We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Using NoSQL & HTML5 Libraries To Rapidly Generate Interactive Web Visualisations Of High-volume Spatio-temporal Data

Formale Metadaten

Titel
Using NoSQL & HTML5 Libraries To Rapidly Generate Interactive Web Visualisations Of High-volume Spatio-temporal Data
Serientitel
Anzahl der Teile
95
Autor
Lizenz
CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben
Identifikatoren
Herausgeber
Erscheinungsjahr
Sprache
ProduktionsortNottingham

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
Twitter has developed over the past few years into a potent source of public opinion and comment. The service passed 500 million users in June 2012, collectively posting hundreds of millions of tweets each day, and several high-profile analyses of this data (such as the Twitter Political Index, which mapped sentiment across the US towards the 2012 presidential candidates over the course of their campaigns) have demonstrated its potential for insight and near-time customer feedback. Handling such large volumes and throughputs of data is a sizeable engineering challenge, however, and several commercial ventures (TweetReach, Tweet Archivist - many others) have sprung up specifically to deal with this complexity - at a cost. In addition, many existing solutions are unable to properly utilise the location data that is present in a significant proportion of tweets, losing out on the rich geographical context. This retrospective aims to demonstrate how an informed coupling of emerging open-source component technologies can be used to resolve the complex problems of i. large stored data volumes, ii. real-time streaming input, iii. concurrency of writes and iv. geographically querying and visualising results - with a minimal development outlay. Specifically, the construction of an open-source process to read, process, write, query and visualise streaming, geolocated Twitter data using the MongoDB NoSQL database and D3.js JavaScript library will be detailed, focusing on how MongoDB handles real-time spatial data (including spatial indexes & querying) and the unique features that make D3 so well-suited to visualising and exploring spatial data in the web browser.