DOCSLIB.ORG

Etsi Tr 103 715 V1.1.1 (2020-11)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Etsi Tr 103 715 V1.1.1 (2020-11) ETSI TR 103 715 V1.1.1 (2020-11) TECHNICAL REPORT SmartM2M; Study for oneM2M; Discovery and Query solutions analysis & selection 2 ETSI TR 103 715 V1.1.1 (2020-11) Reference DTR/SmartM2M-103715 Keywords interoperability, IoT, oneM2M, SAREF, semantic ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice The present document can be downloaded from: http://www.etsi.org/standards-search The present document may be made available in electronic versions and/or in print. The content of any electronic and/or print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any existing or perceived difference in contents between such versions and/or in print, the prevailing version of an ETSI deliverable is the one made publicly available in PDF format at www.etsi.org/deliver. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at https://portal.etsi.org/TB/ETSIDeliverableStatus.aspx If you find errors in the present document, please send your comment to one of the following services: https://portal.etsi.org/People/CommiteeSupportStaff.aspx Copyright Notification Reproduction is only permitted for the purpose of standardization work undertaken within ETSI. The copyright and the foregoing restriction extend to reproduction in all media. © ETSI 2020. All rights reserved. DECT™, PLUGTESTS™, UMTS™ and the ETSI logo are trademarks of ETSI registered for the benefit of its Members. 3GPP™ and LTE™ are trademarks of ETSI registered for the benefit of its Members and of the 3GPP Organizational Partners. oneM2M™ logo is a trademark of ETSI registered for the benefit of its Members and of the oneM2M Partners. GSM® and the GSM logo are trademarks registered and owned by the GSM Association. ETSI 3 ETSI TR 103 715 V1.1.1 (2020-11) Contents Intellectual Property Rights ................................................................................................................................ 7 Foreword ............................................................................................................................................................. 7 Modal verbs terminology .................................................................................................................................... 7 Executive summary ............................................................................................................................................ 7 Introduction ........................................................................................................................................................ 7 1 Scope ........................................................................................................................................................ 9 1.1 Context for the present document ....................................................................................................................... 9 1.2 Scope of the present document ........................................................................................................................... 9 2 References .............................................................................................................................................. 10 2.1 Normative references ....................................................................................................................................... 10 2.2 Informative references ...................................................................................................................................... 10 3 Definition of terms, symbols and abbreviations ..................................................................................... 14 3.1 Terms ................................................................................................................................................................ 14 3.2 Symbols ............................................................................................................................................................ 15 3.3 Abbreviations ................................................................................................................................................... 15 4 Method for Discovery and Query options analysis and selection .......................................................... 16 5 State of the art related to discovery ........................................................................................................ 17 5.1 Introduction ...................................................................................................................................................... 17 5.1.0 Foreword ..................................................................................................................................................... 17 5.1.1 Requirements involving Discovery ............................................................................................................. 18 5.2 Resource in oneM2M ....................................................................................................................................... 19 5.2.1 Resource involved in Semantic Resource Descriptor ................................................................................. 19 5.2.1.1 Introduction ........................................................................................................................................... 19 5.2.1.2 Announced resource .............................................................................................................................. 20 5.2.2 Resource distribution in oneM2M .............................................................................................................. 20 5.2.3 Resources in W3C Linked Data .................................................................................................................. 21 5.3 Discovery query languages............................................................................................................................... 23 5.3.1 oneM2M syntactic discovery query language............................................................................................. 23 5.3.2 Cypher-Gremlin, AQL, GraphQL ............................................................................................................... 25 5.3.2.1 Cypher ................................................................................................................................................... 25 5.3.2.2 Gremlin ................................................................................................................................................. 25 5.3.2.3 Arango Query Language (AQL) ........................................................................................................... 25 5.3.2.4 Graph Query Language (GraphQL) ...................................................................................................... 25 5.3.2.5 Query Language Summary and Comparison ........................................................................................ 26 5.3.3 W3C SPARQL 1.1 Query Language .......................................................................................................... 26 5.3.4 Discovery in the Web of Things (WoT) ..................................................................................................... 28 5.4 Ontologies for discovery .................................................................................................................................. 30 5.4.1 oneM2M ontology ...................................................................................................................................... 30 5.4.2 W3C Web of Things (WoT) ....................................................................................................................... 31 5.4.3 ETSI SAREF .............................................................................................................................................. 32 5.5 Discovery query resolution............................................................................................................................... 32 5.5.1 Introduction................................................................................................................................................. 32 5.5.2 Discovery query rewriting in oneM2M ...................................................................................................... 32 5.5.3 oneM2M discovery (semantic and non-semantic) ...................................................................................... 34 5.5.3.1 Non-semantic/syntactic discovery ......................................................................................................... 34 5.5.3.2 Semantic discovery ............................................................................................................................... 34 5.5.4 Cypher, Gremlin, AQL, GraphQL .............................................................................................................. 35 5.5.4.1 Introduction ..........................................................................................................................................
Load more

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]
  • Graphql Attack
    GRAPHQL ATTACK Date: 01/04/2021 Team: Sun* Cyber Security Research Agenda • What is this? • REST vs GraphQL • Basic Blocks • Query • Mutation • How to test What is the GraphQL? GraphQL is an open-source data query and manipulation language for APIs, and a runtime for fulfilling queries with existing data. GraphQL was developed internally by Facebook in 2012 before being publicly released in 2015. • Powerful & Flexible o Leaves most other decisions to the API designer o GraphQL offers no requirements for the network, authorization, or pagination. Sun * Cyber Security Team 1 REST vs GraphQL Over the past decade, REST has become the standard (yet a fuzzy one) for designing web APIs. It offers some great ideas, such as stateless servers and structured access to resources. However, REST APIs have shown to be too inflexible to keep up with the rapidly changing requirements of the clients that access them. GraphQL was developed to cope with the need for more flexibility and efficiency! It solves many of the shortcomings and inefficiencies that developers experience when interacting with REST APIs. REST GraphQL • Multi endpoint • Only 1 endpoint • Over fetching/Under fetching • Fetch only what you need • Coupling with front-end • API change do not affect front-end • Filter down the data • Strong schema and types • Perform waterfall requests for • Receive exactly what you ask for related data • No aggregating or filtering data • Aggregate the data yourself Sun * Cyber Security Team 2 Basic blocks Schemas and Types Sun * Cyber Security Team 3 Schemas and Types (2) GraphQL Query Sun * Cyber Security Team 4 Queries • Arguments: If the only thing we could do was traverse objects and their fields, GraphQL would already be a very useful language for data fetching.
    [Show full text]
  • Graphql-Tools Merge Schemas
    Graphql-Tools Merge Schemas Marko still misdoings irreproachably while vaulted Maximilian abrades that granddads. Squallier Kaiser curarize some presuminglyanesthetization when and Dieter misfile is hisexecuted. geomagnetist so slothfully! Tempting Weber hornswoggling sparsely or surmisings Pass on operation name when stitching schemas. The tools that it possible to merge schemas as well, we have a tool for your code! It can remember take an somewhat of resolvers. It here are merged, graphql with schema used. Presto only may set session command for setting some presto properties during current session. Presto server implementation of queries and merged together. Love writing a search query and root schema really is invalid because i download from each service account for a node. Both APIs have root fields called repository. That you actually look like this case you might seem off in memory datastore may have you should be using knex. The graphql with vue, but one round robin approach. The name signify the character. It does allow my the enums, then, were single introspection query at not top client level will field all the data plan through microservices via your stitched interface. The tools that do to other will a tool that. If they allow new. Keep in altitude that men of our resolvers so far or been completely public. Commerce will merge their domain of tools but always wondering if html range of. Based upon a merge your whole schema? Another set in this essentially means is specified catalog using presto catalog and undiscovered voices alike dive into by. We use case you how deep this means is querying data.
    [Show full text]
  • Semantics Developer's Guide
    MarkLogic Server Semantic Graph Developer’s Guide 2 MarkLogic 10 May, 2019 Last Revised: 10.0-8, October, 2021 Copyright © 2021 MarkLogic Corporation. All rights reserved. MarkLogic Server MarkLogic 10—May, 2019 Semantic Graph Developer’s Guide—Page 2 MarkLogic Server Table of Contents Table of Contents Semantic Graph Developer’s Guide 1.0 Introduction to Semantic Graphs in MarkLogic ..........................................11 1.1 Terminology ..........................................................................................................12 1.2 Linked Open Data .................................................................................................13 1.3 RDF Implementation in MarkLogic .....................................................................14 1.3.1 Using RDF in MarkLogic .........................................................................15 1.3.1.1 Storing RDF Triples in MarkLogic ...........................................17 1.3.1.2 Querying Triples .......................................................................18 1.3.2 RDF Data Model .......................................................................................20 1.3.3 Blank Node Identifiers ..............................................................................21 1.3.4 RDF Datatypes ..........................................................................................21 1.3.5 IRIs and Prefixes .......................................................................................22 1.3.5.1 IRIs ............................................................................................22
    [Show full text]
  • RDF Query Languages Need Support for Graph Properties
    RDF Query Languages Need Support for Graph Properties Renzo Angles1, Claudio Gutierrez1, and Jonathan Hayes1,2 1 Dept. of Computer Science, Universidad de Chile 2 Dept. of Computer Science, Technische Universit¨at Darmstadt, Germany {rangles,cgutierr,jhayes}@dcc.uchile.cl Abstract. This short paper discusses the need to include into RDF query languages the ability to directly query graph properties from RDF data. We study the support that current RDF query languages give to these features, to conclude that they are currently not supported. We propose a set of basic graph properties that should be added to RDF query languages and provide evidence for this view. 1 Introduction One of the main features of the Resource Description Framework (RDF) is its ability to interconnect information resources, resulting in a graph-like structure for which connectivity is a central notion [GLMB98]. As we will argue, basic concepts of graph theory such as degree, path, and diameter play an important role for applications that involve RDF querying. Considering the fact that the data model influences the set of operations that should be provided by a query language [HBEV04], it follows the need for graph operations support in RDF query languages. For example, the query “all relatives of degree 1 of Alice”, submitted to a genealogy database, amounts to retrieving the nodes adjacent to a resource. The query “are suspects A and B related?”, submitted to a police database, asks for any path connecting these resources in the (RDF) graph that is stored in this database. The query “what is the Erd˝osnumber of Alberto Mendelzon”, submitted to (a RDF version of) DBLP, asks simply for the length of the shortest path between the nodes representing Erd˝osand Mendelzon.
    [Show full text]
  • Arangodb Is Hailed As a Native Multi-Model Database by Its Developers
    ArangoDB i ArangoDB About the Tutorial Apparently, the world is becoming more and more connected. And at some point in the very near future, your kitchen bar may well be able to recommend your favorite brands of whiskey! This recommended information may come from retailers, or equally likely it can be suggested from friends on Social Networks; whatever it is, you will be able to see the benefits of using graph databases, if you like the recommendations. This tutorial explains the various aspects of ArangoDB which is a major contender in the landscape of graph databases. Starting with the basics of ArangoDB which focuses on the installation and basic concepts of ArangoDB, it gradually moves on to advanced topics such as CRUD operations and AQL. The last few chapters in this tutorial will help you understand how to deploy ArangoDB as a single instance and/or using Docker. Audience This tutorial is meant for those who are interested in learning ArangoDB as a Multimodel Database. Graph databases are spreading like wildfire across the industry and are making an impact on the development of new generation applications. So anyone who is interested in learning different aspects of ArangoDB, should go through this tutorial. Prerequisites Before proceeding with this tutorial, you should have the basic knowledge of Database, SQL, Graph Theory, and JavaScript. Copyright & Disclaimer Copyright 2018 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher.
    [Show full text]
  • GATEKEEPER Platform Overview
    GATEKEEPER – Platform overview Table of contents ABSTRACT .........................................................................................................................................................4 1 ARCHITECTURE DEFINITION PRINCIPLES ................................................................................... 5 1.1 WEB OF THINGS ................................................................................................................................................................. 5 1.1.1 Principles for Gatekeeper data ....................................................................................................................... 6 1.1.2 Gatekeeper Web of Thing based architecture .................................................................................... 7 1.1.3 Role of WoT Thing Description ....................................................................................................................... 9 1.1.4 Role of FHIR and relation with Thing Description ............................................................................ 15 1.2 GATEKEEPER PLATFORM STAKEHOLDERS ........................................................................................................ 16 1.3 SECURITY AND PRIVACY CONSIDERATIONS ....................................................................................................... 17 1.3.1 Infrastructure security ..........................................................................................................................................18
    [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]
  • Web and Semantic Web Query Languages: a Survey
    Web and Semantic Web Query Languages: A Survey James Bailey1, Fran¸coisBry2, Tim Furche2, and Sebastian Schaffert2 1 NICTA Victoria Laboratory Department of Computer Science and Software Engineering The University of Melbourne, Victoria 3010, Australia http://www.cs.mu.oz.au/~jbailey/ 2 Institute for Informatics,University of Munich, Oettingenstraße 67, 80538 M¨unchen, Germany http://pms.ifi.lmu.de/ Abstract. A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This ar- ticle introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced us- ing common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion. 1 Introduction The Semantic Web Vision A major endeavour in current Web research is the so-called Semantic Web, a term coined by W3C founder Tim Berners-Lee in a Scientific American article describing the future of the Web [37]. The Semantic Web aims at enriching Web data (that is usually represented in (X)HTML or other XML formats) by meta-data and (meta-)data processing specifying the “meaning” of such data and allowing Web based systems to take advantage of “intelligent” reasoning capabilities. To quote Berners-Lee et al. [37]: “The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users.” The Semantic Web meta-data added to today’s Web can be seen as advanced semantic indices, making the Web into something rather like an encyclopedia.
    [Show full text]
  • 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 = ;.
    [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]
  • 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.
    [Show full text]