Location recommendation methods suggest unvisited locations to their users. Many existing location recommendation methods focus on the spatial, social and temporal aspects of human movements. However, contextual information is also invaluable to location recommendation methods and has the great potential for explaining what triggers users to show different behaviors. CLR learns the response of the

Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior

During the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we focus on data compression techniques with the intention to minimize the size of trajectory data, while, at the same time, minimizing the impact on the

Given a transportation network, a population, and a set of destinations, the goal of Conflict-Free Evacuation Route Planning (CF-ERP) is to produce routes that minimize the evacuation time for the population with no spatiotemporal movement-conflicts. The CF-ERP problem is an essential component of civic emergency preparedness in the wake of man-made or natural disasters (e.g., terrorist acts, hurricanes

Fast and precise querying in a given set of trajectory points is an important issue of trajectory query. Typically, there are massive trajectory data in the database, yet the query sets only have a few points, which is a challenge for the superior performance of trajectory querying. The current trajectory query methods commonly use the tree-based index structure and the signature-based method to classify

In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor

Tremendous increase in the use of the mobile devices equipped with the GPS and other location sensors has resulted in the generation of a huge amount of movement data. In recent years, mining this data to understand the collective mobility behavior of humans, animals and other objects has become popular. Numerous mobility patterns, or their mining algorithms have been proposed, each representing a

Increasing the spatial coverage and temporal resolution of Earth surface monitoring can significantly improve forecasting or monitoring capabilities in the context of smart city, such as extreme weather forecasting, ecosystem monitoring and anthropogenic impact monitoring. As an essential data source for Earth’s surface monitoring, most satellite observations exist data gaps due to various factors

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source

Map matching is to locate GPS trajectories onto the road networks, which is an important preprocessing step for many applications based on GPS trajectories. Currently, hidden Markov model is one of the most widely used methods for map matching. However, both effectiveness and efficiency of conventional map matching methods based on hidden Markov model will decline in the dense road network, as the

Spatiotemporal event sequences (STESs) are the ordered series of event types whose instances frequently follow each other in time and are located close-by. An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time. Previous studies on STES mining require significance and prevalence thresholds for the

A fundamental problem in Numerical Weather Prediction (NWP) is the generation of ensembles to capture the probability of future states of the atmosphere. This research presents a new methodology to generate analogs using deep generative models, an emerging class of deep learning approaches. The goal is to train a deep generative model using a set of historical forecasts and associated observations

The article “MongoDB Vs PostgreSQL: A comparative study on performance aspects”, written by Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dimitrios Zissis, Dimosthenis Anagnostopoulos, was originally published electronically on the publisher’s internet portal on 05 June 2020 without open access.

More and more real-life applications for mobility analytics require the joint exploitation of positional information of moving objects together with weather data that correspond to the movement. In particular, this is evident in fleet management applications for improved routing and reduced fuel consumption, in the maritime domain for more accurate trajectory prediction, as well as in air-traffic management

A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated

Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the

Building a rich and informative model from raw data is a hard but valuable process with many applications. Ship routing and scheduling are two essential operations in the maritime industry that can save a lot of resources if they are optimally designed, but still, need a lot of information to be successful. Past and recent works in the field assume the availability of information such as the birth

Key Smart City applications such as traffic management and public security rely heavily on the intelligent processing of video and image data, often in the form of visual retrieval tasks, such as person Re-IDentification (ReID) and vehicle re-identification. For these tasks, Deep Neural Networks (DNNs) have been the dominant solution for the past decade, for their remarkable ability in learning discriminative

The problem of top-k influence maximization is to find the set of k users in a social network that can maximize the spread of influence under certain influence propagation model. This paper studies the influence maximization problem together with network dynamics. For example, given a real-life social network that evolves over time, we want to find k most influential users on everyday basis. This dynamic

Given a geometric space and a set of weighted spatial points, the Size Constrained k Simple Polygons (SCkSP) problem identifies k simple polygons that maximize the total weights of the spatial points covered by the polygons and meet the polygon size constraint. The SCkSP problem is important for many societal applications including hotspot area detection and resource allocation. The problem is NP-hard;

In geographic data analysis, it is often the case that multiple aspects of a single phenomenon are captured by different sources of data. For instance, a storm can be identified based on its precipitation, as well as windspeed, and changes in barometric pressure. It proves beneficial in specific domains to be able to use all available sources of data, and some method must be used to integrate all of

Spatial join is an important operation in geo-spatial applications, since it is frequently used for performing data analysis involving geographical information. Many efforts have been done in the past decades in order to provide efficient algorithms for spatial join and this becomes particularly important as the amount of spatial data to be processed increases. In recent years, the MapReduce approach

The large volume and variety of data produced in the current Big Data era lead companies to seek solutions for the efficient data management. Within this context, NoSQL databases rise as a better alternative to the traditional relational databases, mainly in terms of scalability and availability of data. A usual feature of NoSQL databases is to be schemaless, i.e., they do not impose a schema or have

Monitoring location updates from mobile users has important applications in many areas, ranging from public health (e.g., COVID-19 contact tracing) and national security to social networks and advertising. However, sensitive information can be derived from movement patterns, thus protecting the privacy of mobile users is a major concern. Users may only be willing to disclose their locations when some

Trajectory mining aims to provide fundamental insights into decision-making tasks related to moving objects. A fundamental pre-processing step for trajectory mining is trajectory segmentation, where a raw trajectory is divided into several meaningful consecutive sub-sequences. In this work, we propose an unsupervised trajectory segmentation algorithm, Sliding Window Segmentation (SWS), that processes

Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated

3D topological relations are commonly used for testing or imposing the existence of desired properties between objects of a dataset, such as a city model. Currently available GIS systems usually provide a limited 3D support which usually includes a set of 3D spatial data types together with few operations and predicates, while limited or no support is generally provided for 3D topological relations

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several

Advancement in technology has resulted in the easy sharing of locations across various stakeholders. Unprotected sharing of location information makes any Global Navigation Satellite System (GNSS) device vulnerable to spoofing attacks. Spoofed GNSS signals propagate misleading trajectories to cripple any Location-Based Service (LBS). This manuscript introduces a novel algorithm for the detection and

If the same land area is photographed from two different angles, then a computer can recognize the two images to show the same land area by proving that the images are affine-invariant. In practice, images are abstracted as a type of spatial data that is a finite set of triangles. Hence we propose a simple affine-generic query language with variables over finite sets of triangles. The proposed query

The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized

Road-network data compression or simplification reduces the size of the network to occupy less storage with the aim to fit small form-factor routing devices, mobile devices, or embedded systems. Simplification (a) reduces the storage cost of memory and disks, and (b) reduces the I/O and communication overhead. There are several road network compression techniques proposed in the literature. These techniques

The volume of trajectory data has become tremendously large in recent years. How to effectively and efficiently search similar trajectories has become an important task. Firstly, to measure the similarity between a trajectory and a query, literature works compute spatial similarity and temporal similarity independently, and next sum the two weighted similarities. Thus, two trajectories with high spatial

A group event such as human and traffic congestion can be very roughly divided into three stages: converging stage before congestion, gathered stage when congestion happens, and dispersing stage that congestion disappears. It is of great interest in modeling and identifying converging behaviors before gathered events actually happen, which helps to proactively predict and handle potential public incidents

With the popularity of smart phones, users’ activities on location-based social networks (LBSNs) evolve faster than traditional social networks. Existing models focus on modeling users’ long-term preferences, leveraging social collaborative filtering to enhance prediction performance. However, the dynamic mobility mechanism of user’s check-in behaviors on LBSNs is seldom considered. In this paper,

Several different models have been defined in literature for the definition of 3D scenes that include a geometrical representation of objects together with a semantical classification of them. Such semantical characterization encapsulates important details about the object properties and behavior and often includes spatial relations that are defined only implicitly or through natural language, such

With the advancement of social sensing technologies, digital maps have recently witnessed a tremendous evolution with the aim of integrating enriched semantic layers from heterogeneous and diverse data sources. Current generations of digital maps are often crowd-sourced, allow interactive route planning, and may contain live updates, such as traffic congestion states. Within this context, we believe

We study five existing map construction algorithms, designed and tested with urban vehicle data in mind, and apply them to hiking trajectories with different terrain characteristics. Our main goal is to better understand the existing strategies and their limitations, in order to shed new light into the current challenges for map construction algorithms. We carefully analyze the results obtained by

Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks. Weather contributed to 23 percent of all non-reoccurring delay and approximately 544 million vehicle hours of delay each year (2014). Nearly 2.3 billion dollars each year are spent by transportation agencies for winter maintenance that contribute to close to 20 percent of most DOT’s yearly

Top-k joins have been extensively studied when numerical valued attributes are joined on an equality predicate. Other types of join attributes and predicates have received little to no attention. In this paper, we consider spatial objects that are assigned a score (e.g., a ranking). Give two collections R, S of such objects and a spatial distance threshold 𝜖, we introduce the top-k spatial distance

With the huge increase in usage of smart mobiles, social media and sensors, large volumes of location-based data is available. Location based data carries important signals pertaining to user intensive information as well as population characteristics. The key analytical tool for location based analysis is multi-way spatial join. Unlike the conventional join strategies, multi-way join using map-reduce

Map-reading and wayfinding form one continuous and indivisible process; however, numerous studies have only focused on one of the two. This study focused on the relationship between map-reading and wayfinding to understand how map users read and acquire information from maps. Thirty Participants were divided into three groups of ten on Shibuya Station in Tokyo. The first group used mounted maps, the

Twitter captures invaluable information about real-world news, spanning a wide scale from large national/international stories like a presidential election to small local stories such as a local farmers market. Detecting and extracting small news for a local place is a challenging problem and the focus of this work. The main challenge lies in identifying these small stories that correspond to a local

Investigating correlation between example features and example labels is essential to the solving of classification problems. However, identification and calculation of the correlation between features and labels can be rather difficult in case involving high-dimensional multi-label data. Both feature embedding and label embedding have been developed to tackle this challenge, and a shared subspace

Conventional Discrete Global Grid Systems are well suited for storing and indexing data on the Earth’s surface, but not for data above and below the surface. To properly support volumetric data, a 3D version of this data structure is needed. One promising approach for this is the Spheroid Degenerate-Octree Grid (SDOG), first proposed by Yu and Wu in 2009. Compared to other methods, this grid does a

Graph simulation is one of the most important queries in graph pattern matching, and it is being increasingly used in various applications, e.g., protein interaction networks, software plagiarism detection. Most previous studies mainly focused on the simulation problem on static graphs, which neglected the temporal factors in daily life. In this paper, we investigate a novel problem, namely, temporal

This paper presents GeoTrend+; a system approach to support scalable local trend discovery on recent microblogs, e.g., tweets, comments, online reviews, and check-ins, that come in real time. GeoTrend+ discovers top-k trending keywords in arbitrary spatial regions from recent microblogs that continuously arrive with high rates and a significant portion has uncertain geolocations. GeoTrend+ distinguishes

The surveillance and preventions of infectious disease epidemics such as influenza and Ebola are important and challenging issues. It is therefore crucial to characterize the disease progress and epidemics process efficiently and accurately. Computational epidemiology can model the progression of the disease and its underlying contact network, but as yet lacks the ability to process of real-time and

Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort and trust issues, have not been fully considered in the existing works. In this paper, we formulate a new problem

Recently, many methods for hyperspectral unmixing have been proposed. These methods are often based on nonnegative matrix factorization (NMF), which naturally inherits the non-negative advantage and is in line with the common sense of physics. Although there are many ways to perform NMF-based hyperspectral unmixing, these methods can only unmix one hyperspectral image at a time. In practice, we may

Location-based social networks (LBSNs) have become popular platforms that allow users to share their check-in activities with friends. Annotating semantic tags of locations, as one of the hottest research topics in LBSNs, has attracted considerable attention. Semantic annotation requires sufficient location features to train classifiers. Based on the analysis of LBSN data, we find that users’ check-in

Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure S2R-tree to integrate spatial and semantic

Spatial keyword search has been playing an indispensable role in personalized route recommendation and geo-textual information retrieval. In this light, we conduct a survey on existing studies of spatial keyword search. We categorize existing works of spatial keyword search based on the types of their input data, output results, and methodologies. For each category, we summarize their common features

Sequantial data are important in many real world location based services. In this paper, we study the problem of sequence matching. Specifically, we want to identify the sequences most similar to a given sequence, under three most commonly used preferece-aware similarity measures, i.e., Fagin’s intersection metric, Kendall’s tau, and Spearman’s footrule. We first analyze the properties of these three

In this paper, we present two novel decaying operators for Telco Big Data (TBD), coined TBD-DP and CTBD-DP that are founded on the notion of Data Postdiction. Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which does not exist anymore as it had to be deleted

Graphs are widely used to model data in many application domains. Thanks to the wide spread use of GPS-enabled devices, many applications assign spatial attributes to graph vertexes (e.g., geographic knowledge bases, geo-tagged social media). Graph database systems such as Neo4j and Titan are commonly used to manage and query graph data. Even though an off-the-shelf graph database system allows users

Location recommendation methods on location-based social networks (LBSN) discover the locational preference of users along with their spatial movement patterns from users’ check-ins and provide users with recommendations of unvisited places. The growing popularity of LBSNs and abundance of shared location information has made location recommendation an active research area in the recent years. However

In the real word, temporal data can be found in many applications, and it is rapidly increasing nowadays. It is urgently important and challenging to manage and operate big temporal data efficiently and effectively, due to the large volume of big temporal data and the real-time response requirement. Processing big temporal data using a distributed system is a desired choice, since a single-machine

Large-scale planned special events in cities including concerts, football games and fairs can significantly impact urban mobility. The lack of reliable models for understanding and predicting mobility needs during urban events causes issues for mobility service users, providers as well as urban planners. In this article, we tackle the problem of building reliable supervised models for predicting the

Collective spatial keyword query (CSKQ) is one of the most useful spatial queries in location-based service systems. Although the availability of large-scale activity trajectories has given us useful knowledge of users’ behavior, existing activity trajectory search methods are unable to support CSKQ queries reasonably. This paper studies effective and efficient CSKQ processing on activity trajectories