Performance Analysis of Mongodb Vs. Postgis/Postgresql
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IR2 Trees for Spatial Database Keyword Search NSF Award
IR2 Trees for Spatial Database Keyword Search NSF Award: CNS-0220562 Mil: Infrastructure for Research and Training in Database Management for Web-based Geospatial Data Visualization with Applications to Aviation PI: Naphtali Rishe Florida International University School of Computing and Information Sciences Co-PI: Boonserm Wongsaroj Florida Memorial University Department of Computer Sciences and Mathematics ABSTRACT Our Mil-provided infrastructure and research support has enabled us to perform basic research in keyword search. Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to - specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally and analytically compared to current methods and are shown to have superior performance and excellent scalability. 1. INTRODUCTION An increasing number of applications require the efficient execution of nearest neighbor queries constrained by the properties of the spatial objects. -
Spatial Databases and Spatial Indexing Techniques
Spatial Databases and Spatial Indexing Techniques Timos Sellis Computer Science Division Department of Electrical and Computer Engineering National Technical University of Athens Zografou 15773, GREECE Tel: +30-1-772-1601 FAX: +30-1-772-1659 e-mail: [email protected] Spatial Database Systems Timos Sellis Spatial Databases and Spatial Indexing Techniques Timos Sellis National Technical University of Athens e-mail: [email protected] Aalborg, June 1998 Outline • Data Models • Algebra • Query Languages • Data Structures • Query Processing and Optimization • System Architecture • Open Research Issues Spatial Database Systems 1 1 Spatial Database Systems Timos Sellis Introduction : Spatial Database Management Systems (SDBMS) QUESTION “What is a Spatial Database Management System ?” ANSWER • SDBMS is a DBMS • It offers spatial data types in its data model and query language • support of spatial relationships / properties / operations • It supports spatial data types in its implementation • efficient indexing and retrieval • support of spatial selection / join Spatial Database Systems 2 Applications of SDBMS Traditional GIS applications • Socio-Economic applications • Urban planning • Route optimization, market analysis • Environmental applications • Fire or Pollution Monitoring • Administrative applications • Public networks administration • Vehicle navigation Spatial Database Systems 3 2 Spatial Database Systems Timos Sellis Applications of SDBMS (cont'd) Novel applications • Image and Multimedia databases • shape configuration -
Niklas Appelmann
Niklas Appelmann Full-Stack Web Developer [email protected] https://niklasappelmann.de +49 151 6469 0684 Nationality German Education Bachelor of Science Computer Science Certificates Company Data Protection Officer IHK / Betrieblicher Datenschutzbeauftragter IHK (June 2018) Languages English (fluent) German (native) Skills Project Management Agile, Scrum, Kanban Frameworks and Tools AWS, Docker, Selenium, Puppeteer, MongoDB Programming Languages / Frameworks React, Meteor, Node.js, JavaScript, Python, Clojure, ClojureScript, CoffeeScript, TypeScript Relevant 3.5 years Experience Project examples since 01/2020 Fullstack Development, Freelance Various engagements in web development. • Automation of lead generation and business processes using Python, Selenium and Puppeteer. • Frontend development using ClojureScript and Reagent • Backend development using Node.js, Clojure and MongoDB • DevOps and Backend development at the government hackathon #wirvsvirus (2020) • AWS consulting (S3, Lambda, EC2, ECS, Route53, SQS, SES, Lightsail, DynamoDB, CloudFront) • Serverless fullstack architecture using AWS Team members: Other freelancers and remote contributors Technologies / Python, Selenium, Puppeteer, ClojureScript, Clojure, reagent, MongoDB, Tools Node.js, Meteor 03/2018 - Fullstack-Developer, blink.it GmbH & Co. KG 12/2019 Development of a progressive Meteor web application in a fast paced startup environment. • Built frontend functionality with React (JavaScript, CoffeeScript), Blaze, HTML5, SCSS/CSS • Built backend functionality with Node.js, Python and MongoDB • Worked on performance optimiziation on a large scale multi-tenant reactive application • Developed tools for data migration and automation of internal processes with Python • Developed an internal solution to migrate Pipedrive leads to Hubspot for a large set of leads and past deal flows Team members: 10 Technologies / React, AWS, JavaScript, CoffeeScript, Python, MongoDB, Node.js, Blaze, Tools HTML5, SCSS/CSS, Selenium, SCORM 10/2016 - Software Engineer (working student), blink.it GmbH & Co. -
Compressed Spatial Hierarchical Bitmap (Cshb) Indexes for Efficiently Processing Spatial Range Query Workloads
Compressed Spatial Hierarchical Bitmap (cSHB) Indexes for Efficiently Processing Spatial Range Query Workloads ∗ Parth Nagarkar K. Selçuk Candan Aneesha Bhat Arizona State University Arizona State University Arizona State University Tempe, AZ 85287-8809, USA Tempe, AZ 85287-8809, USA Tempe, AZ 85287-8809, USA [email protected] [email protected] [email protected] ABSTRACT terms of their coordinates in 2D space. Queries in this 2D In most spatial data management applications, objects are space are then processed using multidimensional/spatial in- represented in terms of their coordinates in a 2-dimensional dex structures that help quick access to the data [28]. space and search queries in this space are processed using spatial index structures. On the other hand, bitmap-based 1.1 Spatial Data Structures indexing, especially thanks to the compression opportuni- The key principle behind most indexing mechanisms is to ties bitmaps provide, has been shown to be highly effective ensure that data objects closer to each other in the data for query processing workloads including selection and ag- space are also closer to each other on the storage medium. gregation operations. In this paper, we show that bitmap- In the case of 1D data, this task is relatively easy as the based indexing can also be highly effective for managing total order implicit in the 1D space helps sorting the ob- spatial data sets. More specifically, we propose a novel com- jects so that they can be stored in a way that satisfies the pressed spatial hierarchical bitmap (cSHB) index structure above principle. When the space in which the objects are to support spatial range queries. -
Building a Spatial Database in Postgresql
Building a Spatial Database in PostgreSQL David Blasby Refractions Research [email protected] http://postgis.refractions.net Introduction • PostGIS is a spatial extension for PostgreSQL • PostGIS aims to be an “OpenGIS Simple Features for SQL” compliant spatial database • I am the principal developer Topics of Discussion • Spatial data and spatial databases • Adding spatial extensions to PostgreSQL • OpenGIS and standards Why PostGIS? • There aren’t any good open source spatial databases available • commercial ones are very expensive • Aren’t any open source spatial functions • extremely difficult to properly code • building block for any spatial project • Enable information to be organized, visualized, and analyzed like never before What is a Spatial Database? Database that: • Stores spatial objects • Manipulates spatial objects just like other objects in the database What is Spatial data? • Data which describes either location or shape e.g.House or Fire Hydrant location Roads, Rivers, Pipelines, Power lines Forests, Parks, Municipalities, Lakes What is Spatial data? • In the abstract, reductionist view of the computer, these entities are represented as Points, Lines, and Polygons. Roads are represented as Lines Mail Boxes are represented as Points Topic Three Land Use Classifications are represented as Polygons Topic Three Combination of all the previous data Spatial Relationships • Not just interested in location, also interested in “Relationships” between objects that are very hard to model outside the spatial domain. • The most -
Introduction to Postgis
Introduction to PostGIS Tutorial ID: IGET_WEBGIS_002 This tutorial has been developed by BVIEER as part of the IGET web portal intended to provide easy access to geospatial education. This tutorial is released under the Creative Commons license. Your support will help our team to improve the content and to continue to offer high quality geospatial educational resources. For suggestions and feedback please visit www.iget.in. IGET_WEBGIS_002 Introduction to PostGIS Introduction to PostGIS Objective In this tutorial we will learn, how to register a new server, creating a spatial database in PostGIS, importing data to the spatial database, spatial indexing and some important PostGIS special functions. Software: OpenGeo Suite 3.0 Level: Beginner Time required: 3 Hour Software: OpenGeo Suite 3.0 Level: Beginner Prerequisites and Geospatial Skills Basic computer skills IGET_WEBGIS_001 should be completed Basic knowledge of SQL is excepted Readings 1. Introduction to the OpenGeo Suite, Chapter 2: PostGIS, pp. 21 – 38. http://presentations.opengeo.org/2012_FOSSGIS/suiteintro.pdf 2. Chapter 13. PostGIS Special Functions Index, http://suite.opengeo.org/docs/postgis/postgis/html/PostGIS_Special_Functions_Index.html Tutorial Data: The tutorial data of this exercise may be downloaded from this link: 2 IGET_WEBGIS_002 Introduction to PostGIS Introduction PostGIS is an open source spatial database extension that turns PostgreSQL database system into a spatial database. It adds support for geographic objects allowing location queries, analytical functions for raster and vector data, raster map algebra, spatial re-projection, network topology, Geodetic measurements and much more. In this tutorial we will learn how to register a server and creation of a spatial database, and importing the shapefiles into it, for this purpose we are using the shapefiles digitized during the tutorial IGET_GIS_005: Digitization of Toposheet using Quantum GIS. -
Tutorial for Course of Spatial Database
Tutorial for Course of Spatial Database Spring 2013 Rui Zhu School of Architecture and the Built Environment Royal Institute of Technology-KTH Stockholm, Sweden April 2013 Lab 1 Introduction to RDBMS and SQL pp. 02 Lab 2 Introduction to Relational Database and Modeling pp. 16 Lab 3 SQL and Data Indexing Techniques in Relational Database pp. 18 Lab 4 Spatial Data Modeling with SDBMS (1) Conceptual and Logical Modeling pp. 21 Lab 5 Spatial Data Modeling with SDBMS(2) Implementation in SDB pp. 25 Lab 6 Spatial Queries and Indices pp. 30 Help File Install pgAdmin and Shapefile Import/Export Plugin pp. 36 1 AG2425 Spatial Databases Period 4, 2013 Spring Rui Zhu and Gyözö Gidófalvi Last Updated: March 18, 2013 Lab 1: Introduction to RDBMS and SQL Due March 27th, 2013 1. Background This introductory lab is designed for students that are new to databases and have no or limited knowledge about databases and SQL. The instructions first introduce some basic relational databases concepts and SQL, then show a number of tools that allow users to interact with PostgreSQL/PostGIS databases (pgAdmin: administration and management tool administration; QuantumGIS). Lastly, the instructions point to a short and simple SQL tutorial using an online PostgreSQL/PostGIS database and ask the students to write a few simple queries against this database. 2. Tutorial 2.1. What is SQL? As it is stated in Wikipedia, SQL (Structured Query Language) is a special-purpose programming language designed for managing data held in a relational database management system (RDBMS). SQL is a simple and unified language, which offers many features to make it as a powerfully diverse that people can work with a secure way. -
Analyzing the Performance of Nosql Vs. SQL Databases for Spatial And
Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings Volume 17 Boston, USA Article 4 2017 Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries Sarthak Agarwal International Institute of Information Technology Hyderabad Gachibowli, Hyderabad, India KS Rajan International Institute of Information Technology Hyderabad Gachibowli, Hyderabad, India Follow this and additional works at: https://scholarworks.umass.edu/foss4g Part of the Data Storage Systems Commons Recommended Citation Agarwal, Sarthak and Rajan, KS (2017) "Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries," Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings: Vol. 17 , Article 4. DOI: https://doi.org/10.7275/R5736P26 Available at: https://scholarworks.umass.edu/foss4g/vol17/iss1/4 This Paper is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries Sarthak Agarwala,∗, KS Rajana aInternational Institute of Information Technology Hyderabad Gachibowli, Hyderabad, India Abstract: Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. -
E-Commerce Application Using MERN Stack
Quang Nhat Mai E-commerce Application using MERN stack Metropolia University of Applied Sciences Bachelor of Engineering Information Technology Bachelor’s Thesis 30 August 2018 Abstract Author Quang Nhat Mai Title E-commerce Application using MERN stack Number of Pages 36 pages + x appendices Date 30 August 2020 Degree Bachelor of Engineering Degree Programme Information Technology Professional Major Software Engineering Instructors Janne Salonen This thesis is about studying the basic components of MERN Stack technology such as: MongoDB, ExpressJS framework, ReactJS library, and NodeJS platform. Discussing the basic functions of an e-commerce web application such as sign up, sign in, showing dash- boards, displaying store categories and products Using MERN Stack technology in conjunc- tion with Braintree to build a web application to search for product stores and payment gate- way. Develop administrative functions for the website such as: user management, store management, statistics, and reports. Since then, this topic is declared to research and create an online product search website so that small grocery stores and groceries can post and manage their products right on the website. website system and branding. At the same time, the store can link to its own website (if any). For customers, they can quickly search for urgent products from their nearby stores. Based on the search results, customers can directly contact the store owner to discuss more about the product they are looking for. Keywords Contents List of Abbreviations 1 Introduction -
Fat-Free Framework 1.4.1 Quick Reference Akismet Auth Data
Akismet Data An API wrapper that you can use during input validation to Input data handlers and validators. determine if a blog comment, trackback, or pingback contains spam. This plug-in requires a key from akismet.com. input( string fields, mixed handler, [ string tags ],[ integer filter ], [ array options ] ); check( string text, string author, string email, string url ); Assign handler to HTML form fields for validation and Submit content (usually a blog comment) to akismet.com. manipulation. handler may be an anonymous or named function, Returns TRUE if the content is determined as spam. a single or daisy-chained string of named functions similar to the route() method. This command passes two arguments to handler ham( string text, string author, string email, string url ); function: value and field name. HTML and PHP tags are stripped Report content as a false positive. If the argument tags is not specified. PHP validation/sanitize filters, filter flags, and options may be passed as additional spam( string text, string author, string email, string url ); Quick Reference Quick Reference arguments. See the PHP filter_var() function for more details on Report content that was not marked as spam. filter types. verify( string key ); scrub( mixed value, [ string tags ] ); Secure approval from akismet.com to use the public API for Remove all HTML tags to protect against XSS/SQL injection spam checking. A valid key is required to use the API. Returns attacks. tags , if specified, will be preserved. If value is an array, TRUE if authentication succeeds. 1.4.1 1.4.1 HTML tags in all string elements will be scrubbed. -
JSON As an XML Alternative
JSON The Fat-Free Alternative to XML { “Lecture”: 27, “Course”: “CSC375”, “Days”: ”TTh", “Instructor”: “Haidar Harmanani” } JSON as an XML Alternative • JSON is a light-weight alternative to XML for data- interchange • JSON = JavaScript Object Notation – It’s really language independent – most programming languages can easily read it and instantiate objects or some other data structure • Defined in RFC 4627 • Started gaining tracking ~2006 and now widely used • http://json.org/ has more information JSON as an XML Alternative • What is JSON? – JSON is language independent – JSON is "self-describing" and easy to understand – *JSON uses JavaScript syntax for describing data objects, but JSON is still language and platform independent. JSON parsers and JSON libraries exists for many different programming languages. • JSON -Evaluates to JavaScript Objects – The JSON text format is syntactically identical to the code for creating JavaScript objects. – Because of this similarity, instead of using a parser, a JavaScript program can use the built-in eval() function and execute JSON data to produce native JavaScript objects. Example {"firstName": "John", l This is a JSON object "lastName" : "Smith", "age" : 25, with five key-value pairs "address" : l Objects are wrapped by {"streetAdr” : "21 2nd Street", curly braces "city" : "New York", "state" : "NY", l There are no object IDs ”zip" : "10021"}, l Keys are strings "phoneNumber": l Values are numbers, [{"type" : "home", "number": "212 555-1234"}, strings, objects or {"type" : "fax", arrays "number” : "646 555-4567"}] l Aarrays are wrapped by } square brackets The BNF is simple When to use JSON? • SOAP is a protocol specification for exchanging structured information in the implementation of Web Services. -
Oracle Spatial
Oracle Spatial An Oracle Technical White Paper May 2002 Oracle Spatial Abstract...............................................................................................................1 1.0 Introduction...............................................................................................1 2.0 Spatial Data and ORDBMS.....................................................................3 2.1 Object-Relational Database Management Systems (ORDBMS)...3 2.2 Challenges of Spatial Databases.........................................................3 2.2.1 Geometry .......................................................................................3 2.2.2 Distribution of Objects in Space................................................4 2.2.3 Temporal Changes........................................................................4 2.2.4 Data Volume .................................................................................4 2.3 Requirements of a Spatial Database System.....................................4 2.3.1 Classification of Space..................................................................5 2.3.2 Data Model ....................................................................................5 2.3.3 Query Language ............................................................................5 2.3.4 Spatial Query Processing .............................................................6 2.3.5 Spatial Indexing.............................................................................6 3.0 Oracle Spatial.............................................................................................7