Reusable Components, Techniques and Standards for the Ciaas
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Arxiv:2103.14485V2 [Physics.Med-Ph] 12 Apr 2021
ADWI-BIDS: AN EXTENSION TO THE BRAIN IMAGING DATA STRUCTURE FOR ADVANCED DIFFUSION WEIGHTED IMAGING James Gholam1,2, Filip Szczepankiewicz3, Chantal M.W. Tax1,2,4, Lars Mueller2,5, Emre Kopanoglu2,5, Markus Nilsson3, Santiago Aja-Fernandez6, Matt Griffin1, Derek K. Jones2,5, and Leandro Beltrachini1,2 1School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom 2Cardiff Univeristy Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom 3Department of Diagnostic Radiology, Lund University, Lund, Sweden 4Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands 5School of Psychology, Cardiff University, Cardiff, United Kingdom 6Universidad de Valladolid, Valladolid, Spain ABSTRACT Diffusion weighted imaging techniques permit us to infer microstructural detail in biological tissue in vivo and noninvasively. Modern sequences are based on advanced diffusion encoding schemes, allowing probing of more revealing measures of tissue microstructure than the standard apparent diffusion coefficient or fractional anisotropy. Though these methods may result in faster or more revealing acquisitions, they generally demand prior knowledge of sequence-specific parameters for which there is no accepted sharing standard. Here, we present a metadata labelling scheme suitable for the needs of developers and users within the diffusion neuroimaging community alike: a lightweight, unambiguous parametric map relaying acqusition parameters. This extensible scheme supports a wide spectrum of diffusion encoding methods, from single diffusion encoding to highly complex sequences involving arbitrary gradient waveforms. Built under the brain imaging data structure (BIDS), it allows storage of advanced diffusion MRI data comprehensively alongside any other neuroimaging information, facilitating processing pipelines and multimodal analyses. We illustrate the usefulness of this BIDS-extension with a range of example data, and discuss the extension’s impact on pre- and post-processing software. -
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. -
A Coap Server with a Rack Interface for Use of Web Frameworks Such As Ruby on Rails in the Internet of Things
A CoAP Server with a Rack Interface for Use of Web Frameworks such as Ruby on Rails in the Internet of Things Diploma Thesis Henning Muller¨ Matriculation No. 2198830 March 10, 2015 Supervisor Prof. Dr.-Ing. Carsten Bormann Reviewer Dr.-Ing. Olaf Bergmann Adviser Dipl.-Inf. Florian Junge Faculty 3: Computer Science and Mathematics 2afc1e5 cbna This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. http://creativecommons.org/licenses/by-nc-sa/4.0/ Henning Muller¨ [email protected] Abstract We present a Constrained Application Protocol (CoAP) server with a Rack interface to enable application development for the Internet of Things (or Wireless Embedded Internet) using frameworks such as Ruby on Rails. Those frameworks avoid the need for reinvention of the wheel, and simplify the use of Test-driven Development (TDD) and other agile software development methods. They are especially beneficial on less constrained devices such as infrastructure devices or application servers. Our solution supports development of applications almost without paradigm change compared to HTTP and provides performant handling of numerous concurrent clients. The server translates transparently between the protocols and also supports specifics of CoAP such as service and resource discovery, block-wise transfers and observing resources. It also offers the possibility of transparent transcoding between JSON and CBOR payloads. The Resource Directory draft was implemented by us as a Rails application running on our server software. Wir stellen einen Constrained Application Protocol (CoAP) Server mit einem Rack In- terface vor, der Anwendungsentwicklung fur¨ das Internet der Dinge (bzw. das Wireless Embedded Internet) mit Frameworks wie Ruby on Rails ermoglicht.¨ Solche Framworks verhindern die Notwendigkeits, das Rad neu zu erfinden und vereinfachen die Anwen- dung testgetriebener Entwicklung (TDD) und anderer agiler Methoden der Softwareen- twicklung. -
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. -
Fullpaper Format
Virtual ITS European Congress, 9-10 November 2020 Paper number ITS-TP2189 TPEG3: Dynamic Information for Automated Vehicles Matthias Unbehaun1, Teun Hendriks2, Martin Pfeifle3, Pieter Pauwels4, 1 TISA – Traveller Information Services Association, Belgium, [email protected] 2 TISA – Traveller Information Services Association, The Netherlands, [email protected] 3 NNG, Hungary, [email protected] , 4 Be-Mobile, Belgium, [email protected] Abstract The TPEG21 protocol suite for traffic and traveller information is developed and maintained by the Traveller Information Services Association (TISA) for delivering real-time services to support drivers. With the advent of automated vehicles, the role of the drivers and their need for information are changing. As the vehicle will be controlling its own trajectory, it will need more detailed and precise external Information for Automated Driving (I4AD) to do this safely. This paper focusses on traffic-related aspects of I4AD; supporting AD vehicles and passengers to stay safe while driving. This paper outlines technical solutions that solve relevant technical and business issues in context with automated driving, addressing also that not all vehicles are automated. The paper describes example use cases and ‘TPEG3 as a product’ in terms of functional, non-functional and business requirements. Basic ideas and concepts that address these requirements are presented. Keywords: Traffic Information, Automated Vehicles, TPEG Introduction Real-time information to support Automated Vehicles (AD) is very relevant for AD vehicles to be able to properly drive through non-standard situations, such as roadworks where roadworkers may move about and dynamic changes in road layouts are to be expected. -
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. -
PURDUE UNIVERSITY GRADUATE SCHOOL Thesis/Dissertation Acceptance
Graduate School Form 30 Updated 12/26/2015 PURDUE UNIVERSITY GRADUATE SCHOOL Thesis/Dissertation Acceptance This is to certify that the thesis/dissertation prepared By GEETHA R SATYANARAYANA Entitled A QUANTITATIVE COMPARISON & EVALUATION OF PROMINENT MARSHALLING/UN-MARSHALLING FORMATS IN DISTRIBUTED REAL-TIME & EMBEDDED SYSTEMS For the degree of Master of Science Is approved by the final examining committee: JAMES H HILL Chair RAJEEV RAJE MIHRAN TUCERYAN To the best of my knowledge and as understood by the student in the Thesis/Dissertation Agreement, Publication Delay, and Certification Disclaimer (Graduate School Form 32), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy of Integrity in Research” and the use of copyright material. Approved by Major Professor(s): JAMES H HILL SHIAOFEN FANG 7/12/2016 Approved by: Head of the Departmental Graduate Program Date A QUANTITATIVE COMPARISON & EVALUATION OF PROMINENT MARSHALLING/UN-MARSHALLING FORMATS IN DISTRIBUTED REAL-TIME & EMBEDDED SYSTEMS A Thesis Submitted to the Faculty of Purdue University by Geetha R. Satyanarayana In Partial Fulfillment of the Requirements for the Degree of Master of Science August 2016 Purdue University Indianapolis, Indiana ii To Amma & Appa iii ACKNOWLEDGMENTS \All that I am, or hope to be, I owe it to my angel mother." I cannot begin to imagine how life would have been if it weren't for all the hardships my mother had to go through, and all the sacrifices she had to make as a single mother to bring my brother and me to where we are today. I cannot thank her enough for her love, her support, her belief in me and her constant motivation to give my best in everything I do. -
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. -
PGQL: a Property Graph Query Language
PGQL: a Property Graph Query Language Oskar van Rest Sungpack Hong Jinha Kim Oracle Labs Oracle Labs Oracle Labs [email protected] [email protected] [email protected] Xuming Meng Hassan Chafi Oracle Labs Oracle Labs [email protected] hassan.chafi@oracle.com ABSTRACT need has arisen for a well-designed query language for graphs that Graph-based approaches to data analysis have become more supports not only typical SQL-like queries over structured data, but widespread, which has given need for a query language for also graph-style queries for reachability analysis, path finding, and graphs. Such a graph query language needs not only SQL-like graph construction. It is important that such a query language is functionality for querying structured data, but also intrinsic general enough, but also has a right level between expressive power support for typical graph-style applications: reachability anal- and ability to process queries efficiently on large-scale data. ysis, path finding and graph construction. Various graph query languages have been proposed in the past. We propose a new query language for the popular Property Most notably, there is SPARQL, which is the standard query lan- Graph (PG) data model: the Property Graph Query Language guage for the RDF (Resource Description Framework) graph data (PGQL). PGQL is based on the paradigm of graph pattern model, and which has been adopted by several graph databases [1, matching, closely follows syntactic structures of SQL, and pro- 7, 12]. However, the RDF data model regularizes the graph repre- vides regular path queries with conditions on labels and prop- sentation as set of triples (or edges), which means that even con- erties to allow for reachability and path finding queries.