The Information System of Pompeii Sustainable Preservation Project. A Tool for the Collection, Management and Sharing of Knowledge Useful for Conservation and Renovation of Archaeological Monuments

Pompeii represents a unique archaeological site in the world for the knowledge potential that it preserves, coinciding with an entire city of the ancient world—a fragile urban organism whose conservation represents an enormous challenge. The Pompeii Sustainable Preservation Project (PSPP) is an international and multidisciplinary research project whose purpose is to provide a concrete contribution to the conservation of the funerary monuments in the necropolis of Porta Nocera. The information system of the PSPP, using the most modern technologies in the field of spatial data management, aims to provide a tool for the management and sharing of knowledge useful for future conservation activities of archaeological monuments.

CAPABILITIES OF THE SYSTEM «1C: ENTERPRISE» AND GEOSPATIAL SOLUTIONS IN THE FIELD OF SPATIAL DATA MANAGEMENT

The article presents the ways integrating the "1C:Enterprise" software with various application solutions, including geoinformation systems. The purpose of the study is to consider various ways of interaction between "1C:Enterprise" and GIS systems. Various protocols that facilitate integration with such systems, as Open Data Protocol (OData), Simple Object Access Protocol (SOAP), as well as a mechanism for supporting Web-services built into the "1C:Enterprise" platform are considered. Methods of representation of spatial data are given. A functional model of data integration is developed and built on the example of interaction with a fairly common geographic information system ESRI ArcGIS Online. A practical application of the integration of the software "1C:Enterprise" and geospatial data management systems is regarded.

Spatial Data Management in IoT Systems: Solutions and Evaluation

As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the IoT systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multi-dimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real-world scenarios.

Exhibiting of geospatial attribute data using popup template Java-script application programming interface

<p>Arc-GIS server is used in creating web, desktop, mobile applications. Arc-GIS for server provides end user applications and services for spatial data management, visualization and spatial analysis. The proposed work deals with exhibiting of geo-spatial attribute data using the facility of Java script application programme interfaces (API’s) from Arc-GIS server. Popup-layout API reference is utilized in the work and furthermore two of its properties are utilized relying upon the need of the work. All the programming interfaces have their advantages for encouraging clients work to connect with the geo-spatial information. Keen web maps make an extraordinary method of envisioning complex data. They assist with beating up apparently disconnected data, uncover concealed examples, mine enormous datasets. Information can be composed on the work area, sent to the cloud, and shared utilizing Arc-GIS server on the web.</p>

How Good Are Modern Spatial Libraries?

Many applications today like Uber, Yelp, Tinder, etc. rely on spatial data or locations from its users. These applications and services either build their own spatial data management systems or rely on existing solutions. JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are some of the spatial processing libraries that these systems build upon. These applications and services depend on indexing capabilities available in these libraries for high-performance spatial query processing. In this work, we compare these libraries qualitatively and quantitatively based on four different spatial queries using two real world datasets. We also compare these libraries with an open-source implementation of the Vantage Point Tree—an index structure that has been well studied in image retrieval and nearest-neighbor search algorithms for high-dimensional data. We found that Vantage Point Trees are very competitive and even outperform the aforementioned libraries in two queries.