Linked Data for Smart Homes: Comparing RDF and Labeled Property Graphs
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Multiple-Aspect Analysis of Semantic Trajectories
Konstantinos Tserpes Chiara Renso Stan Matwin (Eds.) Multiple-Aspect Analysis LNAI 11889 of Semantic Trajectories First International Workshop, MASTER 2019 Held in Conjunction with ECML-PKDD 2019 Würzburg, Germany, September 16, 2019 Proceedings Lecture Notes in Artificial Intelligence 11889 Subseries of Lecture Notes in Computer Science Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany More information about this series at http://www.springer.com/series/1244 Konstantinos Tserpes • Chiara Renso • Stan Matwin (Eds.) Multiple-Aspect Analysis of Semantic Trajectories First International Workshop, MASTER 2019 Held in Conjunction with ECML-PKDD 2019 Würzburg, Germany, September 16, 2019 Proceedings Editors Konstantinos Tserpes Chiara Renso Harokopio University ISTI-CNR Athens, Greece Pisa, Italy Stan Matwin Dalhousie University Halifax, NS, Canada ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-38080-9 ISBN 978-3-030-38081-6 (eBook) https://doi.org/10.1007/978-3-030-38081-6 LNCS Sublibrary: SL7 – Artificial Intelligence © The Editor(s) (if applicable) and The Author(s) 2020. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. -
Graph Databases – Are They Really So New
International Journal of Advances in Science Engineering and Technology, ISSN: 2321-9009 Volume- 4, Issue-4, Oct.-2016 GRAPH DATABASES – ARE THEY REALLY SO NEW 1MIRKO MALEKOVIC, 2KORNELIJE RABUZIN, 3MARTINA SESTAK 1,2,3Faculty of Organization and Informatics Varaždin, University of Zagreb, Croatia E-mail: [email protected], [email protected], [email protected] Abstract- Even though relational databases have been (and still are) the most widely used database solutions for many years, there were other database solutions in use before the era of relational databases. One of those solutions were network databases with the underlying network model, whose characteristics will be presented in detail in this paper. The network model will be compared to the graph data model used by graph databases, the relatively new category of NoSQL databases with a growing share in the database market. The similarities and differences will be shown through the implementation of a simple database using network and graph data model. Keywords- Network data model, graph data model, nodes, relationships, records, sets. I. INTRODUCTION ● Hierarchical databases This type of databases is based on the hierarchical The relational databases were introduced by E. G. data model, whose structure can be represented as a Codd in his research papers in 1970s. In those papers layered tree in which one data table represents the he discussed relational model as the underlying data root of that tree (top layer), and the others represent model for the relational databases, which was based tree branches (nodes) emerging from the root of the on relational algebra and first-order predicate logic. -
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. -
Appendix E: an Overview of the Network Data Model
Elmasri_APPC Page 1 Monday, April 3, 2006 3:40 PM An Overview of the APPENDIXE Network Data Model This appendix provides an overview of the network data model.1 The original network model and language were presented in the CODASYL Data Base Task Group’s 1971 report; hence it is sometimes called the DBTG model. Revised reports in 1978 and 1981 incorporated more recent concepts. In this appendix, rather than concentrating on the details of a particular CODASYL report, we present the general concepts behind net- work-type databases and use the term network model rather than CODASYL model or DBTG model. The original CODASYL/DBTG report used COBOL as the host language. Regardless of the host programming language, the basic database manipulation commands of the net- work model remain the same. Although the network model and the object-oriented data model are both navigational in nature, the data structuring capability of the network model is much more elaborate and allows for explicit insertion/deletion/modification semantic specification. However, it lacks some of the desirable features of the object mod- els that we discussed in Chapter 11, such as inheritance and encapsulation of structure and behavior. 1. The complete chapter on the network data model and about the IDMS system from the second edition of this book is available at http://www.awl.com/cseng/titles/0-8053-1755-4. This appendix is an edited excerpt of that chapter. E–1 Elmasri_APPC Page 2 Monday, April 3, 2006 3:40 PM E–2 Appendix E / An Overview of the Network Data Model E.1 Network Data Modeling Concepts There are two basic data structures in the network model: records and sets. -
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. -
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. -
Studies and Analysis of Popular Database Models
Pramod Singh et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.5, May- 2015, pg. 834-838 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 4, Issue. 5, May 2015, pg.834 – 838 RESEARCH ARTICLE Studies and Analysis of Popular Database Models Dr. P. K. Rai Prof. I/C, BCA & Head, Computer Centre APSU Rewa Pramod Singh Research Scholar M.G.C.G.V Chitrakoot Satna (M.P), Satna, India; Email: [email protected] ABSTRACT: Data is most valuable part of today world because it is used in everyday of life from a single individual to large organizations. To make the selection and maintenance easy and support strong data protection it is stored in a data model. A data model is a repository of subjectively selected and adapted operational data which can successfully answer any, statistical, complex or analytical queries. Nowadays the database is shared by a globally competitive environment where large amounts of data are required to be processed faster and efficient way .data model keep a subject oriented data which gives the support for management's decision making process. This paper discusses in detail four existing database models like Hierarchical, network, relational and object oriented database and does an analysis to understand the advantages and disadvantages of these database models. Keywords: RDB, OODB. Introduction A Database is an important part of any organization because all information is store in the database where all users can easily access this information in well organized manner. -
Evaluating a Graph Query Language for Human-Robot Interaction Data in Smart Environments
Evaluating a Graph Query Language for Human-Robot Interaction Data in Smart Environments Norman K¨oster1, Sebastian Wrede12, and Philipp Cimiano1 1 Cluster of Excellence Center in Cognitive Interactive Technology (CITEC) 2 Research Institute for Cognition and Robotics (CoR-Lab), Bielefeld University, Bielefeld Germany fnkoester,swrede,[email protected], Abstract. Solutions for efficient querying of long-term human-robot in- teraction data require in-depth knowledge of the involved domains and represents a very difficult and error prone task due to the inherent (sys- tem) complexity. Developers require detailed knowledge with respect to the different underlying data schemata, semantic mappings, and most im- portantly the query language used by the storage system (e.g. SPARQL, SQL, or general purpose language interfaces/APIs). While for instance database developers are familiar with technical aspects of query lan- guages, application developers typically lack the specific knowledge to efficiently work with complex database management systems. Addressing this gap, in this paper we describe a model-driven software development based approach to create a long term storage system to be employed in the domain of embodied interaction in smart environments (EISE). The targeted EISE scenario features a smart environment (i.e. smart home) in which multiple agents (a mobile autonomous robot and two virtual agents) interact with humans to support them in daily activities. To support this we created a language using Jetbrains MPS to model the high level EISE domain w.r.t. the occurring interactions as a graph com- posed of nodes and their according relationships. Further, we reused and improved capabilities of a previously created language to represent the graph query language Cypher. -
Flexible Querying of Graph-Structured Data
Flexible Querying of Graph-Structured Data Petra Selmer July 2017 A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science and Information Systems Birkbeck, University of London Declaration This thesis is the result of my own work, except where explicitly acknowledged in the text. Petra Selmer July 23, 2017 Abstract Given the heterogeneity of complex graph data on the web, such as RDF linked data, it is likely that a user wishing to query such data will lack full knowledge of the structure of the data and of its irregularities. Hence, providing flexible querying capabilities that assist users in formulating their information-seeking requirements is highly desirable. The query language we adopt in this thesis comprises conjunctions of regular path queries, thus encompassing recent extensions to SPARQL to allow for querying paths in graphs using regular expressions (SPARQL 1.1). To this language we add three operators: APPROX, supporting standard notions of query approximation based on edit distance; RELAX, which performs query relaxation based on RDFS inference rules; and FLEX, which simultaneously applies both approximation and relaxation to a query conjunct, providing even greater flexibility for users. We undertake a detailed theoretical investigation of query approximation, query relaxation, and their combination. We show how these notions can be integrated into a single theoretical framework and we provide incremental evaluation algorithms that run in polynomial time in the size of the query and the data | provided the queries are acyclic and contain a fixed number of head variables | returning answers in ranked order of their `distance' from the original query. -
The Network Model
Unit 5 The Network Model 5.1 The Network Model 5.2 IDMS Unit 5 The Network Model Data Modeling Issue . Issue: How to represent entities and relationships? . Two major paradigms S P SP • Relational Hierarchical • Graph { Network . E.g., a 'pile' of data • John, 25, NCTU, CS Dept, ... 01 • Mary, 22, NCTU, IS Dept, ... 02 02 02 02 .. 03 03 COBOL 01 data-record 01 02 name PIC X(6) 02 02 02 02 02 age PIC 9(2) 0303 02 univ PIC X(4) 02 dept PIC X(3) Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-2 Model 1: Relational . Model 1: Relational - Decomposition (normalization issue) Emp Dept Project Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-3 Model 2: Hierarchical Dept Emp Project Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-4 Model 3: Network . Model 3: Network proposed by CODASYL Dept 1:M M : M Project Emp 1:M 1:M E-P Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-5 5.1 The Network Model . Data Structure Consider the 'supplier-and-parts‘ database S P SP S# SNAME STATUS CITY P# PNAME COLOR WEIGHT CITY S# P# QTY } Structure S1 Smith 20 London P1 Nut Red 12 London S1 P1 300 S2 Jones 10 Paris P2 Bolt Green 17 Paris S1 P2 200 S3 Blake 30 Paris P3 Screw Blue 17 Rome S1 P3 400 S4 Clark 20 London P4 Screw Red 14 London S1 P4 200 S5 Adams 30 Athens P5 Cam Blue 12 Paris S1 P5 100 P6 Cog Red 19 London S1 P6 100 Sample values S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 S4 P4 300 S4 P5 400 Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-6 The Network Model: Sets and Structure Network Model a set of records (Record types): entities . -
Knowledge Graphs Should AI Know ? CS520
What is a Knowledge Graph? https://web.stanford.edu/~vinayc/kg/notes/What_is_a_Knowledge_Graph... What Knowledge Graphs should AI Know ? CS520 What is a Knowledge Graph? 1. Introduction Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is learned. Our goal here is to explain the basic terminology, concepts and usage of knowledge graphs in a simple to understand manner. We do not intend to give here an exhaustive survey of the past and current work on the topic of knowledge graphs. We will begin by defining knowledge graphs, some applications that have contributed to the recent surge in the popularity of knowledge graphs, and then use of knowledge graphs in machine learning. We will conclude this note by summarizing what is new and different about the recent use of knowledge graphs. 2. Knowledge Graph Definition A knowledge graph is a directed labeled graph in which the labels have well-defined meanings. A directed labeled graph consists of nodes, edges, and labels. Anything can act as a node, for example, people, company, computer, etc. An edge connects a pair of nodes and captures the relationship of interest between them, for example, friendship relationship between two people, customer relationship between a company and person, or a network connection between two computers. -
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.