Towards a Domain-Specific Language for Geospatial Data Visualization - - PowerPoint PPT Presentation

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Towards a Domain-Specific Language for Geospatial Data Visualization - - PowerPoint PPT Presentation

Introduction Related Work The Proposed DSL Evaluation Final Remarks Towards a Domain-Specific Language for Geospatial Data Visualization Maps with Big Data Sets Cleverson Ledur, Dalvan Griebler, Isabel Manssour, Luiz Gustavo Fernandes


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Towards a Domain-Specific Language for Geospatial Data Visualization Maps with Big Data Sets

Cleverson Ledur, Dalvan Griebler, Isabel Manssour, Luiz Gustavo Fernandes

Pontifical Catholic University of Rio Grande do Sul - PUCRS Computer Science Graduate Program - PPGCC Grupo de Modelagem de Aplicac ¸ ˜

  • es Paralelas - GMAP

November 2015

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Outline

1

Introduction

2

Related Work

3

The Proposed DSL

4

Evaluation

5

Final Remarks

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Data Visualization Data visualization is the representation of data using graphic elements. Provide a quick understanding of data. Visualization creation workflow has three steps: data transformation, visual mapping and view transformation.

Figure: Visualization Creation Workflow

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Data Visualization Users with low-level knowledge in programming that need to create a geo-spatial data visualization may have a hard time. They will need to know at least JavaScript and HTML to create a simple map. If they are dealing with a huge volume of data, it will be more difficult since most libraries and tools do not provide big data preprocessing.

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Introduction We propose an external domain-specific language for the creation of large-scale data visualization focused on web data visualization map applications. The main goal is to provide a description language that supports the visualization of the detail specification and the manipulation of raw data automatically, using a data pre-processor.

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Related Work

DSL Domain Focus Parallelism Interface Vivaldi Sci/Vol Vis. Vol Rend.

  • Dist. CPU/GPU

High-level (H:Phyton) ViSlang Sci/Vol Vis. Vol Rend. CPU/GPU High-level(H:C++) Diderot Img Analysis & Med. Vis. Img Rend. & Analysis CPU/GPU High-level(H:C) Shadie Med Vis. Vol Rend. CPU/GPU High-level(H:Phyton) Superconductor General Interactive Vis. Rend. CPU/GPU High-level(Ext.) GMaVis Geo-visualization Data Proc. CPU Description Lan.(Ext.)

Vivaldi, ViSlang, Diderot, and Shadie are focusing in the generation of volumetric data visualizations. Superconductor allows the user to create maps because have more expressiveness, but it requires programming skills. Google Maps API, Leaflet and OpenLayers are visualization maps libraries with high-level abstractions. However, they require users to learn a programming language and pre-processing, insert data.

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Proposed DSL Aim at facilitating the creation of visualizations. To be as close as possible to the domain vocabulary (supporting a suitable and friendly language syntax). Users will not have to know programming aspects like functions, variables, methods and any other web development issue. User will have a automatic data processing that enables the data filtering, cleaning and classification. An optimized file loading in memory that allows open files bigger than the RAM memory available in the system.

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Architecture Overview

Figure: DSL Enviroment Figure: Data Pre-processor Workflow

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Interface - Elements Description Language Few lines of code External DSL Manipulation and preprocessing of data This DSL language rule set consists of blocks and declarations.

Figure: Example of Block and Declaration

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Interface - Logical Operators

Figure: Logical Operators for Filtering and Classifying

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Interface Example - Traffic Accidents in Porto Alegre (Brazil)

Figure: Traffic accidents in Porto Alegre.

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Interface Example - Flickr Pictures Classified by Brand of Used Camera

Figure: Flickr pictures by brand of used camera.

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Evaluation SLOCCount for code productivity.

SLOCCount is a software measurement tool, which counts the physical source lines of code (SLOC), estimates development time, cost and effort based on the COCOMO

  • model. It was used in [3, 5, 2, 1] researches.

Google Maps API, OpenLayers, and Leaflet comparison with the proposed DSL. Four data visualization map applications.

Execution time for performance.

Data processing with 10, 50 and 100 GB.

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Data Set We used YFCC100M [4] data set as input. Yahoo Labs (https://labs.yahoo.com/). 54GB of data divided in 10 files delimited by a tab character. This is a public multimedia data set with 99.3 million images and 0.7 million videos from Flickr. ID, Tags, Urls, date, time, device...

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SLOC

1 2 3 4 5 6 7 8 9 Devices War WCup Man Minutes Applications

YFCC100M Data Set

DSL GOOGLEAPI OpenLayers Leaflet

Figure: SLOCCount Programming Code productivity Results

Application DSL Google Maps API OpenLayers Leaflet “Devices” 22 74 25 79 “War” 15 20 27 25 “Manifestation” 15 20 17 25 “World Cup” 15 20 18 25

Table: Code productivity (physical source lines of code).

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Completion Times

Table: Completion Times (Seconds)

Size Data Transformation Visual Mapping - Google Maps API 10GB 110.4948 (Std. 0.9763) 2.910 (Std. 1.6084) 50GB 544.0506 (Std. 9.4225) 3.2738 (Std. 2.0663) 100GB 1098.9284 (Std. 19.0383) 3.8536 (Std. 2.7584)

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Final Remarks We provided a new domain-specific language for the creation of geospatial data visualization with simpler and friendly interface. Our DSL may help data visualization users for gaining insights and extracting information from big data sets. Evaluation demonstrates that it increases the user’s productivity by the possibility of automatically handling raw input data.

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Final Remarks We plan as future work:

To investigate alternatives for taking advantage of the parallelism available on the multi-core architectures, speeding the pre-processing performance. To provide more features in our language interface. For example, new visualization types (graphs, treemaps, column and area charts), and new logical operators for data selection. To support input of JSON and XML. To include some advanced classification with data mining algorithms in our data processor.

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References I

  • D. Adornes, D. Griebler, C. Ledur, and L. G. Fernandes.

A Unified MapReduce Domain-Specific Language for Distributed and Shared Memory Architectures. In The 27th International Conference on Software Engineering & Knowledge Engineering, page 6, Pittsburgh, USA, July 2015. Knowledge Systems Institute Graduate School.

  • D. Griebler, D. Adornes, and L. G. Fernandes.

Performance and Usability Evaluation of a Pattern-Oriented Parallel Programming Interface for Multi-Core Architectures. In The 26th International Conference on Software Engineering & Knowledge Engineering, pages 25–30, Vancouver, Canada, July 2014. Knowledge Systems Institute Graduate School.

  • M. Hertz, Y. Feng, and E. D. Berger.

Garbage Collection Without Paging. In Proceedings of the 2005 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’05, pages 143–153, New York, NY, USA, Aug 2005. ACM.

  • B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L. Li.

The New Data and New Challenges in Multimedia Research. CoRR arXiv eprint, abs/1503.01817, 2015.

  • N. Vazou, E. L. Seidel, R. Jhala, D. Vytiniotis, and S. Peyton-Jones.

Refinement Types for Haskell. In Proceedings of the 19th ACM SIGPLAN International Conference on Functional Programming, ICFP ’14, pages 269–282, New York, NY, USA, August 2014. ACM. Voltar para Capa 19 / 20

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Questions Thank you! Questions & Answers cleversonledur@gmail.com

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