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

Mobile objects and sensors within a video surveillance system: Spatio-temporal model and queries

International audienceThe videos recorded by video surveillance systems represent a key element in a police inquiry. Based on a spatio-temporal query specified by a victim, (e.g., the trajectory of the victim before and after the aggression) the human operators select the cameras that could contain relevant information and analyse the corresponding video contents. This task becomes cumbersome because of the huge volume of video contents and the cameras' mobility. This paper presents an approach, which assists the operator in his task and reduces the research space. We propose to model the cameras' network (fixed and mobile cameras) on top of the city's transportation network. We consider the video surveillance system as a multilayer geographic information system, where the cameras are situated into a distinct layer, which is added on top of the other layers (e.g., roads, transport) and is related to them by the location. The model is implemented in a spatio-temporal database. Our final goal is that based on a spatio-temporal query to automatically extract the list of cameras (fixed and mobile) concerned by the query. We propose to include this automatically computed relative position of the cameras as an extension of the standard ISO 22311

Mobility Data Mining for Rural and Urban Map-Matching

Ajalis-ruumiliste andmete kogumine on hoogustunud erinevates rakendustes ja seadmetes. Globaalne positsiooneerimise süsteem (GPS) on kõige populaarsem viis asukoha teave saamiseks. Kaardipunktide vastavusse seadmine on konseptsioon, mis püüab GPS andmeid trajektooris viia vastavusse reaalse teedevõrguga. GPS andmete suurim probleem tuleneb andmete mõõtmis-ja kogumisvigadest ja nende parandamine on suur väljakutse. Käesoleva lõputöö eesmärk on arendada andmete töötlusvoo ja visualiseerimise raamistik muutmaks GPS punktid loogilisteks trajektoorideks ja vigaste GPS punktide asukohtade parandamiseks. Selle eesmärgi saavutamiseks tutvustatakse uut lähenemist trajektooride mustrite leidmiseks.The functionality of gathering spatio-temporal data has seen increasing usage in various applications and devices. The Global Positioning System (GPS) is a satellite navigation system which is mostly used for gathering location information. Map-matching is the procedure of matching trajectories from a sequence of raw GPS data points to the appropriate road networks. GPS data errors are one of the biggest problems and correcting them is a big challenge. The main goal of this thesis work is to build a data pipeline and visualization framework for turning raw GPS data to trajectories and correcting erroneous GPS points by new map-matching approach. For achieving the goal a new approach for trajectory pattern mining is introduced

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

Creating new connections: Objects, people, and digital data at the Musée du quai Branly

This article presents the new cultural heritage regime of the Musée du quai Branly’s collection through an analysis of the digitization of records for objects and documentary data. Non-European collections, notably those at the Musée de l’Homme, have been reconfigured within the new institution. The ‘ethnographic’ artifact, which was once a scientific object, has become a ‘work of art and civilization’ a heritage object registered at the Service des Musées de France. Documentary data, which used to be recorded on paper, was entered into a new classification system: the TMS objects database, here conceived as a social space and a space of knowledge shared by different actors. A study of the informational infrastructure shows how connections are created between humans, objects, and digital data. Analysis of this reconfiguration leads to an examination of the materiality of the digital environment. Classifications depend on material storage systems. Manipulating digital data allows for new working methods in creating an inventory, in particular new associations among data offering new possibilities for bringing together isolated objects and collections in unique ways.L’articolo presenta il nuovo regime patrimoniale delle collezioni del Musée du quai Branly attraverso l’analisi della digitalizzazione delle schede di documentazione e dell’informatizzazione dei processi di gestione degli oggetti. Le collezioni extra-europee, in particolare del Musée de l’Homme, sono state riconfigurate nella nuova istituzione. L’artefatto ‘etnografico’ da oggetto scientifico è diventato ‘un’opera d’arte e di civilizzazione’, un oggetto patrimoniale registrato al Service des Musées de France. I dati documentari cartacei sono stati rielaborati in un nuovo sistema di classificazione: il database TMS oggetti, concepito qui come uno spazio sociale e di conoscenza condiviso da diversi attori museali. Lo studio dell’infrastruttura informazionale mostra le nuove connessioni create tra umani, oggetti e dati digitali. L’analisi della riconfigurazione delle classificazioni mette in scena la materialità dell’ambiente digitale. La manipolazione dei dati digitali permette nuovi metodi di produzione dell’inventario, in particolare la nuova aggregazione dei dati induce a delle associazioni inedite tra oggetti e collezioni

Periodic pattern mining from spatio-temporal trajectory data

Rapid development in GPS tracking techniques produces a large number of spatio-temporal trajectory data. The analysis of these data provides us with a new opportunity to discover useful behavioural patterns. Spatio-temporal periodic pattern mining is employed to find temporal regularities for interesting places. Mining periodic patterns from spatio-temporal trajectories can reveal useful, important and valuable information about people's regular and recurrent movements and behaviours. Previous studies have been proposed to extract people's regular and repeating movement behavior from spatio-temporal trajectories. These previous approaches can target three following issues, (1) long individual trajectory; (2) spatial fuzziness; and (3) temporal fuzziness. First, periodic pattern mining is different to other pattern mining, such as association rule ming and sequential pattern mining, periodic pattern mining requires a very long trajectory from an individual so that the regular period can be extracted from this long single trajectory, for example, one month or one year period. Second, spatial fuzziness shows although a moving object can regularly move along the similar route, it is impossible for it to appear at the exactly same location. For instance, Bob goes to work everyday, and although he can follow a similar path from home to his workplace, the same location cannot be repeated across different days. Third, temporal fuzziness shows that periodicity is complicated including partial time span and multiple interleaving periods. In reality, the period is partial, it is highly impossible to occur through the whole movement of the object. Alternatively, the moving object has only a few periods, such as a daily period for work, or yearly period for holidays. However, it is insufficient to find effective periodic patterns considering these three issues only. This thesis aims to develop a new framework to extract more effective, understandable and meaningful periodic patterns by taking more features of spatio-temporal trajectories into account. The first feature is trajectory sequence, GPS trajectory data is temporally ordered sequences of geolocation which can be represented as consecutive trajectory segments, where each entry in each trajectory segment is closely related to the previous sampled point (trajectory node) and the latter one, rather than being isolated. Existing approaches disregard the important sequential nature of trajectory. Furthermore, they introduce both unwanted false positive reference spots and false negative reference spots. The second feature is spatial and temporal aspects. GPS trajectory data can be presented as triple data (x; y; t), x and y represent longitude and latitude respectively whilst t shows corresponding time in this location. Obviously, spatial and temporal aspects are two key factors. Existing methods do not consider these two aspects together in periodic pattern mining. Irregular time interval is the third feature of spatio-temporal trajectory. In reality, due to weather conditions, device malfunctions, or battery issues, the trajectory data are not always regularly sampled. Existing algorithms cannot deal with this issue but instead require a computationally expensive trajectory interpolation process, or it is assumed that trajectory is with regular time interval. The fourth feature is hierarchy of space. Hierarchy is an inherent property of spatial data that can be expressed in different levels, such as a country includes many states, a shopping mall is comprised of many shops. Hierarchy of space can find more hidden and valuable periodic patterns. Existing studies do not consider this inherent property of trajectory. Hidden background semantic information is the final feature. Aspatial semantic information is one of important features in spatio-temporal data, and it is embedded into the trajectory data. If the background semantic information is considered, more meaningful, understandable and useful periodic patterns can be extracted. However, existing methods do not consider the geographical information underlying trajectories. In addition, at times we are interested in finding periodic patterns among trajectory paths rather than trajectory nodes for different applications. This means periodic patterns should be identified and detected against trajectory paths rather than trajectory nodes for some applications. Existing approaches for periodic pattern mining focus on trajectories nodes rather than paths. To sum up, the aim of this thesis is to investigate solutions to these problems in periodic pattern mining in order to extract more meaningful, understandable periodic patterns. Each of three chapters addresses a different problem and then proposes adequate solutions to problems currently not addressed in existing studies. Finally, this thesis proposes a new framework to address all problems. First, we investigated a path-based solution which can target trajectory sequence and spatio-temporal aspects. We proposed an algorithm called Traclus (spatio-temporal) which can take spatial and temporal aspects into account at the same time instead of only considering spatial aspect. The result indicated our method produced more effective periodic patterns based on trajectory paths than existing node-based methods using two real-world trajectories. In order to consider hierarchy of space, we investigated existing hierarchical clustering approaches to obtain hierarchical reference spots (trajectory paths) for periodic pattern mining. HDBSCAN is an incremental version of DBSCAN which is able to handle clusters with different densities to generate a hierarchical clustering result using the single-linkage method, and then it automatically extracts clusters from a hierarchical tree. Thus, we modified traditional clustering method DBSCAN in Traclus (spatio-temporal) to HDBSCAN for extraction of hierarchical reference spots. The result is convincing, and reveals more periodic patterns than those of existing methods. Second, we introduced a stop/move method to annotate each spatio-temporal entry with a semantic label, such as restaurant, university and hospital. This method can enrich a trajectory with background semantic information so that we can easily infer people's repeating behaviors. In addition, existing methods use interpolation to make trajectory regular and then apply Fourier transform and autocorrelation to automatically detect period for each reference spot. An increasing number of trajectory nodes leads to an exponential increase of running time. Thus, we employed Lomb-Scargle periodogram to detect period for each reference spot based on raw trajectory without requiring any interpolation method. The results showed our method outperformed existing approaches on effectiveness and efficiency based on two real datasets. For hierarchical aspect, we extended previous work to find hierarchical semantic periodic patterns by applying HDBSCAN. The results were promising. Third, we apply our methodology to a case study, which reveals many interesting medical periodic patterns. These patterns can effectively explore human movement behaviors for positive medical outcomes. To sum up, this research proposed a new framework to gradually target the problems that existing methods cannot handle. These include: how to consider trajectory sequence, how to consider spatial temporal aspects together, how to deal with trajectory with irregular time interval, how to consider hierarchy of space and how to extract semantic information behind trajectory. After addressing all these problems, the experimental results demonstrate that our method can find more understandable, meaningful and effective periodic patterns than existing approaches

Bringing Together Species Observations: A Case Story of Sweden's Biodiversity Informatics Infrastructures

Biodiversity informatics produces global biodiversity knowledge through the collection and analysis of biodiversity data using informatics techniques. To do so, biodiversity informatics relies upon data accrual, standardization, transferability, openness, and "invisible" infrastructure. What biodiversity informatics mean to society, however, cannot be adequately understood without recognizing what organizes biodiversity data. Using insights from science and technology studies, we story the organizing "visions" behind the growth of biodiversity informatics infrastructures in Sweden-an early adopter of digital technologies and significant contributor to global biodiversity data-through interviews, scientific literature, governmental reports and popular publications. This case story discloses the organizational formation of Swedish biodiversity informatics infrastructures from the 1970s to the present day, illustrating how situated perspectives or "visions" shaped the philosophies, directions and infrastructures of its biodiversity informatics communities. Specifically, visions related to scientific progress and species loss, their institutionalization, and the need to negotiate external interests from governmental organizations led to unequal development across multiple infrastructures that contribute differently to biodiversity knowledge. We argue that such difference highlights that the social and organizational hurdles for combining biodiversity data are just as significant as the technological challenges and that the seemingly inconsequential organizational aspects of its infrastructure shape what biodiversity data can be brought together, modelled and visualised