Combining Geospatial and Temporal Ontologies

Publicly available ontologies are growing in number at present. These ontologies describe entities in a domain and the relations among these entities. This thesis describes a method to automatically combine a pair of orthogonal ontologies using cross products. A geospatial ontology and a temporal ontology are combined in this work. Computing the cross product of the geospatial and the temporal ontologies gives a complete set of pairwise combination of terms from the two ontologies. This method offers researchers the benefit of using ontologies that are already existing and available rather than building new ontologies for areas outside their scope of expertise. The resulting framework describes a geospatial domain over all possible temporal granularities or levels, allowing one domain to be understood from the perspective of another domain. Further queries on the framework help a user to make higher order inferences about a domain. In this work, Protege, an open source ontology editor and a knowledge base tool, is used to model ontologies. Protege supports the creation, visualization and manipulation of ontologies in various formats including XML (Extensible Markup Language). Use of standard and extensible languages like XML allows sharing of data across different information systems, and thus supports reuse of these ontologies. Both the geospatial ontology and the temporal ontology are represented in Protege. This thesis demonstrates the usefulness of this integrated spatio-temporal framework for reasoning about geospatial domains. SQL queries can be applied to the cross product to return to the user different kinds of information about their domain. For example, a geospatial term Library can be combined with all terms from the temporal ontology to consider Library over all possible kinds of times, including those that might have been overlooked during previous analyses. Visualizations of cross product spaces using Graphviz provides a means for displaying the geospatial-temporal terms as well as the different relations that link these terms. This visualization step also highlights the structure of the cross product for users. In order to generate a more tractable cross product for analysis purposes, methods for filtering terms from the cross product are also introduced. Filtering results in a more focused understanding of the spatio-temporal framework

Adding HL7 version 3 data types to PostgreSQL

The HL7 standard is widely used to exchange medical information electronically. As a part of the standard, HL7 defines scalar communication data types like physical quantity, point in time and concept descriptor but also complex types such as interval types, collection types and probabilistic types. Typical HL7 applications will store their communications in a database, resulting in a translation from HL7 concepts and types into database types. Since the data types were not designed to be implemented in a relational database server, this transition is cumbersome and fraught with programmer error. The purpose of this paper is two fold. First we analyze the HL7 version 3 data type definitions and define a number of conditions that must be met, for the data type to be suitable for implementation in a relational database. As a result of this analysis we describe a number of possible improvements in the HL7 specification. Second we describe an implementation in the PostgreSQL database server and show that the database server can effectively execute scientific calculations with units of measure, supports a large number of operations on time points and intervals, and can perform operations that are akin to a medical terminology server. Experiments on synthetic data show that the user defined types perform better than an implementation that uses only standard data types from the database server.Comment: 12 pages, 9 figures, 6 table

Technical report of data mining

No abstract availabl

Modeling Multiple Granularities of Spatial Objects

People conceptualize objects in an information space over different levels of details or granularities and shift among these granularities as necessary for the task at hand. Shifting among granularities is fundamental for understanding and reasoning about an information space. In general, shifting to a coarser granularity can improve one\u27s understanding of a complex information space, whereas shifting to a more detailed granularity reveals information that is otherwise unknown. To arrive at a coarser granularity. objects must be generalized. There are multiple ways to perform generalization. Several generalization methods have been adopted from the abstraction processes that are intuitively carried out by people. Although, people seem to be able to carry out abstractions and generalize objects with ease. formalizing these generalization and shifts between them in an information system, such as geographic information system, still offers many challenges. A set of rules capturing multiple granularities of objects and the use of these granularities for enhanced reasoning and browsing is yet to be well researched. This thesis pursues an approach for arriving at multiple granularities of spatial objects based on the concept of coarsening. Coarsening refers to the process of transforming a representation of objects into a less detailed representation. The focus of this thesis is to develop a set of coarsening operators that are based on the objects\u27 attributes, attribute values and relations with other objects, such as containment, connectivity, and nearness. for arriving at coarser or amalgamated objects. As a result. a set of four coarsening operators—group, group, compose, coexist, and filter are defined. A framework, called a granularity graph. is presented for modeling the application of coarsening operators iteratively to form amalgamated objects. A granularity graph can be used to browse through objects at different granularities, to retrieve objects that are at different granularities, and to examine how the granularities are related to each other. There can occur long sequences of operators between objects in the graph, which need to be simplified. Compositions of coarsening operators are derived to collapse or simplify the chain of operators. The semantics associated with objects amalgamations enable to determine correct results of the compositions of coarsening operators. The composition of operators enables to determine all the possible ways for arriving at a coarser granularity of objects from a set of objects. Capturing these different ways facilitates enhanced reasoning of how objects at multiple granularities are related to each other

Logical Interpretation of Relational Databases

The reformulation of data management type databases in a formal, logical calculus is described. Advantages of this logical form are to provide a framework for automatic inferencing on the database as well as a formal clarification of the databases semantics. Principle applications are to artificially intelligent managerial decision support systems

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

Detecting anomalous longitudinal associations through higher order mining

The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it is often difficult to determine which algorithms might detect anomalies given any random dataset. In this paper we provide a partial solution to this problem by elevating the search for anomalous data in transaction-oriented datasets to an inspection of the rules that can be produced by higher order longitudinal/spatio-temporal association rule mining. In this way we are able to apply algorithms that may provide a view of anomalies that is arguably closer to that sought by information analysts.Sydney, NS