Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data
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Array DBMS in Environmental Science
The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 21-23 September, 2017, Bucharest, Romania Array DBMS in Environmental Science: Satellite Sea Surface Height Data in the Cloud Ramon Antonio Rodriges Zalipynis National Research University Higher School of Economics, Moscow, Russia [email protected] Abstract – Nowadays environmental science experiences data model is designed to uniformly represent diverse tremendous growth of raster data: N-dimensional (N-d) raster data types and formats, take into account a dis- arrays coming mainly from numeric simulation and Earth tributed cluster environment, and be independent of the remote sensing. An array DBMS is a tool to streamline raster data processing. However, raster data are usually stored in underlying raster file formats at the same time. Also, new files, not in databases. Moreover, numerous command line distributed algorithms are proposed based on the model. tools exist for processing raster files. This paper describes a Four modern raster data management trends are rele- distributed array DBMS under development that partially vant to this paper: industrial raster data models, formal delegates raster data processing to such tools. Our DBMS offers a new N-d array data model to abstract from the array models and algebras, in situ data processing algo- files and the tools and processes data in a distributed fashion rithms, and raster (array) DBMS. Good survey on the directly in their native file formats. As a case study, popular algorithms is contained in [7]. A recent survey of existing satellite altimetry data were used for the experiments carried array DBMS and similar systems is in [5]. -
An Array DBMS for Simulation Analysis and ML Models Predictions
SAVIME: An Array DBMS for Simulation Analysis and ML Models Predictions Hermano. L. S. Lustosa1, Anderson C. Silva1, Daniel N. R. da Silva1, Patrick Valduriez2, Fabio Porto1 1 National Laboratory for Scientific Computing, Rio de Janeiro, Brazil {hermano, achaves, dramos, fporto}@lncc.br 2 Inria, University of Montpellier, CNRS, LIRMM, France [email protected] Abstract. Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make them benefit from DBMS support, enabling declarative data analysis and visualization over scientific data, we present an in-memory array DBMS called SAVIME. In this work we describe the system SAVIME, along with its data model. Our preliminary evaluation show how SAVIME, by using a simple storage definition language (SDL) can outperform the state-of-the-art array database system, SciDB, during the process of data ingestion. We also show that it is possible to use SAVIME as a storage alternative for a numerical solver without affecting its scalability, making it useful for modern ML based applications. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems Keywords: Scientific Data Management, Multidimensional Array, Machine Learning 1. INTRODUCTION Due to the increasing computational power of HPC environments, vast amounts of data are now generated in different research fields, and a relevant part of this data is best represented as array data. Geospatial and temporal data in climate modeling, astronomical images, medical imagery, multimedia and simulation data are naturally represented as multidimensional arrays. To analyze such data, DBMSs offer many advantages, like query languages, which eases data analysis and avoids the need for extensive coding/scripting, and a logical data view that isolates data from the applications that consume it. -
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. -
The Gamma Operator for Big Data Summarization on an Array DBMS
JMLR: Workshop and Conference Proceedings 36:88{103, 2014 BIGMINE 2014 The Gamma Operator for Big Data Summarization on an Array DBMS Carlos Ordonez [email protected] Yiqun Zhang [email protected] Wellington Cabrera [email protected] University of Houston, USA Editors: Wei Fan, Albert Bifet, Qiang Yang and Philip Yu Abstract SciDB is a parallel array DBMS that provides multidimensional arrays, a query language and basic ACID properties. In this paper, we introduce a summarization matrix operator that computes sufficient statistics in one pass and in parallel on an array DBMS. Such sufficient statistics benefit a big family of statistical and machine learning models, including PCA, linear regression and variable selection. Experimental evaluation on a parallel cluster shows our matrix operator exhibits linear time complexity and linear speedup. Moreover, our operator is shown to be an order of magnitude faster than SciDB built-in operators, two orders of magnitude faster than SQL queries on a fast column DBMS and even faster than the R package when the data set fits in RAM. We show SciDB operators and the R package fail due to RAM limitations, whereas our operator does not. We also show PCA and linear regression computation is reduced to a few minutes for large data sets. On the other hand, a Gibbs sampler for variable selection can iterate much faster in the array DBMS than in R, exploiting the summarization matrix. Keywords: Array, Matrix, Linear Models, Summarization, Parallel 1. Introduction Row DBMSs remain the best technology for OLTP [Stonebraker et al.(2007)] and col- umn DBMSs are becoming a competitor in OLAP (Business Intelligence) query processing [Stonebraker et al.(2005)] in large data warehouses [Stonebraker et al.(2010)]. -
Multidimensional Arrays for Analysing Geoscientific Data
International Journal of Geo-Information Review Multidimensional Arrays for Analysing Geoscientific Data Meng Lu 1,2,*,† , Marius Appel 1 and Edzer Pebesma 1 1 Institute for Geoinformatics, University of Muenster, 48149 Muenster, Germany; [email protected] (M.A.); [email protected] (E.P.) 2 Department of Physical Geography, Faculty of Geoscience, Utrecht University, 3584 CB Utrecht, The Netherlands * Correspondence: [email protected]; Tel.: +31 648901629 † Current address: Vening Meinesz Building A, Princetonlaan 8A, 3584 CB Utrecht, The Netherlands. Received: 27 June 2018; Accepted: 30 July 2018; Published: 3 August 2018 Abstract: Geographic data is growing in size and variety, which calls for big data management tools and analysis methods. To efficiently integrate information from high dimensional data, this paper explicitly proposes array-based modeling. A large portion of Earth observations and model simulations are naturally arrays once digitalized. This paper discusses the challenges in using arrays such as the discretization of continuous spatiotemporal phenomena, irregular dimensions, regridding, high-dimensional data analysis, and large-scale data management. We define categories and applications of typical array operations, compare their implementation in open-source software, and demonstrate dimension reduction and array regridding in study cases using Landsat and MODIS imagery. It turns out that arrays are a convenient data structure for representing and analysing many spatiotemporal phenomena. Although the array model simplifies data organization, array properties like the meaning of grid cell values are rarely being made explicit in practice. Keywords: multidimensional arrays; geoscientific data; data analysis; spatiotemporal modeling 1. Introduction An array is a storage form for a sequence of objects of similar type. -
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. -
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. -
A Survey on Array Storage, Query Languages, and Systems
A Survey on Array Storage, Query Languages, and Systems Florin Rusu Yu Cheng University of California, Merced 5200 N Lake Road Merced, CA 95343 {frusu,ycheng4}@ucmerced.edu February 2013 Abstract Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organizedalong three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention. 1 Introduction Big Data [33] is the new buzz word in computer science as of 2012. There are new conferences orga- nized specifically to tackle Big Data issues. Many classical research areas – beyond data management and databases – allocate significant attention to Big Data problems. And, most importantly, state governments provide unprecedented amounts of funds to support Big Data research [36]—at the end of the day, Big Data analytics played a significant role in the 2012 US presidential elections. -
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. -
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. -
The Bigdawg Polystore System
The BigDAWG Polystore System Jennie Duggan Aaron J. Elmore Michael Magda Balazinska Bill Howe Northwestern Univ. of Chicago Stonebraker Univ. of Univ. of MIT Washington Washington Jeremy Kepner Sam Madden David Maier Tim Mattson Stan Zdonik MIT MIT Portland State Intel Brown Univ. ABSTRACT and Technology Center for Big Data, we have constructed This paper presents a new view of federated databases to a medical example of this new class of applications, based address the growing need for managing information that on the MIMIC II dataset [18]. These publicly available pa- spans multiple data models. This trend is fueled by the tient records cover 26,000 intensive care unit admissions proliferation of storage engines and query languages based at Boston’s Beth Israel Deaconess Hospital. It includes on the observation that “no one size fits all”. To address waveform data (up to 125 Hz measurements from bed- this shift, we propose a polystore architecture; it is de- side devices), patient metadata (name, age, etc.), doctor’s signed to unify querying over multiple data models. We and nurse’s notes (text), lab results, and prescriptions filled consider the challenges and opportunities associated with (semi-structured data). A production implementation would polystores. Open questions in this space revolve around store all of the historical data augmented by real-time streams query optimization and the assignment of objects to stor- from current patients. Given the variety of data sources, age engines. We introduce our approach to these topics this system must support an assortment of data types, stan- and discuss our prototype in the context of the Intel Sci- dard SQL analytics (e.g., how many patients were given ence and Technology Center for Big Data. -
The Architecture of Scidb
The Architecture of SciDB Michael Stonebraker, Paul Brown, Alex Poliakov, Suchi Raman Paradigm4, Inc. 186 Third Avenue Waltham, MA 02451 Abstract. SciDB is an open-source analytical database oriented toward the data management needs of scientists. As such it mixes statistical and linear algebra operations with data management ones, using a natural nested multi-dimensional array data model. We have been working on the code for two years, most recently with the help of venture capital backing. Release 11.06 (June 2011) is downloadable from our website (SciDB.org). This paper presents the main design decisions of SciDB. It focuses on our decisions concerning a high-level, SQL-like query language, the issues facing our query optimizer and executor and efficient storage management for arrays. The paper also discusses implementation of features not usually present in DBMSs, including version control, uncertainty and provenance. Keywords: scientific data management, multi-dimensional array, statistics, linear algebra 1 Introduction and Background The Large Synoptic Survey Telescope (LSST) [1] is the next “big science” astronomy project, a telescope being erected in Chile, which will ultimately collect and manage some 100 Petabytes of raw and derived data. In October 2007, the members of the LSST data management team realized the scope of their data management problem, and that they were uncertain how to move forward. As a result, they organized the first Extremely Large Data Base (XLDB-1) conference at the Stanford National Accelerator Laboratory [2]. Present were many scientists from a variety of natural science disciplines as well as representatives from large web properties. All reported the following requirements: Multi-petabyte amounts of data.