Automated conflation framework for integrating transportation big datasets

The constant merging of the data, commonly known as Conflation, from various sources, has been a vital part for any phase of development, be it planning, governing the existing system or to study the effects of any intervention in the system. Conflation allows enriching the existing data by integrating information through numerous sources available out there. This process becomes unusually critical because of the complexities these diverse data bring along such as, distinct accuracies with which data has been collected, projections, diverse nomenclature adaption, etc., and hence demands special attention. Although conflation has always been a topic of interest among researchers, this area has witnessed a significant enthusiasm recently due to current advancements in the data collection methods. Even though with this escalation in interest, the developed methods didn't justify the expansions field of data collections has made. Contemporary conflation algorithms still lack an efficient automated technique; most of the existing system demands some sort of human involvement for the analysis to achieve higher accuracy. Through this work, an effort has been made to establish a fully automated process to conflate the road segments of Missouri state from two big data sources. Taking the traditional conflation a step further, this study has also focused on enriching the road segments with traffic information like delay, volume, route safety, etc., by conflating with available traffic data and crash data. The accuracy of the conflation rate achieved through this algorithm was 80-95 percent for the different data sources. The final conflated layer gives detailed information about road networks coupled with traffic parameters like delay, travel time, route safety, travel time reliability, etc.by Neetu ChoubeyIncludes bibliographical reference

A spatial mediator model for integrating heterogeneous spatial data

The complexity and richness of geospatial data create specific problems in heterogeneous data integration. To deal with this type of data integration, we propose a spatial mediator embedded in a large distributed mobile environment (GeoGrid). The spatial mediator takes a user request from a field application and uses the request to select the appropriate data sources, constructs subqueries for the selected data sources, defines the process of combining the results from the subqueries, and develop an integration script that controls the integration process in order to respond to the request. The spatial mediator uses ontologies to support search for both geographic location based on symbolic terms as well as providing a term-based index to spatial data sources based on the relational model. In our approach, application designers only need to be aware of a minimum amount about the queries needed to supply users with the required data. The key part of this research has been the development of the spatial mediator that can dynamically respond to requests within the GeoGrid environment for geographic maps and related relational spatial data