaders don’t only need to manage larger volumes of data, but they critically need to generate insight from their existing data. So Identity & Access how should CIOs and CTOs generate those insights? Management 10 To paraphrase Seth Godin, businesses need to stop merely collecting data points, and start connecting them. In other words, the relationships between data points matter almost more Conclusion 12 than the individual points themselves. In order to leverage those data relationships, your organization needs a database technology that stores relationship information as a first-class entity. That technology is a graph database. Ironically, legacy relational database management systems (RDBMS) are poor at handling “Stop merely collecting relationships between data points. Their tabular data models and rigid schemas make it data points, and start difficult to add new or different kinds of connections. connecting them.” Graphs are the future. Not only do graph databases effectively store the relationships between data points, but they’re also flexible in adding new kinds of relationships oradapting a data model to new business requirements.