Dieter Pfoser

Capturing the Uncertainty of Moving-Object Representations

Spatiotemporal applications, such as fleet management and air traffic control, involving continuously moving objects are increasingly at the focus of research efforts. The representation of the continuously changing positions of the objects is fundamentally important in these applications. This paper reports on on-going research in the representation of the positions of moving-point objects. More specifically, object positions are sampled using the Global Positioning System, and interpolation is applied to determine positions in-between the samples. Special attention is given in the representation to the quantification of the position uncertainty introduced by the sampling technique and the interpolation. In addition, the paper considers the use for query processing of the proposed representation in conjunction with indexing. It is demonstrated how queries involving uncertainty may be answered using the standard filter-and-refine approach known from spatial query processing.

Querying the trajectories of on-line mobile objects

Position data is expected to play a central role in a wide range of mobile computing applications, including advertising, leisure, safety, security, tourist, and traffic applications. Applications such as these are characterized by large quantities of wirelessly Internet-worked, position-aware mobile objects that receive services where the objects position is essential. The movement of an object is captured via sampling, resulting in a trajectory consisting of a sequence of connected line segments for each moving object. This paper presents a technique for querying these trajectories. The technique uses indices for the processing of spatiotemporal range queries on trajectories. If object movement is constrained by the presence of infrastructure, e.g., lakes, park areas, etc., the technique is capable of exploiting this to reduce the range query, the purpose being to obtain better query performance. Specifically, an algorithm is proposed that segments the original range query based on the infrastructure contained in its range. The applicability and limitations of the proposal are assessed via empirical performance studies with varying datasets and parameter settings.

Requirements, definitions, and notations for spatiotemporal application environments

Modeling spatiotemporal applications is a complex task, involving intricate issues, such as the representation of objects’ position in time, and spatial attributes that change values depending on specific locations in time periods. Due to this complexity, the analysis of users’ requirements−as the first phase of an application development methodology−is often neglected, focusing, mainly, on physical design aspects. In this paper, we address the set of spatial, temporal and spatiotemporal concepts as they are drawn from users’ needs. The goal is to support the developer’s better understanding about spatiotemporal applications, by providing the concepts and the notations needed in such environments; these concepts, will later be translated into specific constructs and implementation issues. More specifically, space, spatial objects, spatial attributes, fields, time and models of time are presented and then combined to accommodate spatiotemporal peculiarities, resulting into the new, spatiotemporal, concepts of snapshots, changes, and versions of objects and maps, motion and phenomena. Examples taken from two real large-scale applications show the necessity and adequacy of the presented concepts.

Capturing Fuzziness and Uncertainty of Spatiotemporal Objects

For the majority of spatiotemporal applications, we assume that the modeled world is precise and bound. This simplification seems unnecessary crude for many environments handling spatial and temporal extents, such as navigational applications. In this work, we explore fuzziness and uncertainty, which we subsume under the term indeterminacy, in the spatiotemporal context. We first show how the fundamental modeling concepts of spatial objects, attributes, relationships, time points, time periods, and events are influenced by indeterminacy, and then show how these concepts can be combined. Next, we focus on the change of spatial objects according to their geometry over time. We outline four scenarios, which identify discrete and continuous change, and we present how to model indeterminate change. We demonstrate the applicability of this proposal by describing the uncertainty related to the movement of point objects, such as the recording of the whereabouts of taxis.

Indeterminacy and Spatiotemporal Data: Basic Definitions and Case Study

For some spatiotemporal applications, it can be assumed that the modeled world is precise and bounded, and that also our record of it is precise. While these simplifying assumptions are sufficient in applications like a land information system, they are unnecessarily crude for many other applications that manage data with spatial and/or temporal extents, such as navigational applications. This work explores fuzziness and uncertainty, subsumed under the term indeterminacy, in the spatiotemporal context. To better illustrate the basic spatiotemporal concepts of change or evolution, it is shown how the fundamental modeling concepts of spatial objects, attributes, and relationships and time points and periods are influenced by indeterminacy and how they can be combined. In particular, the focus is on the change of spatial objects and their geometries across time. Four change scenarios are outlined, which concern discrete versus continuous change and asynchronous versus synchronous measurement, and it is shown how to model indeterminacy for each. A case study illustrates the applicability of the paper’s general proposal by describing the uncertainty related to the management of the movements of point objects, such as the management of vehicle positions in a fleet management system.

Data semantics in location-based services

As location-based applications become part of our everyday life, ranging from traffic prediction systems to services over mobile phones providing us with information about our surroundings, the call for more semantics and accurate services is emerging. In this work, we analyze and register the data semantics of Location-based Services (LBS). Initially, we categorize LBS data according to the related concepts and use. We distinguish the (a) Domain Data, including spatial and temporal concepts, namely, position, location, movement and time, (b) Content Data, describing the LBS specific content, and (c) Application Data, consisting of the user profile and the services provided by LBS. Next, we model these three data categories in a way that captures their peculiarities and allows their sharing and exchange among different LBS, when desired. For this, we use semantically rich and expressive models, like UML, as well as the long-praised method of ontologies, realized in the open source, ontology and knowledge-based editor Protege. To argue about the design choices and show their applicability, we present examples from two characteristic real-world applications, both in the Athens Metropolitan Area: an LBS for tourists carrying mobile devices, and a traffic LBS informing drivers about troublesome situations.

Trajectory Indexing Using Movement Constraints

With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensional (x, y) space, which means that their movements across time may be embedded in the three-dimensional (x, y, t) space. Further, the movements are typically represented as trajectories, sequences of connected line segments. In certain cases, movement is restricted; specifically, in this paper, we aim at exploiting that movements occur in transportation networks to reduce the dimensionality of the data. Briefly, the idea is to reduce movements to occur in one spatial dimension. As a consequence, the movement occurs in two-dimensional (x, t) space. The advantages of considering such lower-dimensional trajectories are that the overall size of the data is reduced and that lower-dimensional data is to be indexed. Since off-the-shelf database management systems typically do not offer higher-dimensional indexing, this reduction in dimensionality allows us to use existing DBMSes to store and index trajectories. Moreover, we argue that, given the right circumstances, indexing these dimensionality-reduced trajectories can be more efficient than using a three-dimensional index. A decisive factor here is the fractal dimension of the network—the lower, the more efficient is the proposed approach. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks.

Incremental join of time-oriented data

Data warehouses as well as a wide range of other databases exhibit a strong temporal orientation: it is important to track the temporal variation of data over several months or years. In addition, databases often exhibit append-only characteristics where old data is retained while new data is appended. Performing joins efficiently on large databases such as these is essential to obtain good overall query processing performance. The paper presents a sort-merge based incremental algorithm for time oriented data. While incremental computation techniques have proven competitive in many settings, they also introduce a space overhead in the form of differential files. For the temporal data explored here, this overhead is avoided because the differential files are already part of the database. In addition, data is naturally sorted, leaving only merging. The incremental algorithm works in a partitioned storage environment and does not assume the availability of indices, making it a competitor to sort based and nested-loop joins. The paper presents analytical as well as simulation based characterizations of the performance of the join.

Chapter 6: Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B + -tree, hashing with chaining, and, in some cases, linear hashing [52]. Object-oriented and object-relational systems have also adopted these structures to a great extend.

Spatial Data Management Aspects in Archaeological Excavation Documentation1

Archaeological excavation documentation poses several challenges to spatial data management. This work gives a data model, respective query functionality and the description of the resulting prototype system for recording and analyzing the data produced during the course of an excavation. Based on a requirements analysis, we develop a data model that not only captures geo-referenced finds (pottery sherds, bones, etc.) and their properties, but also excavation diaries and the particularities of the excavation space. Towards an effort of analyzing the collected data and constructing a virtual excavation space, we describe spatial query capabilities providing access to the data in two and three-dimensional space. The system also captures uncertainty with respect to position, which is quite common due to either missing information and/or approximate measurements. Finally, we will give a complete overview of “Arxaiorama”, the system prototype that was developed during the course of a research project and that is currently being used at the excavation site of Dispilio in Kastoria, Greece.