Geographic Information System Interoperability in Local Community Environment

Abstract
In this paper we present research in Geographic Information Systems (GIS) interoperability. Also, this paper describes interoperability framework called GeoNis. GeoNis uses new technologies, to perform integration task between GIS applications and legacy data sources over the Internet. Our approach provides integration of distributed GIS data sources and legacy information systems in local community environment. The proposed framework uses the technology based on mediators and ontologies, to allow communications between GIS applications over the Internet/Intranet.

Introduction
Popularity of GIS in government and municipality institutions induces an increasing amount of available information [1]. In local community environment (city services, local offices, local telecom, public utilities, water and power supply services, etc) different information systems deal with huge amount of available information, where the most of data in databases is geo-referenced. Information that exists in different spatial databases may be useful for many other GIS applications. The problem of bringing together heterogeneous and distributed information systems is known as interoperability problem. Today, research on interoperability solutions promises a way to move away from the monolithic systems that dominate the GIS market [2].

Research in information systems interoperability is motivated by the ever-increasing heterogeneity of the computer world. Heterogeneity in GIS is not an exception, but the complexity and richness of geographic data and the difficulty of their representation raise specific issues for GIS interoperability. Great number of independent geo-data producers, accessible by means of World Wide Web, has only increase problems of heterogeneity.

Interoperability of information systems relies on bases of agreement that describe what is shared among information sources. To enable interoperability, remote systems must be able not only to locate and access data sources, but also to interpret and process retrieved data. In order to achieve this, remote systems had to deal not only with syntactically heterogeneous data objects (objects that are organized following different conceptual schemas) but as well with semantically heterogeneous objects (objects that that have different meaning) [3,4,5].

The paper is structured as follows. In the first part, we describe related work in interoperability, mediation and ontologies in GIS. In the second part of paper, we describe architecture and role of GeoNis interoperability framework. The goals of our research activities, described in this paper, are defining architecture for semantic integration of distributed and heterogeneous GIS data sources and adding the integration technology to the existing framework. Making local geographic datasets available publicly and establishing a common interoperability framework over shared data interchange protocols are important parts of this research.

GIS and Interoperability
GIS applications often have to process geo-data obtained from various geoinformation communities. In that kind of environment problem of semantic heterogeneity often arise. In such cases there is a problem with correct interpretation of datasets obtained from different geo-information communities. Very often different datasets can use different terms for same kind of information. On the other hand different datasets can use same term for completely different piece of information. These problems can lead to serious conflicts during discovering and interpretation of geographic data.

For example let’s consider geographic information system in local community environment [5,6]. In such environment, information provided by GIS application can have key influence on decision make by local community authorities. In many cases this decision are crucial for everyday functioning of the community and for short-term and long-term planning of local community development. Geographic information, used during decision-making process, is originating from different organizations (local Telecom, water and soil service, transport service, power supply service, police and other local government services) in local community (Figure 1).

Very often, as shown in Figure 1, different organizations in local community are interested for same spatial object. But every organization has different view and different understanding of that object, and according to that produce different datasets that describes that object. Some attributes in this datasets are common for all organization in local community, some are common for few of them and every organization can have some specific attributes for dataset. At the same time different organizations can use different terms for same dataset attributes (synonyms) or same term for different dataset attributes (homonyms).

Fig. 1. Local GI community environment