Kevin Shaw

A Rule-based Approach for the Conflation of Attributed Vector Data

In this paper we present a complete approach for the conflation of attributed vector digital mapping data such as the Vector Product Format (VPF) datasets produced and disseminated by the National Imagery and Mapping Agency (NIMA). While other work in the field of conflation has traditionally used statistical techniques based on proximity of features, the approach presented here utilizes all information associated with data, including attribute information such as feature codes from a standardized set, associated data quality information of varying levels, and topology, as well as more traditional measures of geometry and proximity. In particular, we address the issues associated with the problem of matching features and maintaining accuracy requirements. A hierarchical rule-based approach augmented with capabilities for reasoning under uncertainty is presented for feature matching as well as for the determination of attribute sets and values for the resulting merged features. Additionally, an in-depth analysis of horizontal accuracy considerations with respect to point features is given. An implementation of the attribute and geometrical matching phases within the scope of an expert system has proven the efficacy of the approach and is discussed within the context of the VPF data.

The 2-3TR-tree, a trajectory-oriented index structure for fully evolving valid-time spatio-temporal datasets

Supporting large volumes of multi-dimensional data is an inherent characteristic of modern database applications, such as Geographical Information Systems (GIS), Computer Aided design (CAD), and Image and Multimedia Databases. Such databases need underlying systems with extended features like query languages, data models, and indexing methods, as compared to traditional databases, mainly because of the complexity of representing and retrieving data. The presented work deals with access methods for databases that accurately model the real world. More precisely, the focus is on index structures that can capture the time varying nature of moving objects, namely spatio-temporal structures. A new taxonomy to classify these structures has been defined according to dataset characteristics and query requirements. Then, a new spatio-temporal access method, the 2-3TR-tree, has been designed to process specific datasets and fulfill specific query requirements that no other existing spatio-temporal index could handle.

Stream data management

to Stream Data Management.- Query Execution and Optimization.- Filtering, Punctuation, Windows and Synopses.- XML & Data Streams.- CAPE: A Constraint-Aware Adaptive Stream Processing Engine.- Efficient Support for Time Series Queries in Data Stream Management Systems.- Managing Distributed Geographical Data Streams with the GIDB Portal System.- Streaming Data Dissemination Using Peer-Peer Systems.

Enhancing the US Navy's GIDB Portal with Web Services

Using Web services, the authors have been able to increase the amount of data accessible through the Naval Research Laboratorys geospatial information database (GIDB) portal. They created a Web crawler for Web mapping services (WMS) that harvests and adds data to the portal index and a translator that provides access to non-WMS data through the WMS interface. The integrated Web services broker, using traditional Web services standards, provides automated access to meteorological and oceanographic data

Fuzzy spatial relationship refinements based on minimum bounding rectangle variations

Abstract Many spatial data modeling strategies rely upon approximate representations of spatial objects both for computational efficiency issued as well as the simplification of logical modeling strategies. The most widely used approximation is the minimum bounding rectangle (MBR). While the use of MBRs in spatial data modeling is extensive due to their efficiency for storage and relationship calculation, their use as a solitary means of identifying, for example, topological relationships between objects is problematic due to the inconsistency of mappings between relationships of MBRs and corresponding relationships of the objects they represent. In this paper we examine several extensions to the MBR model that reduce the discrepancies between binary spatial relationships of the MBRs and those of the contained objects. For each scheme, we consider the implications to the determination of fuzzy spatial relationships and the impact on computational issues.

Efficient AKNN spatial network queries using the M-Tree

Aggregate K Nearest Neighbor (AKNN) queries are problematic when performed within spatial networks. While simpler network queries may be solved by a single network traversal search, the AKNN requires a large number costly network distance computations to completely compute results. The M-Tree index, when used with Road Network Embedding, provides an efficient alternative which can return estimates of the AKNN results. The M-Tree index can then be used as a filter for AKNN results by quickly computing a superset of the query results. The final AKNN query results can be computed by sorting the results from the M-Tree. In comparison to Incremental Euclidean Restriction (IER), the M-Tree reduces the overall query processing time and the total number of necessary network distance computations required to complete a query. In addition, the M-Tree filtering method is tunable to allow increasing performance at the expense of accuracy, making it suitable for a wide variety of applications.

Hydraulic Splines: A Hybrid Approach to Modeling River Channel Geometries

The hydraulic spline algorithm generates irregular 2D channel grids from highly accurate cross-sectional survey data at any desired resolution, facilitating its integration with high-density light detection and ranging (lidar) data.

Case-based classification alternatives to ontologies for automated web service discovery and integration

Web Services are becoming the standard technology used to share data for many Navy and other DoD operations. Since Web Services technologies provide for discoverable, self-describing services that conform to common standards, this paradigm holds the promise of an automated capability to obtain and integrate data. However, automated integration of applications to access and retrieve data from heterogeneous sources in a distributed system such as the Internet poses many difficulties. Assimilation of data from Web-based sources means that differences in schema and terminology prevent simple querying and retrieval of data. Thus, machine understanding of the Web Services interface is necessary for automated selection and invocation of the correct service. Service availability is also an issue that needs to be resolved. There have been many advances on ontologies to help resolve these difficulties to support the goal of sharing knowledge for various domains of interest. In this paper we examine the use of case-based classification as an alternative/supplement to using ontologies for resolving several questions related to knowledge sharing. While ontologies encompass a formal definition of a domain of interest, case-based reasoning is a problem solving methodology that retrieves and reuses decisions from stored cases to solve new problems, and case-based classification involves applying this methodology to classification tasks. Our approach generalizes well in sparse data, which characterizes our Web Services application. We present our study as it relates to our work on development of the Advanced MetOc Broker, whose objective is the automated application integration of meteorological and oceanographic (MetOc) Web Services.

Efficient Approximation of Spatial Network Queries using the M-Tree with Road Network Embedding

Spatial networks, such as road systems, operate differently from normal geospatial systems because objects are constrained to locations on the network. Performing queries on spatial networks demands entirely different solutions. Most spatial queries make use of an R-Tree to process them efficiently. The M-Tree is a data tree index which is capable of indexing data in any metric space. The M-Tree index can replace the R-Tree index for spatial network queries, such as range and KNN queries. The difficulty is that the M-Tree is only as efficient as the distance algorithm used on the underlying objects. Most network distance algorithms, such as A*, are too slow to allow the M-Tree to operate efficiently on spatial networks. The truncated road network embedding (tRNE) maps the network into a higher dimensional space where any LP metric can be used to efficiently compute an accurate approximation of network distance. The M-Tree combined with tRNE creates an efficient index structure for computing spatial network queries. The M-Tree substantially outperforms network expansion, the most popular method of computing spatial network queries, when performing spatial network KNN and range queries.

Querying Multiple Data Sources via an Object-Oriented Spatial Query Interface and Framework

Abstract A spatial query interface has been designed and implemented in the object-oriented paradigm for heterogeneous data sets. The object-oriented approach presented is shown to be highly suitable for querying typical multiple heterogeneous sources of spatial data. The spatial query model takes into consideration two common components of spatial data: spatial location and attributes. Spatial location allows users to specify an area or a region of interest, also known as a spatial range query. Also, the spatial query allows users to query spatial orientation and relationships (geometric and topological relationships) among other spatial data within the selected area or region. Queries on the properties and values of attributes provide more detailed non-spatial characteristics of spatial data. A query model specific to spatial data involves exploitation of both spatial and attribute components. This paper presents a conceptual spatial query model of heterogeneous data sets based on the object-oriented data model used in the geospatial information distribution system (GIDS).