Erlend Tøssebro

An Advanced Discrete Model for Uncertain Spatial Data

This paper presents a discrete model for storing uncertain spatial data in a database. It expands on earlier works on modelling uncertain and vague regions as well as an abstract model of uncertain spatial data developed by the present authors. The model presented in this paper is based on a vector representation of the spatial data and covers uncertain points, uncertain lines and uncertain regions as well as uncertain versions of base types such as integers. Three alternative methods for computing the probabilities associated with uncertain spatial data are included. This paper also describes how to implement two operations with these types.

Representing resuscitation data—Considerations on efficient analysis of quality of cardiopulmonary resuscitation

OBJECTIVE To discuss challenges in representing resuscitation data from Utstein style reports and devices like defibrillators with focus on unified and efficient handling of variety of resuscitation research objectives. METHODS AND RESULTS Information on therapy such as shock delivery, chest compressions and ventilation can be extracted from defibrillators. A method for merging this information with cardiac rhythm annotated from ECGs, yields a representation of the resuscitation episode with cardiac rhythm also giving information on response to therapy. These data should be synchronised to an electronic Utstein report. With modern technology for communicating information it is possible to structure, store and transport data flexibly so that data captured with devices from different manufacturers can be combined. CONCLUSIONS A scheme for representing resuscitation data should combine essential information stored in different locations after a resuscitation attempt. The resulting representation should enable data analysis to enable studies of the relationship between therapy and patient response. As the complexity and amount of data generated during resuscitation efforts are ever increasing, the time is mature for using modern information technology tools to provide infrastructure for efficient data management and analysis to identify and meet future challenges in resuscitation data analysis.

Representing topological relationships for spatiotemporal objects

Several representations have been created to store topological information in normal spatial databases. Some work has also been done to represent topology for 3D objects, and such representations could be used to store topology for spatiotemporal objects. However, using 3D models has some disadvantages with regards to retrieving snapshots of the database. This paper aims at creating a spatiotemporal version of the sliced representation that supports efficient retrieval of snapshots of the past and that supports enforcing topological relationships. This paper aims to extend an earlier representation of moving objects so that it can also store and enforce some of the topological relationships between the objects. One use of such a representation is storing a changing spatial partition. As part of the effort to construct the model, an analysis of the topological relationships has been carried out to see which need to be stored explicitly and which can be computed from geometry. Both a basic time slice model and a 3D model are examined to determine how suitable they are for storing topological relationships. An extension of the time slice model is then proposed that solves some of the problems of the basic time slice model. Some algorithms for constructing the new model from snapshots of the objects along with an adjacency graph have been created. The paper also contains a short analysis on how to handle current time, as the time slice model is best at handling historical data, and on ways to speed up searches in a database in which objects of many types are connected to one another and many files therefore potentially need to be accessed.

A medium complexity discrete model for uncertain spatial data

This paper presents a method for representing uncertainty in spatial data in a database. The model presented requires moderate amounts of storage space. To compute the probability that an object is at a particular place, the representation employs probability functions that can be computed quickly and efficiently. This is different from an advanced model presented by the same authors. This medium complexity model is less powerful, but requires much less storage space, and computing probabilities is much less complicated.

A Discrete Model for Topological Relationships between Uncertain Spatial Objects

Even though the positions of objects may be uncertain, one may know some topological information about them. In this paper, we develop a new model for storing topological relationships in uncertain spatial data. It is intended to be the equivalent of such representations as the Node-Arc-Area representation but for spatial objects with uncertain positions.

Abstract and discrete models for uncertain spatiotemporal data

Summary form only given. We deal with the uncertainty in spatial and spatiotemporal databases. Due to lack of accurate measurements, or rapid changes in time, spatial and spatiotemporal data are often uncertain. Our work presents new abstract and discrete models for uncertain spatial and spatiotemporal information. The models are based on the principle that one knows that the uncertain object, regardless of type, must be within a certain area.

Using ontologies to integrate and share resuscitation data from diverse medical devices

OBJECTIVE To propose a method for standardised data representation and demonstrate a technology that makes it possible to translate data from device dependent formats to this standard representation format. METHODS AND RESULTS Outcome statistics vary between emergency medical systems organising resuscitation services. Such differences indicate a potential for improvement by identifying factors affecting outcome, but data subject to analysis have to be comparable. Modern technology for communicating information makes it possible to structure, store and transfer data flexibly. Ontologies describe entities in the world and how they relate. Letting different computer systems refer to the same ontology results in a common understanding on data content. Information on therapy such as shock delivery, chest compressions and ventilation should be defined and described in a standardised ontology to enable comparison and combining data from diverse sources. By adding rules and logic data can be merged and combined in new ways to produce new information. An example ontology is designed to demonstrate the feasibility and value of such a standardised structure. CONCLUSIONS The proposed technology makes possible capturing and storing of data from different devices in a structured and standardised format. Data can easily be transformed to this standardised format, compared and combined independent of the original structure.

Representing topological relationships for moving objects

Moving objects databases managing spatial objects with continuously changing position and extent over time have recently found large interest in the database community. Queries about moving objects become particularly interesting when they ask for temporal changes in the topological relationships between evolving spatial objects. A concept of spatio-temporal predicates has been proposed to describe these relationships. The goal of this paper is to design efficient algorithms for them so that they can be used in spatio-temporal joins and selections. This paper proposes not to design an algorithm for each new predicate individually but to employ a generic algorithmic scheme, which is able to cover present and future predicate definitions.Several representations have been created to store topological information in normal spatial databases. However, not that much work has been done to store such relationships for spatiotemporal data. This paper extends the representation of moving objects from [7] so that it can also store and enforce some of the topological relationships between the objects. This is done in a fashion similar to the Node-Arc-Area model for normal spatial databases. One use of such a rep resentation is storing a changing spatial partition.

ELECTRONIC BEHAVIOUR MAPPING AND GIS APPLICATION FOR STAVANGER TORGET, NORWAY

In Norway, there is a growing interest in urban life as opposed to previously urban space. For urban planners and designers this means that methods of spatial registration and analysis need to be extended to include user behaviour, perception and experience. Therefore, user observation and behavioural surveys have become more prominent. To this end, behaviour mapping can be used as a tool to investigate the current use of a space. While manual behaviour mapping has limitations, this paper describes the development and testing of a GIS-based application using electronic maps for registration and analysis of observed behaviour in an urban public space. The behaviour mapping application has been tested and used in a study of Stavanger Torget, the most central public space in Stavanger, Norway. The collection and analysis of data was executed to investigate the amount of users and types of activities in the space throughout the week. Here, the electronic map application proved to be helpful in terms of making registration more efficient, instantly generating digital maps of the observations and providing tabular data for further analysis. This strongly improves urban analysis with regard to behavioural observation and makes related data collection and analysis much more efficient. This ultimately allows for the creation of a GIS database regarding the relationship between physical characteristics and user behaviour, something that is particularly relevant with the growing awareness for quality in public space and urban life.

Interpolating Probability Values or Fuzzy Set Values for Uncertain Spatiotemporal Objects

This paper looks at ways of quickly interpolating probability values or fuzzy set values for uncertain spatiotemporal objects that may change continuously over time. The paper starts with presenting a way to compute a tetrahedralization of an uncertain spatiotemporal object and using that to compute consistent interpolations. This approach also turns out to be able to create fairly good interpolations of the shape of the spatiotemporal object without needing an extra algorithm for this purpose. However, a naïve use of any tetrahedralization turns out to create interpolation artifacts in those objects that become significantly more or less uncertain with time. The paper then presents a way to overcome this issue at the cost of more processing.