MULTIMODAL AND MULTIDIMENSIONAL GEODATA VISUALIZATION SYSTEM

It has been observed that Virtual Geographic Environments (VGEs) has been taking a lot of attention over the last decade, particularly within the domain of geographical information systems (GIS) and geographic analysis area. In this paper, we shed the light on the benefits of implementing archaeological visualization systems through the use of Google Earth application. Our application helps the end users and archaeologists working in data exploration and excavation analysis to deal with new web services that allows them to visualize huge amount of data in a new and usable way. For the purposed of our study, have tested our system with data from The Rocha Castle (an historic castle in the Galicia region (Spain) that was built in the 12th century). The system provides access to the excavation database and automatically updates the visualization, whenever the database is changed. The system can handle various types of Data, which could be, one, two or three-dimensional data. The paper aims to answer four fundamental questions regarding archaeological GIS systems: I. How to integrate a one and three dimensions representation into the same scenes? II. How to adapt data resolution to fit them into a particular Level of Visualization Detail (LOD) III. How to optimize data retrieval for efficient recovery data interpolation or continuous visualization? And finally IV. How to represent many objects in the same coordinates without overlapping

TOWARDS LARGE SCALE ENVIRONMENTAL DATA PROCESSING WITH APACHE SPARK

Currently available environmental datasets are either manually constructed by professionals or automatically generated from the observations provided by sensing devices. Usually, the former are modelled and recorded with traditional general-purpose relational technologies, whereas the latter require more specific scientific array formats and tools. Declarative data processing technologies are available both for relational and array data, however, the efficient declarative integrated processing of array and relational environmental data is a problem for which a satisfactory solution has still not been provided. Due to the above, an integrated data processing language called MAPAL has been proposed. This paper provides a brief description of the design decisions and challenges, related to data storage and data processing that arise during the ongoing implementation of MAPAL on top of the Apache Spark large scale data processing framework