Efficient MaxCount and threshold operators of moving objects

Calculating operators of continuously moving objects presents some unique challenges, especially when the operators involve aggregation or the concept of congestion, which happens when the number of moving objects in a changing or dynamic query space exceeds some threshold value. This paper presents the following six d-dimensional moving object operators: (1) MaxCount (or MinCount), which finds the Maximum (or Minimum) number of moving objects simultaneously present in the dynamic query space at any time during the query time interval. (2) CountRange, which finds a count of point objects whose trajectories intersect the dynamic query space during the query time interval. (3) ThresholdRange, which finds the set of time intervals during which the dynamic query space is congested. (4) ThresholdSum, which finds the total length of all the time intervals during which the dynamic query space is congested. (5) ThresholdCount, which finds the number of disjoint time intervals during which the dynamic query space is congested. And (6) ThresholdAverage, which finds the average length of time of all the time intervals when the dynamic query space is congested. For these operators separate algorithms are given to find only estimate or only precise values. Experimental results from more than 7,500 queries indicate that the estimation algorithms produce fast, efficient results with error under 5%

Linking Moving Object Databases with Ontologies

This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies with databases. A major benefit gained from this kind of linking is the augmentation of database knowledge and multi-granular perspectives that are provided by ontologies through the process of generalization. Methods are presented for linking based on a military transportation scenario where data on vehicle position is collected from a sensor network and stored in a geosensor database. An ontology linking tool, implemented as a stand alone application, is introduced. This application associates individual values from the geosensor database with classes from a military transportation device ontology and returns linked value-class pairs to the user as a set of equivalence relations (i.e., matches). This research also formalizes a set of motion relations between two moving objects on a road network. It is demonstrated that the positional data collected from a geosensor network and stored in a spatio-temporal database, can provide a foundation for computing relations between moving objects. Configurations of moving objects, based on their spatial position, are described by motion relations that include isBehind and inFrontOf. These relations supply a user context about binary vehicle positions relative to a reference object. For example, the driver of a military supply truck may be interested in knowing what types of vehicles are in front of the truck. The types of objects that participate in these motion relations correspond to particular classes within the military transportation device ontology. This research reveals that linking a geosensor database to the military transportation device ontology will facilitate more abstract or higher-level perspectives of these moving objects, supporting inferences about moving objects over multiple levels of granularity. The details supplied by the generalization of geosensor data via linking, helps to interpret semantics and respond to user questions by extending the preliminary knowledge about the moving objects within these relations

Une approche pour supporter l'analyse qualitative des suites d'actions dans un environnement géographique virtuel et dynamique : l'analyse " What-if " comme exemple

Nous proposons une approche basée sur la géosimulation multi-agent et un outil d’aide à la décision pour supporter l’analyse « What-if » durant la planification des suites d’actions (plans) dans un environnement géographique dynamique. Nous présentons les caractéristiques du raisonnement « What-if » en tant 1) que simulation mentale 2) suivant un processus en trois étapes et 3) basé sur du raisonnement causal qualitatif. Nous soulignons les limites de la cognition humaine pour appliquer ce raisonnement dans le cadre de la planification des suites d’actions dans un environnement géographique dynamique et nous identifions les motivations de notre recherche. Ensuite, nous présentons notre approche basée sur la géosimulation multi-agent et nous identifions ses caractéristiques. Nous traitons en particulier trois problématiques majeures. La première problématique concerne la modélisation des phénomènes géographiques dynamiques. Nous soulignons les limites des approches existantes et nous présentons notre modèle basé sur le concept de situation spatio-temporelle que nous représentons en utilisant le formalisme de graphes conceptuels. En particulier, nous présentons comment nous avons défini ce concept en nous basant sur les archétypes cognitifs du linguiste J-P. Desclés. La deuxième problématique concerne la transformation des résultats d’une géosimulation multi-agent en une représentation qualitative exprimée en termes de situations spatio-temporelles. Nous présentons les étapes de traitement de données nécessaires pour effectuer cette transformation. La troisième problématique concerne l’inférence des relations causales entre des situations spatio-temporelles. En nous basant sur divers travaux traitant du raisonnement causal et de ses caractéristiques, nous proposons une solution basée sur des contraintes causales spatio-temporelles et de causalité pour établir des relations de causation entre des situations spatio-temporelles. Finalement, nous présentons MAGS-COA, une preuve de concept que nous avons implémentée pour évaluer l’adéquation de notre approche comme support à la résolution de problèmes réels. Ainsi, les principales contributions de notre travail sont: 1- Une approche basée sur la géosimulation multi-agent pour supporter l’analyse « What-if » des suites d’actions dans des environnements géographiques virtuels. 2- L’application d’un modèle issu de recherches en linguistique à un problème d’intérêt pour la recherche en raisonnement spatial. 3- Un modèle qualitatif basé sur les archétypes cognitifs pour modéliser des situations dynamiques dans un environnement géographique virtuel. 4- MAGS-COA, une plateforme de simulation et d’analyse qualitative des situations spatio-temporelles. 5- Un algorithme pour l’identification des relations causales entre des situations spatio-temporelles.We propose an approach and a tool based on multi-agent geosimulation techniques in order to support courses of action’s (COAs) “What if” analysis in the context of dynamic geographical environments. We present the characteristics of “What if” thinking as a three-step mental simulation process based on qualitative causal reasoning. We stress humans’ cognition limits of such a process in dynamic geographical contexts and we introduce our research motivations. Then we present our multi-agent geosimulation-based approach and we identify its characteristics. We address next three main problems. The first problem concerns modeling of dynamic geographical phenomena. We stress the limits of existing models and we present our model which is based on the concept of spatio-temporal situations. Particularly, we explain how we define our spatio-temporal situations based on the concept of cognitive archetypes proposed by the linguist J-P. Desclés. The second problem consists in transforming the results of multi-agent geosimulations into a qualitative representation expressed in terms of spatio-temporal situations and represented using the conceptual graphs formalism. We present the different steps required for such a transformation. The third problem concerns causal reasoning about spatio-temporal situations. In order to address this problem, we were inspired by works of causal reasoning research community to identify the constraints that must hold to identify causal relationships between spatio-temporal situations. These constraints are 1) knowledge about causality, 2) temporal causal constraints and 3) spatial causal constraints. These constraints are used to infer causal relationships among the results of multi-agent geosimulations. Finally, we present MAGS-COA, a proof on concept that we implemented in order to evaluate the suitability of our approach as a support to real problem solving. The main contributions of this thesis are: 1- An approach based on multi-agent geosimulation to support COA’s “What if” analysis in the context of virtual geographic environments. 2- The application of a model proposed in the linguistic research community to a problem of interest to spatial reasoning research community. 3- A qualitative model based on cognitive archetypes to model spatio-temporal situations. 4- MAGS-COA, a platform of simulation and qualitative analysis of spatio-temporal situations. 5- An algorithm to identify causal relationships between spatio-temporal 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.