Giorgos Mountrakis

A Differential Spatio-temporal Model: Primitives and Operators

In this paper, we present a differential change-oriented model for the storage and communication of spatio-temporal information. The focus is on the development of model primitives and operators to support the aggregation of change over time and the propagation of change across resolution. Our investigation is motivated by recent advances in image-based geospatial databases, with constantly increasing update frequencies, and diverse user communities performing queries of various levels of detail. The primitives and operators presented here extend existing qualitative operators to support the management of quantitative and geometric information within a change-oriented spatio-temporal environment. We also show how the design of our model results from ‘change semantics’ at different granularities. By doing so, advanced communication operations can be addressed within our model in an efficient way.

Learning Similarity with Fuzzy Functions of Adaptable Complexity

A common approach in database queries involves the multi-dimensional representation of objects by a set of features. These features are compared to the query representation and then combined together to produce a total similarity metric. In this paper we introduce a novel technique for similarity learning within features (attributes) by manipulating fuzzy membership functions (FMFs) of different complexity. Our approach is based on a gradual complexity increase adaptable to problem requirements. The underlying idea is that less adaptable functions will act as approximations for more complex ones. We begin by interpolating a set of planes in the training dataset and due to linearity we get a fast first impression of the underlying complexity. We proceed to interpolate two asymmetrical sigmoidal functions whose initial approximations are calculated from the plane properties. If satisfactory accuracy is not achieved we provide advanced modeling capabilities by investigating FMFs parameters and convolving their output with additional functions.

Navigating through hierarchical change propagation in spatiotemporal queries

In spatiotemporal applications, meaningful changes vary according to object type, level of detail, and nature of application. We introduce a dynamic classification scheme and its interaction with a spatiotemporal model, to describe change propagation in spatiotemporal queries. Our classification is based on identifying three levels of change, ranging from simple verification of the continuous existence of an entity to the identification of change and the detailed description of this change. In order to provide a transformation function between these levels, we introduce a tree-based hierarchy in the spatial and temporal domains. This hierarchy is dynamic and continuously updated as new information arrives. The user has the flexibility to navigate through different resolution representations by manipulating two functions, the minimum spatial element and the minimum temporal element. In the temporal domain, queries are further more decoded as point-based and interval-based. In doing so we address the problem of time continuity within a spatiotemporal query context.

Adaptable User Profiles for Intelligent Geospatial Queries

The geospatial information user community is becoming increasingly diverse, with numerous users accessing distributed datasets for various types of applications. Currently in GIS, unlike traditional databases, there is a lack of machine learning algorithms to customize information retrieval results. Thus the particular interests of individual users are not taken into account in traditional geospatial queries. In this paper we present a system that adjusts query results based on user requirements and needs. It does so by using a collection of fuzzy functions that express user preference specifically in GIS environments. The focus of this work is on preference learning for one-dimensional, quantitative attributes, and on the customization of geospatial queries using this information. The model used to express user preferences adjusts gradually to the underlying complexity during a training process, starting with fairly simple linear functions and progressing to complex non-linear ones as needed. Our advanced modeling capabilities are demonstrated through an applicability example, and statistical simulations show the robustness of our system.

Supporting Quality-Based Image Retrieval Through User Preference Learning

It is common for modern geospatial libraries to contain multiple datasets that cover the same area but differ only in some specific quality attributes (e.g., resolution and precision). This is affecting the concept of content-based geospatial queries, as simple coverage-based query mechanisms (e.g., declaring a specific area of interest) as well as theme-based query mechanisms (e.g., requesting a black and white aerial photo or multispectral satellite imagery) are rendered inadequate to identify and access specific datasets in such collections. In this paper we introduce a novel approach to handle data quality attributes in geospatial queries. Our approach is characterized by the ability to model and learn user preferences, thus establishing user profiles that allow us to customize image queries for improving their functionality in a constantly diversifying geospatial user community.

Multitemporal geospatial query grouping using correlation signatures

With recent advances in temporal and spatiotemporal databases, user demands are becoming more complex. As a result, simple queries are replaced by complex multitemporal query scenarios. In this paper we propose a novel image-based approach to temporally group together multidimensional geospatial queries. Correlation signatures act as a powerful raster mapping that visualizes multi-dimensional similarity of multiple queries and expresses it in a temporally referenced manner. Convolution of our raster representation with discrete weight masks can express arbitrary temporal preference (e.g. relative, cyclic queries). Furthermore, our multiquery grouping in the temporal domain can also support temporal relations between queries (e.g. alternative scenarios, AND/OR operators). By transforming the problem in the image domain, the expressiveness of our method allows an intuitive visual interaction to assist nonexpert database users.