DOCSLIB.ORG

Rasdaman Documentation Release 10.0.0

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Rasdaman Documentation Release 10.0.0 rasdaman documentation Release 10.0.0 rasdaman team Oct 01, 2021 CONTENTS 1 Introduction3 1.1 Features......................................4 1.2 Rasdaman Application Domains.........................6 1.3 OGC geo standards support...........................7 1.4 How to Contribute................................7 2 Installation and Administration Guide9 2.1 Preface......................................9 2.2 Getting Started..................................9 2.3 Directories and Configuration Files....................... 38 2.4 rasdaman Architecture.............................. 42 2.5 Access Interfaces................................. 47 2.6 Server Administration.............................. 49 2.7 Security...................................... 65 2.8 Backup...................................... 65 2.9 Example Database and Programs........................ 66 2.10 Uninstall rasdaman................................ 68 2.11 Troubleshooting................................. 68 3 Contributing to Rasdaman 71 3.1 Development Contributions........................... 72 3.2 Documentation.................................. 72 3.3 Git resources................................... 77 3.4 C++ Guidelines.................................. 80 3.5 Adding Tests................................... 83 3.6 rasdaman Code Guide.............................. 100 3.7 Geo services................................... 120 4 Query Language Guide 129 4.1 Preface...................................... 129 4.2 Introduction................................... 129 4.3 Terminology................................... 132 4.4 Sample Database................................. 133 4.5 Type Definition Using rasql........................... 134 4.6 Query Execution with rasql........................... 141 4.7 Overview: General Query Format........................ 143 i 4.8 Constants..................................... 146 4.9 Spatial Domain Operations............................ 150 4.10 Array Operations................................. 151 4.11 Data Format Conversion............................. 189 4.12 Object identifiers................................. 201 4.13 Null Values.................................... 203 4.14 Miscellaneous.................................. 206 4.15 Arithmetic Errors and Other Exception Situations................ 209 4.16 Database Retrieval and Manipulation...................... 211 4.17 Transaction Scheduling............................. 218 4.18 Linking MDD with Other Data......................... 220 4.19 Storage Layout Language............................ 221 4.20 Web Access to rasql............................... 229 4.21 Appendix A: rasql Grammar........................... 231 4.22 Appendix B: Reserved keywords........................ 235 5 Geo Services Guide 237 5.1 OGC Coverage Standards Overview....................... 237 5.2 OGC Web Services Endpoint.......................... 238 5.3 OGC Coverage Implementation Schema (CIS)................. 239 5.4 OGC Web Coverage Service........................... 242 5.5 OGC Web Coverage Processing Service (WCPS)................ 250 5.6 OGC Web Map Service (WMS)......................... 261 5.7 Data Import.................................... 268 5.8 Data export.................................... 312 5.9 rasdaman / petascope Geo Service Administration............... 313 5.10 Geo Service Standards Compliance....................... 316 6 CRS Management 319 6.1 Introduction................................... 319 6.2 Service...................................... 319 6.3 Concepts..................................... 321 7 RRasdaman Guide 327 7.1 Prerequisites................................... 327 7.2 Connect to rasdaman............................... 327 7.3 Handles...................................... 328 7.4 Arrays representation............................... 328 7.5 Displaying data.................................. 329 7.6 Writing data................................... 330 7.7 Transaction management............................. 332 7.8 Further reading.................................. 332 8 C++ Developers Guide 333 8.1 Preface...................................... 333 8.2 Introduction................................... 334 8.3 Terminology................................... 334 8.4 Application Examples.............................. 334 8.5 Raslib Classes.................................. 340 ii 8.6 Linking MDD with Other Data......................... 361 8.7 Compilation and Linkage of Client Programs.................. 363 8.8 HTML Documentation.............................. 366 9 Java Developers Guide 367 9.1 Preface...................................... 367 9.2 Introduction................................... 368 9.3 Terminology................................... 368 9.4 Application Examples.............................. 368 9.5 rasj........................................ 371 9.6 ODMG...................................... 373 9.7 Points and Intervals............................... 376 9.8 Multidimensional Arrays............................. 378 9.9 Storage Layout.................................. 382 9.10 Collections and Queries............................. 383 9.11 OIDs....................................... 385 9.12 Type Management................................ 386 9.13 Exceptions.................................... 387 9.14 Compilation and Execution of Client Programs................. 391 9.15 HTML Documentation.............................. 392 10 raswct Web Client Toolkit Developer Guide 393 10.1 Preface...................................... 393 10.2 Introduction................................... 394 10.3 Utility Namespace................................ 395 10.4 Query Namespace................................ 396 10.5 Widget Namespace................................ 399 11 Cheatsheets 413 11.1 WCS....................................... 413 11.2 WCPS...................................... 416 11.3 WMS....................................... 420 11.4 Clients...................................... 420 iii iv rasdaman documentation, Release 10.0.0 This is the documentation of rasdaman community, a multi-dimensional Array Analytics En- gine that enables efficient querying and manipulation of multi-dimensional arrays of unlimited size. Rasdaman Community is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. Rasdaman Community is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with rasdaman. If not, see www.gnu.org/licenses. For more information please see www.rasdaman.org or contact Peter Baumann via [email protected]. Originally created by rasdaman GmbH, this document is published under a Creative Commons Attribution-ShareAlike 4.0 International License. All trade names referenced are service mark, trademark, or registered trademark of the respec- tive manufacturer. Contents: CONTENTS 1 rasdaman documentation, Release 10.0.0 2 CONTENTS CHAPTER ONE INTRODUCTION The goodies of database technology for building flexible, large-scale information systems are well known: • information integration bringing along better consistency and much easier administra- tion • flexibility by replacing static APIs by dynamic query languages • scalability achieved through advanced storage and processing techniques, in particular: query optimization which is a powerful performance booster • maturity of decades of development and functionality richness, as opposed to 1.0 ver- sions of reinvented wheels (load balancing, indexing, transaction handling, catalog meta- data management, . ) Unfortunately, these advantages until now can be reaped only for alphanumeric (and, more recently, also vectorial) data types. Raster data (also called gridded data, sampled data, etc.) do not benefit, since traditional databases do not support the information category of large, multidimensional arrays. This gap is closed by the rasdaman technology which offers distinct array management features. Its conceptual model supports arrays of any number of dimensions and over virtually any cell (“pixel”, “voxel”) type. The rasdaman query language, rasql, is crafted along standard SQL and gives high-level, declarative access to any kind of raster data. Its architecture principle of tile stream processing, together with highly effective optimizations, has proven scalable into multi-Terabyte object sizes. Rasdaman has been developed since 1996 and since has reached a high level of maturity itself being in operational use since many years. The rasdaman project strongly commits itself to open standards, in particular those of the geo service community. We actively participate in the development and maintenance of the Open Geospatial Consortium open geo raster standards. Among other activities, we have developed specification and reference implementation of OGC WCS 2.0, WCPS, and WPS. The free and open-source rasdaman community version is available for free download; gener- ally, rasdaman is available in a dual [wiki:License license model]. If the scientific background of rasdaman is of interest, then check out our Publications . and cite our papers! Finally, for the legalese see Imprint and Disclaimer. 3 rasdaman documentation, Release 10.0.0 1.1
Load more

Recommended publications
  • Array Databases: Concepts, Standards, Implementations
    Baumann et al. J Big Data (2021) 8:28 https://doi.org/10.1186/s40537-020-00399-2 SURVEY PAPER Open Access Array databases: concepts, standards, implementations Peter Baumann , Dimitar Misev, Vlad Merticariu and Bang Pham Huu* *Correspondence: b. Abstract phamhuu@jacobs-university. Multi-dimensional arrays (also known as raster data or gridded data) play a key role in de Large-Scale Scientifc many, if not all science and engineering domains where they typically represent spatio- Information Systems temporal sensor, image, simulation output, or statistics “datacubes”. As classic database Research Group, Jacobs technology does not support arrays adequately, such data today are maintained University, Bremen, Germany mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for fexible ad-hoc analytics on large n-D arrays, similar to what SQL ofers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison diferentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the feld; for example, subsetting tests were designed in a way that systems cannot be tuned to specifcally these queries.
    [Show full text]
  • Spatio-Temporal Retrieval with Rasdaman
    Spatio-Temporal Retrieval with RasDaMan Peter Baumann, Andreas Dehmel, Paula Furtado, Roland Ritsch, Norbert Widmann FORWISS (Bavarian Research Center for Knowledge-Based Systems) Orleansstr.34, D-81667 Munich, Germany Dial-up: voice +49-89-48095-207, fax - 203 E-Mail {baumann,dehmel,furtado,ritsch,widmann}@forwiss.de Abstract important research has been accomplished in several subfields. In practice, BLOBs still prevail in multimedia, while statistics and OLAP have developed their own Database support for multidimensional arrays is methods of MDD management. an area of growing importance; a variety of high- The RasDaMan array DBMS has been developed by volume applications such as spatio-temporal data FORWISS in the course of an international project partly management and statistics/OLAP become focus sponsored by the European Commission [Bau97]. The of academic and market interest. overall goal of RasDaMan is to provide classic database RasDaMan is a domain-independent array services in a domain-independent way on MDD database management system with server-based structures. Based on a formal algebraic framework query optimization and evaluation. The system is [Bau99], RasDaMan offers a query language [Bau98a], fully operational and being used in international which extends SQL-92 [ISO92] with declarative MDD projects. We will demonstrate spatio-temporal operators, and an ODMG 2.0 conformant programming retrieval using the rView visual query client. interface [Cat96]. Server-based query evaluation provides Examples will encompass 1-D time series, 2-D several optimization techniques and a specialized storage images, 3-D and 4-D voxel data. The real-life manager. The latter combines MDD tiling with spatial data sets used stem from life sciences, geo indexing and compression whereby an administration sciences, numerical simulation, and climate interface allows to change default strategies for research.
    [Show full text]
  • Web Coverage Service (WCS) V1.0.0
    Open Geospatial Consortium Inc. Date: 2005-10-14 Reference number of this OGC® Project Document: OGC 05-076 Version: 1.0.0 (Corrigendum) Category: OGC® Implementation Specification Editor: John D. Evans Web Coverage Service (WCS), Version 1.0.0 (Corrigendum) Copyright © 2005 Open Geospatial Consortium. All Rights Reserved To obtain additional rights of use, visit http://www.opengeospatial.org/legal/ Document type: OpenGIS® Implementation Specification Document subtype: Corrigendum Document stage: Approved Document language: English OGC 05-076 Contents Page 1 Scope........................................................................................................................1 2 Conformance............................................................................................................1 3 Normative references...............................................................................................1 4 Terms and definitions ..............................................................................................2 5 Conventions .............................................................................................................4 5.1 Symbols (and abbreviated terms) ............................................................................4 5.2 UML notation ..........................................................................................................4 5.3 XML schema notation..............................................................................................6 6 Basic service elements.............................................................................................6
    [Show full text]
  • An Array Database Approach for Earth Observation Data Management and Processing
    International Journal of Geo-Information Article An Array Database Approach for Earth Observation Data Management and Processing Zhenyu Tan 1, Peng Yue 2,3,* and Jianya Gong 2 1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China; [email protected] 2 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; [email protected] 3 Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China * Correspondence: [email protected] Received: 2 June 2017; Accepted: 17 July 2017; Published: 19 July 2017 Abstract: Over the past few years, Earth Observation (EO) has been continuously generating much spatiotemporal data that serves for societies in resource surveillance, environment protection, and disaster prediction. The proliferation of EO data poses great challenges in current approaches for data management and processing. Nowadays, the Array Database technologies show great promise in managing and processing EO Big Data. This paper suggests storing and processing EO data as multidimensional arrays based on state-of-the-art array database technologies. A multidimensional spatiotemporal array model is proposed for EO data with specific strategies for mapping spatial coordinates to dimensional coordinates in the model transformation. It allows consistent query semantics in databases and improves the in-database computing by adopting unified array models in databases for EO data. Our approach is implemented as an extension to SciDB, an open-source array database. The test shows that it gains much better performance in the computation compared with traditional databases. A forest fire simulation study case is presented to demonstrate how the approach facilitates the EO data management and in-database computation.
    [Show full text]
  • Wcs-Core-2.0.1
    Open Geospatial Consortium Approval Date: 2012-07-10 Publication Date: 2012-07-12 External identifier of this OGC® document: http://www.opengis.net/doc/IS/wcs-core-2.0.1 Reference number of this Document: OGC 09-110r4 Version: 2.0.1 Category: OGC® Interface Standard Editor: Peter Baumann OGC® WCS 2.0 Interface Standard- Core: Corrigendum Copyright © 2012 Open Geospatial Consortium. To obtain additional rights of use, visit http://www.opengeospatial.org/legal/. Warning This document is an OGC Member approved corrigendum to existing OGC standard. This document is available on a royalty free, non-discriminatory basis. Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting documentation. Document type: OGC® Interface Standard Document subtype: Corrigendum Document stage: Approved for public release Document language: English OGC 09-110r4 License Agreement Permission is hereby granted by the Open Geospatial Consortium, ("Licensor"), free of charge and subject to the terms set forth below, to any person obtaining a copy of this Intellectual Property and any associated documentation, to deal in the Intellectual Property without restriction (except as set forth below), including without limitation the rights to implement, use, copy, modify, merge, publish, distribute, and/or sublicense copies of the Intellectual Property, and to permit persons to whom the Intellectual Property is furnished to do so, provided that all copyright notices on the intellectual property are retained intact and that each person to whom the Intellectual Property is furnished agrees to the terms of this Agreement.
    [Show full text]
  • IAC-18-F1.2.3 Page 1 of 9 IAC-18.B1.4.4X44924 Bigdatacube: Making Big Data a Commodity Dimitar Miševa*, Peter Baumanna
    69th International Astronautical Congress (IAC), Bremen, Germany, 1-5 October 2018. Copyright ©2018 by the International Astronautical Federation (IAF). All rights reserved. IAC-18.B1.4.4x44924 BigDataCube: Making Big Data a Commodity Dimitar Miševa*, Peter Baumanna, Vlad Merticariua, Dimitris Bellosb, Stefan Wiehlec a Department of Computer Science & Electrical Engineering, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany, {first name initial}.{last name}@jacobs-university.de b cloudeo AG, Ludwigstrasse 8, 80539 Munich, Germany, {first name initial}{last name}@cloudeo.group c Remote Sensing Technology Institute / SAR Signal Processing, German Aerospace Center (DLR), Henrich-Focke- Straße 4, 28199 Bremen, Germany, {first name}.{last name}@dlr.de * Corresponding Author Abstract The BigDataCube project aims at advancing the innovative datacube paradigm – i.e., analysis-ready spatio- temporal raster data – from the level of a scientific prototype to pre-commercial Earth Observation (EO) services. To this end, the European Datacube Engine (in database lingo: ‖Array Database System‖), rasdaman, will be installed on the public German Copernicus hub, CODE-DE, as well as in a commercial cloud environment to exemplarily offer analytics services and to federate both, thereby demonstrating an integrated public/private service. Started in January 2018 with a runtime of 18 months, BigDataCube will complement the batch-oriented Hadoop service already available on CODE-DE with rasdaman thereby offering important additional functionality, in particular a paradigm of ―any query, any time, on any size‖, strictly based on open geo standards and federated with other data centers. On this platform novel, specialized services can be established by third parties in a fast, flexible, and scalable manner.
    [Show full text]
  • GIS Glossary (PDF)
    GIS Glossary A AAT Arc attribute table. A table containing attributes for arc coverage features. In addition to user-defined attributes, the AAT contains the from and to nodes, the left and right polygons, the length, an internal sequence number and a feature identifier. See also feature attribute table. ACCESS directory The system directory that LIBRARIAN uses to store the files that manage access to the library. Each library has an ACCESS directory located in the library's DATABASE directory. accessibility An aggregate measure of how reachable locations are from a given location. The ACCESSIBILITY command computes values for accessibility as a function of the distance between locations and an empirically derived distance decay parameter. access rights The privileges accorded a user for reading, writing, deleting, updating and executing files on a disk. Access rights are stated as `no access', `read only' and `read/write'. ACODE file An INFO data file storing arc attributes for coverages created from TIGER, DIME, IGDS and Etak files. ACODE stands for `Arc CODE'. The ACODE file is related by Cover-ID to the Arc Attribute Table (AAT) of the coverage. address matching A mechanism for relating two files using address as the relate item. Geographic coordinates and attributes can be transferred from one address to the other. For example, a data file containing student addresses can be matched to a street coverage that contains addresses creating a point coverage of where the students live. ADS 1. Arc Digitizing System. A simple digitizing and editing system used to add arcs and label points to a coverage.
    [Show full text]
  • Web Coverage Service (WCS) V0.8.4
    Open Geospatial Consortium Inc. Date: 2003-08-27 Reference number of this OGC™ Project Document: OGC 03-065r6 Version: 1.0.0 Category: OpenGIS® Implementation Specification Editor: John D. Evans Web Coverage Service (WCS), Version 1.0.0 © OGC 2003 – All rights reserved i OGC 03-065r6 Copyright notice Copyright 2002, 2003, BAE SYSTEMS Mission Solutions Copyright 2002, 2003, CubeWerx Inc. Copyright 2002, 2003, George Mason University Copyright 2002, 2003, German Aerospace Center – DLR Copyright 2002, 2003, Intergraph Mapping and Geospatial Solutions Copyright 2002, 2003, IONIC SOFTWARE s.a. Copyright 2002, 2003, National Aeronautics and Space Administration (U.S.) Copyright 2002, 2003, Natural Resources Canada Copyright 2002, 2003, PCI Geomatics Copyright 2002, 2003, Polexis, Inc. The companies listed above have granted the Open Geospatial Consortium, Inc. (OGC) a nonexclusive, royalty-free, paid up, world- wide license to copy and distribute this document and to modify this document and distribute copies of the modified version. This document does not represent a commitment to implement any portion of this specification in any company’s products. OGC’s Legal, IPR and Copyright Statements are found at http://www.opengeospatial.org/about/?page=ipr&view=ipr_policy NOTICE Permission to use, copy, and distribute this document in any medium for any purpose and without fee or royalty is hereby granted, provided that you include the above list of copyright holders and the entire text of this NOTICE. We request that authorship attribution be provided in any software, documents, or other items or products that you create pursuant to the implementation of the contents of this document, or any portion thereof.
    [Show full text]
  • Glossary of Common Gis and Gps Terms
    United States Department of Agriculture NATURAL RESOURCES CONSERVATION SERVICE Cartographic and GIS Technical Note MT-1 (Rev. 1) August 2006 CARTOGRAPHIC AND GIS TECHNICAL NOTE GLOSSARY OF COMMON GIS AND GPS TERMS A almanac Information describing the orbit of each GPS satellite including clock corrections and atmospheric delay parameters. An almanac is used by a GPS receiver to facilitate rapid satellite acquisition. altitude Altitude is specified relative to either mean sea level (MSL) or an ellipsoid (HAE). Altitude above an ellipsoid is the distance from a precise mathematical model, whereas altitude above Mean Sea Level is a distance from a surface of gravitational equipotential that approximates the statistical average level of the sea. AML ARC Macro Language. A high-level algorithmic language for generating end-user applications. Features include the ability to create on-screen menus, use and assign variables, control statement execution, and get and use map or page unit coordinates. AML includes an extensive set of commands that can be used interactively or in AML programs (macros) as well as commands that report on the status of ARC/INFO environment settings. analysis The process of identifying a question or issue to be addressed, modeling the issue, investigating model results, interpreting the results, and possibly making a recommendation. annotation 1. Descriptive text used to label coverage features. It is used for display, not for analysis. 2. One of the feature classes in a coverage used to label other features. lnformation stored for annotation includes a text string, the location at which it is displayed, and a text symbol (color, font, size, etc.) for display.
    [Show full text]
  • Unidata Outreach Activities Focusing on Evolving Standards for Delivering Atmospheric Data Into the GIS Realm (Mainly OGC GALEON)
    Unidata Outreach Activities focusing on Evolving Standards for Delivering Atmospheric Data into the GIS Realm (mainly OGC GALEON) Ben Domenico, Unidata Stefano Nativi, CNR/IMAA Jeff Weber, Unidata With Input from the GALEON Team September 2009 Outreach Activities (Winding Down) • KNMI ADAGUC – Royal Dutch Meteorological Institute – Atmospheric Data Access for the GIS User Community • NSF NSDL: THREDDS 2G – Finished up last CU CIRES evaluation – THREDDS now part of Unidata core • NASA: Gateway to Oceans Land Air Collaboration with George Mason – OGC Standard CS-W search of TDS – Final report in press • NSF GEO: AccessData (formerly DLESE Data Services) – Final workshop was last June – Possible follow on evaluation activity Ongoing Outreach Activities (not the focus of this presentation) • NCAR GIS Program (official program of NCAR as of a couple months ago) • Marine Metadata Interoperability Project • IOOS DMAC Steering Team • CUAHSI Standing Committee • Oceans Interoperability Experiment sponsor • UCAR wide representative to OGC Technical Committee • AGU (and EGU) ESSI Focus Group • ESIN Journal Editorial Board • Liaison to OOI Cyberinfrastructure Project Working Together on A Mosaic for Atmospheric Data ThisThis presentationpresentation describesdescribes andand drawsdraws onon thethe work*work* ofof manymany collaboratingcollaborating individualsindividuals andand institutionsinstitutions * Unidata’s contribution supported by the U.S. National Science Foundation OstiaOstia AnticaAntica circacirca 77 BCBC Acronym Glossary • GALEON (Geo-interface
    [Show full text]
  • On the Integration of Array and Relational Models in Databases
    On the Integration of Array and Relational Models in Databases by Dimitar Miˇsev A thesis submitted in partial fulfillment for the degree of Doctor of Philosophy in Computer Science Prof. Dr. Peter Baumann Jacobs University Bremen Prof. Dr. Michael Sedlmair Jacobs University Bremen Prof. Dr. Tore Risch Uppsala University Dr. Heinrich Stamerjohanns Jacobs University Bremen Date of defense: May 15th, 2018 Computer Science & Electrical Engineering Statutory Declaration Family Name, Given/First Name Miˇsev,Dimitar Matriculation number 20327580 Type of thesis PhD English: Declaration of Authorship I hereby declare that the thesis submitted was created and written solely by myself without any external support. Any sources, direct or indirect, are marked as such. I am aware of the fact that the contents of the thesis in digital form may be revised with regard to usage of unauthorized aid as well as whether the whole or parts of it may be identified as plagiarism. I do agree my work to be entered into a database for it to be compared with existing sources, where it will remain in order to enable further comparisons with future theses. This does not grant any rights of reproduction and usage, however. This document was neither presented to any other examination board nor has it been published. German: Erkl¨arung der Autorenschaft (Urheberschaft) Ich erkl¨arehiermit, dass die vorliegende Arbeit ohne fremde Hilfe ausschließlich von mir erstellt und geschrieben worden ist. Jedwede verwendeten Quellen, direkter oder indirekter Art, sind als solche kenntlich gemacht worden. Mir ist die Tatsache bewusst, dass der Inhalt der Thesis in digitaler Form gepr¨uftwerden kann im Hinblick darauf, ob es sich ganz oder in Teilen um ein Plagiat handelt.
    [Show full text]
  • STANDARDS-BASED SERVICES for BIG SPATIO-TEMPORAL DATA P. Baumann A,*, V. Merticariu B, A. Dumitru B, D. Misev B a Jacobs Univers
    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B4, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic STANDARDS-BASED SERVICES FOR BIG SPATIO-TEMPORAL DATA P. Baumann a,*, V. Merticariu b, A. Dumitru b, D. Misev b a Jacobs University, Germany, - [email protected] b rasdaman GmbH, Bremen, Germany – {merticariu,dumitru,misev}@rasdaman.com KEY WORDS: Big Data, datacubes, coverages, WCS, WCPS, standards, OGC, rasdaman ABSTRACT: With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights - yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets - ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine. 1. INTRODUCTION prompted ISO and INSPIRE to also adopt the OGC coverage and WCS standards, which is currently under way.
    [Show full text]