vinci2017

VINCI 2017

***CHAPTER 3***

*Sciences et technologies de l’information et de la communication*

*Scienze e tecnologie dell'informazione, dell’organizzazione della comunicazione*

New Geovisualization methods for Spatial Big Data Warehouse

Monica Sebillo. University of Salerno,Italy (website her departement is here http://www.di.unisa.it/, her webpage is here http://www.unisa.it/docenti/monicamariasebillo/index)

Sandro Bimonte. IRSTEA, France (website of his equpe COPAIN is here http://www.irstea.fr/en/research/research-units/tscf/communication-and-agri-environmental-information-systems)

This page provides additional information for the submitted Vinci2017 project Chapter 3

1. References of the proposal

2. Pubblications of the two teams on the topic: geovisualization and Spatial Data Warehouse

3. Irstea SOLAP tool

1. References of the proposal

• 1. Bédard, Y, Rivest, S., Proulx, M-J., 2006. Spatial on-line analytical processing (solap): Concepts, architectures, and solutions from a geomatics engineering perspective, Data Warehouses and OLAP: Concepts, Architecture, and Solutions, 14, p. 298–319.

  2. Bimonte, S., 2014. A generic geovisualization model for spatial OLAP and its implementation in a standardsbased architecture, Ingénierie des Systèmes d'Information, 19(5), p. 97-118.

  3. Kimball, R., 1996. The data warehouse toolkit: practical techniques for building dimensional data warehouses, John Wiley & Sons.

  4. MacEachren A, Gahegan M, Pike W., 2004. Geovisualization for Knowledge Construction and Decision Support, IEEE Comput. graphics and appl., 24(1), p. 13-17.

1. Ahmed Eldawy and Mohamed F. Mokbel. 2015. The Era of Big Spatial Data: Challenges and Opportunities. In Proceedings of the 2015 16th IEEE International Conference on Mobile Data Management - Volume 02 (MDM '15), Vol. 2. IEEE Computer Society, Washington, DC, USA, 7-10.

   1. Lee, J., Kang, M.2015. Geospatial Big Data: Challenges and Opportunities. Big Data Research 2(2), p. 74-81

   2. Shekhar, S., Gunturi, V., Evans, M., Yang, K. 2012. Spatial big-data challenges intersecting mobility and cloud computing. MobiDE 201, p. 1-6

   3. Krishnan, K. 2013. Data Warehousing in the Age of Big Data. Elsevier.

   4. Li, S. ,Dragicevic, S., Castro, FA. ,Sester, M. ,Winter, S.,Coltekin, A.,Pettit, A. 2015. Geospatial big data handling theory and methods: A review and research challengesISPRS Journal of Photogrammetry and Remote sensing

   5. Fischer, F., Fuchs, J., Mansmann, F., Keim, D. 2015. BANKSAFE: Visual analytics for big data in large-scale computer networks. Information Visualization 14(1), p. 51-61

   6. Stolte C., Tang D., Hanrahan P. 2008. Polaris: a system for query, analysis, and visualization of multidimensional databases. Communication ACM, 51(11), p. 75- 84.

   7. Silva R., Moura-Pires J., Santos M. 2012. Spatial clustering in SOLAP systems to enhance map visualization. International Journal of Data Warehousing and Mining, 8(2), p.23-43.

   8. Scotch M., Parmanto B. 2006. Development of SOVAT: a numerical-spatial decision support system for community health assessment research. International Journal of Medical Informatics, 34(10) p. 771-784.

   9. Köbben B., Becker T. Blok B. 2012. Webservices for Animated Mapping: The TimeMapper Prototype. Online maps with APIs and webservices. Springer, p. 205-217

   10. Rivest S., Bédard Y., Proulx M.-J., Nadeau M., Hubert F., Pastor M. 2005. SOLAP: merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data. Journal of International Society for Photogrammetry and Remote Sensing (ISPRS) Advances in spatio-temporal analysis and representation, 60(1), p. 17-33.

   11. Malinowski E., Zimányi E. 2008. Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications. Springer.

   12. Leonardi L., Orlando S., Raffaetà A., Roncato A., Silvestri C., Andrienko G., Andrienko N. 2014. A general framework for trajectory data warehousing and visual OLAP. GeoInformatica, 18(2), p. 273-312.

   13. Harrower M., Fabrikant S. 2008. The role of map animation in geographic visualization. Geographic Visualization: Concepts, Tools and Applications. p. 49-65.

   14. Bertin J. 1983. The Semiology of Graphics. University of Wisconsin Press.

   15. Bimonte S. 2010. A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data. International Journal of Agricultural and Environmental Information Systems, 1(2), p. 42-67.

   16. Andrienko N., Andrienko G., Gatalsky P. 2003. Exploratory spatio-temporal visualization: an analytical review. Journal of Visual Languages and Computing & Computing, 14, p. 503-541.

   17. Bimonte, S., Del Fatto, V., Paolino, L., Sebillo, M., Vitiello, G. 2010. A Visual Query Language for Spatial Data Warehouses. AGILE Conf. 2010, p. 43-60

   18. Di Martino, S., Bimonte, S., Bertolotto, M., Ferrucci, F. 2009. Integrating Google Earth within OLAP Tools for Multidimensional Exploration and Analysis of Spatial Data. ICEIS 2009, p. 940-951

   19. Bimonte, S., Tchounikine, A., Di Martino, S., Ferrucci, F. 2007 Supporting Geographical Measures through a New Visualization Metaphor in Spatial OLAP. ICEIS (5) 2007, p. 19-26

   20. Bimonte, S., Johany, F., Lardon, S. 2015. A First Framework for Mutually Enhancing Chorem and Spatial OLAP Systems. DATA 2015, p. 38-48

   21. Del Fatto, V., Laurini, R., Lopez, K., Sebillo, M., Vitiello, G., 2008. A chorem-based approach for visually synthesizing complex phenomena, Information Visualization, 7(3-4), p. 253-26.

   22. Scotch, M., Parmanto, B., Monaco, V. 2007. Usability evaluation of the spatial olap visualization and analysis tool (sovat). Journal of Usability Studies 2, Issue2, p. 76–95

   23. Bozkaya, B., Singh, V. 2015. Geo-Intelligence and Visualization through Big Data Trends. IGI Global

   24. Lemieux , V., Gormly , B., Rowledge , L., 2014. Meeting Big Data challenges with visual analytics: The role of records management, Records Management Journal, 24(2), p.122 - 141

   25. Li Jiyuan, Lingkui Meng, Frank Z. Wang, Wen Zhang et Yang Cai. 2014. A Map-ReduceEnabled SOLAP Cube for Large-Scale Remotely Sensed Data Aggregation . Computers & Geosciences 70, pages 110‑ 19

2. Sandro Bimonte, Ali Hassan, Philippe Beaune: From Design to Visualization of Spatial OLAP Applications: A First Prototyping Methodology. ER Workshops 2016: 113-123

3. Lars Harrie, Hanna Stigmar, Milan Djordjevic: Analytical Estimation of Map Readability. ISPRS Int. J. Geo-Information 4(2): 418-446 (2015)

4. Maha Ben Kraiem, Jamel Feki, Kaïs Khrouf, Franck Ravat, Olivier Teste: Modeling and OLAPing social media: the case of Twitter.Social Netw. Analys. Mining 5(1): 47:1-47:15 (2015)

5. https://www.researchgate.net/profile/Katie_Abrams/publication/230888183_A_Little_Birdie_Told_Me_About_Agriculture_Best_Practices_and_Future_Uses_of_Twitter_in_Agricultural_Communications/links/0fcfd505cac6955b45000000.pdf

6. https://www.youtube.com/watch?v=srx0Hm7BPkw: IRSTEA SOLAP TOOL WITH CHOREMS

7. https://www.youtube.com/watch?v=sxji8pU4WhE&feature=youtu.be. IRSTEA SOLAP TOOL FOR MAP READIBILITY

2. Pubblications of the two teams on the topic: geovisualization and Spatial Data Warehouse

Sandro Bimonte, Vincenzo Del Fatto, Luca Paolino, Monica Sebillo, Giuliana Vitiello: A Visual Query Language for Spatial Data Warehouses. AGILE Conf. 2010: 43-60

Sergio Di Martino, Sandro Bimonte, Michela Bertolotto, Filomena Ferrucci: Integrating Google Earth within OLAP Tools for Multidimensional Exploration and Analysis of Spatial Data. ICEIS 2009: 940-951

Sandro Bimonte, Anne Tchounikine, Sergio Di Martino, Filomena Ferrucci: Supporting Geographical Measures through a New Visualization Metaphor in Spatial OLAP. ICEIS (5) 2007: 19-26

3. Irstea SOLAP tool

Spatial Warehouse (SDW) and Spatial On-Line Analytical Processing (SOLAP) systems are technologies intended to support geographic business intelligence. SOLAP clients provide a cartographic representation of facts on maps, and perform SOP operations through simple user interactions with the maps. Services are unassociated, loosely coupled units of functionality that are self-contained. A Service Oriented Architecture (SOA) is an architectural design pattern where each tier is defined by means of a set of web services. (Un)luckly, there is currently no standard approach to implement SOA solution for visualizing the results of SOLAP queries. Existing SOLAP clients are not flexible and provide closed ad-hoc implementation policies for the visualization of SOLAP queries, which is an important obstacle to interoperability. In this paper we propose a data model for the cartographic visualization of SOLAP queries. Based on this model, we present a new SOA architecture for SOLAP systems where the SOLAP client is totally based on standard data representations (e.g. XMLA, SLD, etc.), and cartographic visualization web services (XMLA, WMS, etc.).

The tool is now under industrialization with the French entreprise Agaetis. A person of 8 months contract is in charge of the developpment. This project is funded by CAPTIVEN Irstea