Top-k Route Search through Submodularity Modeling of Recurrent POI Features

We consider a practical top-k route search problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination and limited in a cost budget, that maximally match her needs on feature preferences. One challenge is dealing with the personalized diversity requirement where users have various trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features). Another challenge is the large scale of the POI map and the great many alternative routes to search. We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints. We also present promising heuristic solutions and evaluate all the solutions on real life data.Comment: 11 pages, 7 figures, 2 table

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Mobile Map Browsers: Anticipated User Interaction for Data Pre-fetching

When browsing a graphical display of geospatial data on mobile devices, users typically change the displayed maps by panning, zooming in and out, or rotating the device. Limited storage space on mobile devices and slow wireless communications, however, impede the performance of these operations. To overcome the bottleneck that all map data to be displayed on the mobile device need to be downloaded on demand, this thesis investigates how anticipated user interactions affect intelligent pre-fetching so that an on-demand download session is extended incrementally. User interaction is defined as a set of map operations that each have corresponding effects on the spatial dataset required to generate the display. By anticipating user interaction based on past behavior and intuition on when waiting for data is acceptable, it is possible to device a set of strategies to better prepare the device with data for future use. Users that engage with interactive map displays for a variety of tasks, whether it be navigation, information browsing, or data collection, experience a dynamic display to accomplish their goal. With vehicular navigation, the display might update itself as a result of a GPS data stream reflecting movement through space. This movement is not random, especially as is the case of moving vehicles and, therefore, this thesis suggests that mobile map data could be pre-fetched in order to improve usability. Pre-fetching memory-demanding spatial data can benefit usability in several ways, but in particular it can (1) reduce latency when downloading data over wireless connections and (2) better prepare a device for situations where wireless internet connectivity is weak or intermittent. This thesis investigates mobile map caching and devises an algorithm for pre-fetching data on behalf of the application user. Two primary models are compared: isotropic (direction-independent) and anisotropic (direction-dependent) pre-fetching. A prefetching simulation is parameterized with many trajectories that vary in complexity (a metric of direction change within the trajectory) and it is shown that, although anisotropic pre-fetching typically results in a better pre-fetching accuracy, it is not ideal for all scenarios. This thesis suggests a combination of models to accommodate the significant variation in moving object trajectories. In addition, other methods for pre-fetching spatial data are proposed for future research

Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis

Ph.DDOCTOR OF PHILOSOPH

Yellow Tree: A Distributed Main-memory Spatial Index Structure for Moving Objects

Mobile devices equipped with wireless technologies to communicate and positioning systems to locate objects of interest are common place today, providing the impetus to develop location-aware applications. At the heart of location-aware applications are moving objects or objects that continuously change location over time, such as cars in transportation networks or pedestrians or postal packages. Location-aware applications tend to support the tracking of very large numbers of such moving objects as well as many users that are interested in finding out about the locations of other moving objects. Such location-aware applications rely on support from database management systems to model, store, and query moving object data. The management of moving object data exposes the limitations of traditional (spatial) database management systems as well as their index structures designed to keep track of objects\u27 locations. Spatial index structures that have been designed for geographic objects in the past primarily assume data are foremost of static nature (e.g., land parcels, road networks, or airport locations), thus requiring a limited amount of index structure updates and reorganization over a period of time. While handling moving objects however, there is an incumbent need for continuous reorganization of spatial index structures to remain up to date with constantly and rapidly changing object locations. This research addresses some of the key issues surrounding the efficient database management of moving objects whose location update rate to the database system varies from 1 to 30 minutes. Furthermore, we address the design of a highly scaleable and efficient spatial index structure to support location tracking and querying of large amounts of moving objects. We explore the possible architectural and the data structure level changes that are required to handle large numbers of moving objects. We focus specifically on the index structures that are needed to process spatial range queries and object-based queries on constantly changing moving object data. We argue for the case of main memory spatial index structures that dynamically adapt to continuously changing moving object data and concurrently answer spatial range queries efficiently. A proof-of concept implementation called the yellow tree, which is a distributed main-memory index structure, and a simulated environment to generate moving objects is demonstrated. Using experiments conducted on simulated moving object data, we conclude that a distributed main-memory based spatial index structure is required to handle dynamic location updates and efficiently answer spatial range queries on moving objects. Future work on enhancing the query processing performance of yellow tree is also discussed

Selective Trajectory Memory Network andits application in Vehicle DestinationPrediction

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. Cho, Sungzoon.Predicting efficiently the final destinations of moving vehicles can be of significant usefulness for several applications. Many probabilistic methods have been developed to address it but often include heavy feature engineering and do not generalize well to new datasets. To face these limitations, Deep-Learning models present the advantage of automating processing steps and can therefore be easily adapted to new input data. De Brébisson et al. proposed clustering based deep-learning approaches to solve it in the specific case of the prediction of Taxis destinations with remarkable performances, alongside with a proposition of a novel architecture inspired by Memory-Networks used in Natural Language Processing, and requiring no preliminary clustering. A large room for improvement was however left for the latter approach : the necessity of a relevant selection function retrieving historical trajectories similar to partial trips to predict was indeed outlined by the authors. In this work we propose to use the Segment-Path distance, introduced by Besse et al. in former works on trajectory clustering, to come up with an improved architecture of this memory model. A review of several Memory Networks architecture and their applications in time-series prediction is provided to give an overview of the different structural alternatives existing for the design of our model architecture. Finally, our model is confronted to individual car data and we propose a personalized user-by-user prediction of destinations. We discuss the suitability and limits of the type of model in this specific problem and conclude that the promising obtained results are penalized by infrequent destinations cases inducing noise whose effect could be reduced by turning our approach into a classification problem.Abstract i Contents List of Tables vi List of Figures viii Chapter 1 Introduction 1 1.1 Motivations, background . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Description : destination forecasting problem . . . . . . . . 2 1.2.1 General context . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Specific problem tackled . . . . . . . . . . . . . . . . . . . . . 2 1.3 Existing models and methods . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Research Motivation and Contributions . . . . . . . . . . . . . . . . 6 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Related works 8 2.1 Artificial neural network models for trajectory prediction . . . . . . 8 2.1.1 Encoding and clustering approach . . . . . . . . . . . . . . . 8 2.1.2 "Memory network" model for taxi trajectory prediction . . . 11 2.2 Memory networks and applications . . . . . . . . . . . . . . . . . . . 13 2.2.1 MemNN models . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 End-to-end memory networks (MemN2N) . . . . . . . . . . . 16 2.2.3 Memory networks for multi-dimensional time-series forecasting (MTNnet) . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Analogies and comparisons between the memory models introduced . 19 2.4 Distances measures for vehicle trajectories . . . . . . . . . . . . . . . 22 2.4.1 Segment-Path Distance (SPD) . . . . . . . . . . . . . . . . . 23 2.5 Personalized predictions on car manufacturer data . . . . . . . . . . 26 2.5.1 Problem approach and redefinition . . . . . . . . . . . . . . . 26 2.5.2 Method and model . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 3 Proposed Model 28 3.1 Overall architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Memory storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Trajectory encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Encoding architecture . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Metadata and embedding . . . . . . . . . . . . . . . . . . . . 31 3.4.3 Distinctions between encoders, weight-sharing . . . . . . . . . 31 3.5 Memory selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.1 Attention mechanism . . . . . . . . . . . . . . . . . . . . . . 32 3.5.2 Data used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.6 Query-memory association . . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Final prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Chapter 4 Experiments 35 4.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.1 Variability and predictability . . . . . . . . . . . . . . . . . . 36 4.2.2 Considered vehicles . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 Experimental settings . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1 Training and testing set . . . . . . . . . . . . . . . . . . . . . 39 4.3.2 Test methodology and parameters . . . . . . . . . . . . . . . 40 4.3.3 Baseline model : simple encoding . . . . . . . . . . . . . . . . 42 4.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.1 General results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.2 Factors of influence on models performances . . . . . . . . . . 45 4.4.3 Case studies : 5 example vehicles analysis . . . . . . . . . . . 49 4.4.4 Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Chapter 5 Conclusion 56 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Bibliography 58 감사의 글 62Maste

L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response

Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy concerns by collecting sensitive locations. A strong privacy model ''local differential privacy'' (LDP) has been recently deployed in many different applications (e.g., Google RAPPOR, iOS, and Microsoft Telemetry) but not effective for LBS applications due to the low utility of existing LDP mechanisms. To address such deficiency, we propose the first LDP framework for a variety of location-based services (namely ''L-SRR''), which privately collects and analyzes user locations with high utility. Specifically, we design a novel randomization mechanism ''Staircase Randomized Response'' (SRR) and extend the empirical estimation to significantly boost the utility for SRR in different LBS applications (e.g., traffic density estimation, and k-nearest neighbors). We have conducted extensive experiments on four real LBS datasets by benchmarking with other LDP schemes in practical applications. The experimental results demonstrate that L-SRR significantly outperforms them.Comment: accepted to CCS'22; full versio

Conditional heavy hitters : detecting interesting correlations in data streams

The notion of heavy hitters—items that make up a large fraction of the population—has been successfully used in a variety of applications across sensor and RFID monitoring, network data analysis, event mining, and more. Yet this notion often fails to capture the semantics we desire when we observe data in the form of correlated pairs. Here, we are interested in items that are conditionally frequent: when a particular item is frequent within the context of its parent item. In this work, we introduce and formalize the notion of conditional heavy hitters to identify such items, with applications in network monitoring and Markov chain modeling. We explore the relationship between conditional heavy hitters and other related notions in the literature, and show analytically and experimentally the usefulness of our approach. We introduce several algorithm variations that allow us to efficiently find conditional heavy hitters for input data with very different characteristics, and provide analytical results for their performance. Finally, we perform experimental evaluations with several synthetic and real datasets to demonstrate the efficacy of our methods and to study the behavior of the proposed algorithms for different types of data

Distributed Data Management in Vehicular Networks Using Mobile Agents

En los últimos años, las tecnologías de la información y las comunicaciones se han incorporado al mundo de la automoción gracias a sus avances, y han permitido la creación de dispositivos cada vez más pequeños y potentes. De esta forma, los vehículos pueden ahora incorporar por un precio asequible equipos informáticos y de comunicaciones.En este escenario, los vehículos que circulan por una determinada zona (como una ciudad o una autopista) pueden comunicarse entre ellos usando dispositivos inalámbricos que les permiten intercambiar información con otros vehículos cercanos, formando así una red vehicular ad hoc, o VANET (Vehicular Ad hoc Network). En este tipo de redes, las comunicaciones se establecen con conexiones punto a punto por medio de dispositivos tipo Wi-Fi, que permiten la comunicación con otros del mismo tipo dentro de su alcance, sin que sea necesaria la existencia previa de una infraestructura de comunicaciones como ocurre con las tecnologías de telefonía móvil (como 3G/4G), que además requieren de una suscripción y el pago de una tarifa para poder usarlas.Cada vehículo puede enviar información y recibirla de diversos orígenes, como el propio vehículo (por medio de los sensores que lleva incorporados), otros vehículos que se encuentran cerca, así como de la infraestructura de tráfico presente en las carreteras (como semáforos, señales, paneles electrónicos de información, cámaras de vigilancia, etc.). Todos estas fuentes pueden transmitir datos de diversa índole, como información de interés para los conductores (por ejemplo, atascos de tráfico o accidentes en la vía), o de cualquier otro tipo, mientras sea posible digitalizarla y enviarla a través de una red.Todos esos datos pueden ser almacenados localmente en los ordenadores que llevan los vehículos a medida que son recibidos, y sería muy interesante poder sacarles partido por medio de alguna aplicación que los explotara. Por ejemplo, podrían utilizarse los vehículos como plataformas móviles de sensores que obtengan datos de los lugares por los que viajan. Otro ejemplo de aplicación sería la de ayudar a encontrar plazas de aparcamiento libres en una zona de una ciudad, usando la información que suministrarían los vehículos que dejan una plaza libre.Con este fin, en esta tesis se ha desarrollado una propuesta de la gestión de datos basada en el uso de agentes móviles para poder hacer uso de la información presente en una VANET de forma eficiente y flexible. Esta no es una tarea trivial, ya que los datos se encuentran dispersos entre los vehículos que forman la red, y dichos vehículos están constantemente moviéndose y cambiando de posición. Esto hace que las conexiones de red establecidas entre ellos sean inestables y de corta duración, ya que están constantemente creándose y destruyéndose a medida que los vehículos entran y salen del alcance de sus comunicaciones debido a sus movimientos.En un escenario tan complicado, la aproximación que proponemos permite que los datos sean localizados, y que se puedan hacer consultas sobre ellos y transmitirlos de un sitio cualquiera de la VANET a otro, usando estrategias multi-salto que se adaptan a las siempre cambiantes posiciones de los vehículos. Esto es posible gracias a la utilización de agentes móviles para el procesamiento de datos, ya que cuentan con una serie de propiedades (como su movilidad, autonomía, adaptabilidad, o inteligencia), que hace que sean una elección muy apropiada para este tipo de entorno móvil y con un elevado grado de incertidumbre.La solución propuesta ha sido extensamente evaluada y probada por medio de simulaciones, que demuestran su buen rendimiento y fiabilidad en redes vehiculares con diferentes condiciones y en diversos escenarios.<br /