A spatial column-store to triangulate the Netherlands on the fly

3D digital city models, important for urban planning, are currently constructed from massive point clouds obtained through airborne LiDAR (Light Detection and Ranging). They are semantically enriched with information obtained from auxiliary GIS data like Cadastral data which contains information about the boundaries of properties, road networks, rivers, lakes etc. Technical advances in the LiDAR data acquisition systems made possible the rapid acquisition of high resolution topographical information for an entire country. Such data sets are now reaching the trillion points barrier. To cope with this data deluge and provide up-to-date 3D digital city models on demand current geospatial management strategies should be re-thought. This work presents a column-oriented Spatial Database Management System which provides in-situ data access, effective data skipping, efficient spatial operations, and interactive data visualization. Its efficiency and scalability is demonstrated using a dense LiDAR scan of The Netherlands consisting of 640 billion points and the latest Cadastral information, and compared with PostGIS

Cracking KD-Tree: The first multidimensional adaptive indexing

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns

Development and implementation of utility relocation cost estimation system

This thesis explores how to leverage information management techniques in developing a database system which can store, access and query data to generate preliminary cost estimate reports for utility relocations in highway construction projects. Although cost estimation for utility relocation is an essential part of most transportation projects, there are very few ready-to-use cost database or software platform available to fulfill this purpose for state DOTs personnel. Therefore, the research aimed to develop a database system that can provide estimates with historical cost data. The unit cost data used in this database are derived either from the executed utility agreements between TxDOT office and utility owners or a publicly available open source database. The estimated costs are computed with these pre-stored data. As a result of the research, the Utility Relocation Cost Estimation Database system was completed and has been handed over to TxDOT Austin District for further tests and implementations.Civil, Architectural, and Environmental Engineerin

Development of a Web-enabled Spatial Decision Support System (SDSS) for Prevention of Tick Borne Disease in Kuantan, Malaysia

Ticks are the second most common vectors of human disease after mosquitoes. They are found on many small mammal hosts and also blood-feed on humans with the risk of transmitting diseases. Considering the diseases’ risks, this study has investigated the potential for a web-enabled spatial decision support system (SDSS) to assist government decision-makers in the control, management of resources and prevention of tick borne diseases specifically in the study area of Kuantan, Malaysia