Action Stories for Counter Terrorism (extended abstract)

Due to the raised terrorist threat worldwide, there is an urgent need to research that assists security and police services to protect the public and key assets and to prevent attacks from taking place. Successful protection and prevention may require potential and known suspects to be monitored or arrested. These operations are high risk because inappropriate surveillance, interview or arrest may have damaging political, public relations and intelligence effects. In addition to better tracking information on which to base suspicions, the security and police services need to have confidence that operations will yield evidence that can demonstrate conclusively that a deceptive activity such as a terrorist attack was in the process of being planned or executed before an operation takes place

Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen