Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically. / Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly. / Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy.

<p>In a distributed real-time environment where it is imperative to make correct decisions it is important to have all facts available to make the most accurate decision in a certain situation. An example of such an environment is an Unmanned Aerial Vehicle (UAV) system where several UAVs cooperate to carry out a certain task and the data recorded is analyzed after the completion of the mission. This project aims to define and implement a time series architecture for use together with a distributed real-time database for the ability to store temporal data. The result from this project is a time series (TS) architecture that uses DeeDS, a distributed real-time database, for storage. The TS architecture is used by an application modelled from a UAV scenario for storing temporal data. The temporal data is produced by a simulator. The TS architecture solves the problem of storing temporal data for applications using DeeDS. The TS architecture is also useful as a foundation for integrating time series in DeeDS since it is designed for space efficiency and real-time requirements.</p>

This dissertation is in the area of pervasive computing. It focuses on designing platforms for storing, querying, and computing contextual information. More specifically, we are interested in platforms for storing and querying spatio-temporal events where queries exhibit locality. Recent advances in sensor technologies have made possible gathering a variety of information on the status of users, the environment machines, etc. Combining this information with computation we are able to extract context, i.e., a filtered high-level description of the situation. In many cases, the information gathered exhibits locality both in space and time, i.e., an event is likely to be consumed in a location close to the location where the event was produced, at a time whic h is close to the time the event was produced. This dissertation builds on this observation to create better platforms for computing context. We claim three key contributions. We have studied the problem of designing and optimizing spatial organizations for exchanging context. Our thesis has original theoretical work on how to create a platform based on cells of a Voronoi diagram for optimizing the energy and bandwidth required for mobiles to exchange contextual information t hat is tied to specific locations in the platform. Additionally, we applied our results to the problem of optimizing a system for surveilling the locations of entities within a given region. We have designed a platform for storing and querying spatio-temporal events exhibiting locality. Our platform is based on a P2P infrastructure of peers organized based on the Voronoi diagram associated with their locations to store events based on their own associated locations. We have developed theoretical results based on spatial point processes for the delay experienced by a typical query in this system. Additionally, we used simulations to study heuristics to improve the performance of our platform. Finally, we came up with protocols for the replicated storage of events in order to increase the fault-tolerance of our platform. Finally, in this thesis we propose a design for a platform, based on RFID tags, to support context-aware computing for indoor spaces. Our platform exploits the structure found in most indoor spaces to encode contextual information in suitably designed RFID tags. The elements of our platform collaborate based on a set of messages we developed to offer context-aware services to the users of the platform. We validated our research with an example hardware design of the RFID tag and a software emulation of the tag's functionality. / text

Disertaciniame darbe nagrinėjama erdvės – laiko duomenų modeliavimo problema. Dažnai erdvinių duomenų rinkiniai yra gana nedideli, o taškai, kuriuose pasklidę stebėjimai, išsidėstę netaisyklingai. Sprendžiant „erdvinį“ uždavinį, paprastai siekiama inerpoliuoti arba įvertinti erdvinį vidurkį. Laiko eilučių duomenys dažniausiai naudojami ateities reikšmėms prognozuoti. Tuo tarpu erdvės – laiko uždaviniai jungia abu uždavinių tipus. Pasiūlyta keletas originalių erdvinių laiko eilučių modeliavimo metodų. Siūlomi metodai pirmiausia analizuoja vienmates laiko eilutes, o pašalinus laikinę priklausomybė jose, laiko eilučių liekanoms vertinama erdvinė priklausomybė. Tikslas – sudaryti modelį, leidžiantį prognozuoti požymio reikšmę naujame, nestebėtame taške, nauju laiko momentu. Tokio modelio sudarymas remiasi laiko eilučių parametrų erdviniu interpoliavimu. / Space – time data modeling problem is analysed. Often spatial data sets are relatively small, and the points, where observations are taken, are located irregularly. When solving spatial task, usually we are interpolating or estimating the spatial average. Time series data usually are used to predict future values. Meanwhile, the space - time tasks combines both types of tasks. Few original modeling methods of spatial time series are proposed. The proposed methods firstly analyzes the univariate time series, and after removing temporal dependence, spatial dependence in the time series of residuals is measured. Aim of this dissertational work - to create time series model at new unobserved location by incorporating spatial interaction thru spatial interpolation of estimated time series parameters. Such a model is based on the spatial interpolation of time series parameters.

The construction industry has consistently suffered the highest number of fatalities among all human involved industries over the years. Safety managers struggle to prevent injuries and fatalities by monitoring at-risk behavior exhibited by workers and equipment operators. Current methods of identifying and reporting potential hazards on site involve periodic manual inspection, which depends upon personal judgment, is prone to human error, and consumes enormous time and resources. This research presents a framework for automatic identification and analysis of potential hazards by analyzing spatio-temporal data from construction resources. The scope of the research is limited to human-equipment interactions in outdoor construction sites involving ground workers and heavy equipment. A grid-based mapping technique is developed to quantify and visualize potentially hazardous regions caused by resource interactions on a construction site. The framework is also implemented to identify resources that are exposed to potential risk based on their interaction with other resources. Cases of proximity and blind spots are considered in order to create a weight-based scoring approach for mapping hazards on site. The framework is extended to perform ``what-if'' safety analysis for operation planning by iterating through multiple resource configurations. The feasibility of using both real and simulated data is explored. A sophisticated data management and operation analysis platform and a cell-based simulation engine are developed to support the process. This framework can be utilized to improve on-site safety awareness, revise construction site layout plans, and evaluate the need for warning or training workers and equipment operators. It can also be used as an education and training tool to assist safety managers in making better, more effective, and safer decisions.

In a distributed real-time environment where it is imperative to make correct decisions it is important to have all facts available to make the most accurate decision in a certain situation. An example of such an environment is an Unmanned Aerial Vehicle (UAV) system where several UAVs cooperate to carry out a certain task and the data recorded is analyzed after the completion of the mission. This project aims to define and implement a time series architecture for use together with a distributed real-time database for the ability to store temporal data. The result from this project is a time series (TS) architecture that uses DeeDS, a distributed real-time database, for storage. The TS architecture is used by an application modelled from a UAV scenario for storing temporal data. The temporal data is produced by a simulator. The TS architecture solves the problem of storing temporal data for applications using DeeDS. The TS architecture is also useful as a foundation for integrating time series in DeeDS since it is designed for space efficiency and real-time requirements.

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

Temporal data is common in real-world datasets. Analysis of such data, for example by means of clustering algorithms, can be difficult due to its dynamic behaviour. There are various types of changes that may occur to clusters in a dataset. Firstly, data patterns can migrate between clusters, shrinking or expanding the clusters. Additionally, entire clusters may move around the search space. Lastly, clusters can split and merge. Data clustering, which is the process of grouping similar objects, is one approach to determine relationships among data patterns, but data clustering approaches can face limitations when applied to temporal data, such as difficulty tracking the moving clusters. This research aims to analyse the ability of particle swarm optimisation (PSO) and differential evolution (DE) algorithms to cluster temporal data. These algorithms experience two weaknesses when applied to temporal data. The first weakness is the loss of diversity, which refers to the fact that the population of the algorithm converges, becoming less diverse and, therefore, limiting the algorithm’s exploration capabilities. The second weakness, outdated memory, is only experienced by the PSO and refers to the previous personal best solutions found by the particles becoming obsolete as the environment changes. A data clustering algorithm that addresses these two weaknesses is necessary to cluster temporal data. This research describes various adaptations of PSO and DE algorithms for the purpose of clustering temporal data. The algorithms proposed aim to address the loss of diversity and outdated memory problems experienced by PSO and DE algorithms. These problems are addressed by combining approaches previously used for the purpose of dealing with temporal or dynamic data, such as repulsion and anti-convergence, with PSO and DE approaches used to cluster data. Six PSO algorithms are introduced in this research, namely the data clustering particle swarm optimisation (DCPSO), reinitialising data clustering particle swarm optimisation (RDCPSO), cooperative data clustering particle swarm optimisation (CDCPSO), multi-swarm data clustering particle swarm optimisation (MDCPSO), cooperative multi-swarm data clustering particle swarm optimisation (CMDCPSO), and elitist cooperative multi-swarm data clustering particle swarm optimisation (eCMDCPSO). Additionally, four DE algorithms are introduced, namely the data clustering differential evolution (DCDE), re-initialising data clustering differential evolution (RDCDE), dynamic data clustering differential evolution (DCDynDE), and cooperative dynamic data clustering differential evolution (CDCDynDE). The PSO and DE algorithms introduced require prior knowledge of the total number of clusters in the dataset. The total number of clusters in a real-world dataset, however, is not always known. For this reason, the best performing PSO and best performing DE are compared. The CDCDynDE is selected as the winning algorithm, which is then adapted to determine the optimal number of clusters dynamically. The resulting algorithm is the k-independent cooperative data clustering differential evolution (KCDCDynDE) algorithm, which was compared against the local network neighbourhood artificial immune system (LNNAIS) algorithm, which is an artificial immune system (AIS) designed to cluster temporal data and determine the total number of clusters dynamically. It was determined that the KCDCDynDE performed the clustering task well for problems with frequently changing data, high-dimensions, and pattern and cluster data migration types. / Dissertation (MSc)--University of Pretoria, 2015. / Computer Science / Unrestricted

As cities grow, efficient public transport systems are becoming increasingly important. To offer a more efficient service, public transport providers use systems that predict arrival times of buses, trains and similar vehicles, and present this information to the general public. The accuracy and reliability of these predictions are paramount, since many people depend on them, and erroneous predictions reflect badly on the public transport provider. When public transport vehicles move throughout the cities, they create motion patterns, which describe how their positions change over time. This thesis proposes a way of modeling their motion patterns using Gaussian processes, and investigates whether it is possible to predict the arrival times of public transport buses in Linköping based on their motion patterns. The results are evaluated by comparing the accuracy of the model with a simple baseline model and a recurrent neural network (RNN), and the results show that the proposed model achieves superior performance to that of an RNN trained on the same amounts of data, with excellent explainability and quantifiable uncertainty. However, an RNN is capable of training on much more data than the proposed model in the same amount of time, so in a scenario with large amounts of data the RNN outperforms the proposed model.

Congestion is a major problem in most metropolitan areas and given the increasingrate of urbanization it is likely to be an even more serious problem in the rapidlyexpanding mega cities. One possible method to combat congestion is to provide in-telligent traffic management systems that can in a timely manner inform drivers aboutcurrent or predicted traffic congestions that are relevant to them on their journeys. Thedetection of traffic congestion and the determination of whom to send in advance no-tifications about the detected congestions is the objective of the present research. Byadopting a grid based discretization of space, the proposed system extracts and main-tains traffic flow statistics and mobility statistics from the grid based recent trajectoriesof moving objects, and captures periodical spatio-temporal changes in the traffic flowsand movements by managing statistics for relevant temporal domain projections, i.e.,hour-of-day and day-of-week. Then, the proposed system identifies a directional con-gestion as a cell and its immediate neighbor, where the speed and flow of the objectsthat have moved from the neighbor to the cell significantly deviates from the histori-cal speed and flow statistics. Subsequently, based on one of two notification criteria,namely, Mobility Statistic Criterion (MSC) and Linear Movement Criterion (LMC),the system decides which objects are likely to be affected by the identified conges-tions and sends out notifications to the corresponding objects such that the numberof false negative (missed) and false positive (unnecessary) notifications is minimized.The thesis discusses the design and DBMS-based implementation of the proposedsystem. Empirical evaluations on realistically simulated trajectory data assess the ac-curacy of the methods and test the scalability of the system for varying input sizes andparameter settings. The accuracy assessment results show that the MSC based systemachieves an optimal performance with a true positive notification rate of 0.67 and afalse positive notification rate of 0.05 when min prob equals to 0.35, which is superiorto the performance of the LMC based system. The execution time of- and the spaceused by the system scales linearly with the input size (number of concurrently movingvehicles) and the methods mutually dependent parameters (grid resolution r and RTlength l) that jointly define a spatio-temporal resolution. Within the area of a large  city (40km by 40km), assuming a 60km/h average vehicle speed, the system, runningon a commodity personal computer, can manage the described congestion detectionand three-minute-ahead notification tasks within real-time requirements for 2000 and20000 concurrently moving vehicles for spatio-temporal resolutions (r=100m, l=19)and (r=2km, l=3), respectively.