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Performance Analysis of MongoDB Vs. PostGIS/PostGreSQL Databases For Line Intersection and Point Containment Spatial Queries.

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In questions please without the 1 what it's not 1 of of the latest versions of this graphs online for a while I think they have introduced as a non square communities in 1 most just did you start of those in and effects on the ability of those but they they depend on it and and try to kinds of the the comparison and post squares and using a large database last dataset that's a definition of large or not this is the 1st to air the view after we used in the event of a bit of a bit of a person from section and fair opened in the beleaguered lousy at the point in in the 1st layer is is ranked and then use that there's as a template and the standard and and the space and that led to the number of of bands and the number of patients in both of these in both the datasets and the number of intersections and apparent that within a and the people in the paper we can the the number of the intersection of number of points within a polygon rendering million and then these intersections and the engraving billion that was was last datasets I and you are talking about class sections of the sentence and would you define that those graphs on Don's uh this past fall below the largest dataset federal managing among depends on the case that the book 3 cases for the focus is 1 the systematically expected when the distance between all the points with increasing sequentially they think it is the distance between all the points exponentially and then in the Pope regarded begin and so there was past cases they were the quenching is and they would expand introduces of now a preposterous library has gone on for a special functions compared to all can come from the current understanding that is is a alternatives to implement a special functions you just mention the intersection in the you the plant contains uh is is the ways that you can associate to the french special special functions on yet a new food under the with a more restricted areas of interest and that's the spatial that functions and the idea of the dopamine we did the uh and did they did we discuss the possibility of indicating that special functions and I don't know but I police do I please do the and the melody and we're still in the process of including the functions to all of these the inferences this and the not in this this paper and also in the history of the business of the another thing and so I don't know that as soon as I did that mentality and and we some of the regression trees of the fact that on I cuisinary
understanding extensive close to each other of the nineties involves the the the difference between indexed and non-indexed was then that of the use of the but the Paducah predicting lobbyist and that detainees being 4 laps to a single unit this is the reason for that not of and then we may and hence the experience this is because in the moment in sparse cases is then it's in and an intersection use of it in the the 1st the him the for the intersection of plane intersection written queries for In other questions the the insurance agent of the consignee against in to index the spatial data snow of which are the things which are special indexing techniques used to to index is not the way for the it did in India how are the former to change if you had spatial and temporal query and temporary so if you the and statement in the I yeah so you get this place as a sport certain point in time as in space so basically I combine not just a spatial query but I couldn't find the elements as well over the years new database and the other the more I like this did do staple food even I data uh elite and the and the states it's assisted here and you think Christians in the notes and improvements in
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Metadaten

Formale Metadaten

Titel Performance Analysis of MongoDB Vs. PostGIS/PostGreSQL Databases For Line Intersection and Point Containment Spatial Queries.
Serientitel FOSS4G Seoul 2015
Autor agarwal, Sarthak
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
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.
DOI 10.5446/32000
Herausgeber FOSS4G
Erscheinungsjahr 2015
Sprache Englisch
Produzent FOSS4G KOREA
Produktionsjahr 2015
Produktionsort Seoul, South Korea

Inhaltliche Metadaten

Fachgebiet Informatik
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 on- relational databases, which are now being adopted for spatial object storage and handling too. And this is gaining ground in the context of increased shift towards GeoSpatial Web Services on both the Web and mobile platforms especially in the usercentric services, where there is a need to improve the query response time. 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.This paper attempts to evaluate the performance of an existing NoSQL database 'MongoDB' with its inbuilt spatial functions with that of an SQL database with spatial extension 'PostGIS' for two primitive spatial problems LineIntersection and Point Containment problem, across a range of datasets, with varying features counts. For LineIntersection function, the dataset consisted of two independent layers of horizontal lines and vertical lines with incremental lengths and their size varied from ten lines to ten thousand lines in each layer and another dataset with two layers, one of random lines of variable size and shape and another layer of a single line which is intersecting many lines of layer1. For Point Containment problem, the dataset consists of two layers, one of polygons in a space of different shape and size and another layer of random points in the space, some inside the polygons and some outside. All the data in the analysis was processed In memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 25x for Line Intersection Problem and 10x for Point Containment Problem which increases exponentially as the data size increases in both indexed and non indexed operations. Given these results NoSQL databases may be better suited for simultaneous multipleuser query systems including WebGIS and mobileGIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.

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