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Distributed mining of convoys in large scale datasets GeoInformatica (IF 1.774) Pub Date : 2021-02-24 Faisal Orakzai, Torben Bach Pedersen, Toon Calders
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
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From reanalysis to satellite observations: gap-filling with imbalanced learning GeoInformatica (IF 1.774) Pub Date : 2021-01-07 Jingze Lu, Kaijun Ren, Xiaoyong Li, Yanlai Zhao, Zichen Xu, Xiaoli Ren
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
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Finding the most navigable path in road networks GeoInformatica (IF 1.774) Pub Date : 2021-01-03 Ramneek Kaur, Vikram Goyal, Venkata M. V. Gunturi
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
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Hidden Markov map matching based on trajectory segmentation with heading homogeneity GeoInformatica (IF 1.774) Pub Date : 2021-01-02 Ge Cui, Wentao Bian, Xin Wang
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
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Spatiotemporal event sequence discovery without thresholds GeoInformatica (IF 1.774) Pub Date : 2020-11-09 Berkay Aydin, Soukaina Filali Boubrahimi, Ahmet Kucuk, Bita Nezamdoust, Rafal A. Angryk
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
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Probabilistic forecasting using deep generative models GeoInformatica (IF 1.774) Pub Date : 2020-10-14 Alessandro Fanfarillo, Behrooz Roozitalab, Weiming Hu, Guido Cervone
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
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Correction to: MongoDB Vs PostgreSQL: a comparative study on performance aspects GeoInformatica (IF 1.774) Pub Date : 2020-09-25 Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dimitrios Zissis, Dimosthenis Anagnostopoulos
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.
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Scalable enrichment of mobility data with weather information GeoInformatica (IF 1.774) Pub Date : 2020-09-17 Nikolaos Koutroumanis, Georgios M. Santipantakis, Apostolos Glenis, Christos Doulkeridis, George A. Vouros
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
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Route intersection reduction with connected autonomous vehicles GeoInformatica (IF 1.774) Pub Date : 2020-08-23 Sadegh Motallebi, Hairuo Xie, Egemen Tanin, Jianzhong Qi, Kotagiri Ramamohanarao
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
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MTLM: a multi-task learning model for travel time estimation GeoInformatica (IF 1.774) Pub Date : 2020-08-15 Saijun Xu, Ruoqian Zhang, Wanjun Cheng, Jiajie Xu
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
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Building navigation networks from multi-vessel trajectory data GeoInformatica (IF 1.774) Pub Date : 2020-08-07 Iraklis Varlamis, Ioannis Kontopoulos, Konstantinos Tserpes, Mohammad Etemad, Amilcar Soares, Stan Matwin
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
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AP-GAN: Adversarial patch attack on content-based image retrieval systems GeoInformatica (IF 1.774) Pub Date : 2020-08-02 Guoping Zhao, Mingyu Zhang, Jiajun Liu, Yaxian Li, Ji-Rong Wen
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
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Dynamic top-k influence maximization in social networks GeoInformatica (IF 1.774) Pub Date : 2020-07-24 Binbin Zhang, Hao Wang, Leong Hou U
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
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Size constrained k simple polygons GeoInformatica (IF 1.774) Pub Date : 2020-07-14 KwangSoo Yang, Kwang Woo Nam, Ahmad Qutbuddin, Aaron Reich, Valmer Huhn
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;
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An information fusion approach for conflating labeled point-based time-series data GeoInformatica (IF 1.774) Pub Date : 2020-07-07 Zion Schell, Ashok Samal, Leen-Kiat Soh
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
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Cost estimation of spatial join in spatialhadoop GeoInformatica (IF 1.774) Pub Date : 2020-07-05 A. Belussi, S. Migliorini, A. Eldawy
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
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JS4Geo: a canonical JSON Schema for geographic data suitable to NoSQL databases GeoInformatica (IF 1.774) Pub Date : 2020-06-27 Angelo A. Frozza, Ronaldo dos S. Mello
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
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A secure location-based alert system with tunable privacy-performance trade-off. GeoInformatica (IF 1.774) Pub Date : 2020-06-16 Gabriel Ghinita,Kien Nguyen,Mihai Maruseac,Cyrus Shahabi
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
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SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels GeoInformatica (IF 1.774) Pub Date : 2020-06-15 Mohammad Etemad, Amilcar Soares, Elham Etemad, Jordan Rose, Luis Torgo, Stan Matwin
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
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MongoDB Vs PostgreSQL: A comparative study on performance aspects GeoInformatica (IF 1.774) Pub Date : 2020-06-05 Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dimitrios Zissis, Dimosthenis Anagnostopoulos
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
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A framework for evaluating 3D topological relations based on a vector data model GeoInformatica (IF 1.774) Pub Date : 2020-06-04 Alberto Belussi, Sara Migliorini, Mauro Negri
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
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SHAREK*: A Scalable Matching Method for Dynamic Ride Sharing GeoInformatica (IF 1.774) Pub Date : 2020-06-02 Bin Cao, Chenyu Hou, Liwei Zhao, Louai Alarabi, Jing Fan, Mohamed F. Mokbel, Anas Basalamah
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
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Mitigating Spoofed GNSS Trajectories through Nature Inspired Algorithm GeoInformatica (IF 1.774) Pub Date : 2020-05-22 Saravjeet Singh, Jaiteg Singh, Sukhjit Singh
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
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Affine-invariant querying of spatial data using a triangle-based logic GeoInformatica (IF 1.774) Pub Date : 2020-05-19 Sofie Haesevoets, Bart Kuijpers, Peter Z. Revesz
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
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A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification GeoInformatica (IF 1.774) Pub Date : 2020-05-11 Jing Lv, Huimin Zhang, Ming Yang, Wanqi Yang
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
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Road network simplification for location-based services GeoInformatica (IF 1.774) Pub Date : 2020-05-01 Abdeltawab Hendawi, John A. Stankovic, Ayman Taha, Shaker El-Sappagh, Amr A. Ahmadain, Mohamed Ali
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
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SST: Synchronized Spatial-Temporal Trajectory Similarity Search GeoInformatica (IF 1.774) Pub Date : 2020-04-28 Peng Zhao, Weixiong Rao, Chengxi Zhang, Gong Su, Qi Zhang
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
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A Framework for Group Converging Pattern Mining using Spatiotemporal Trajectories GeoInformatica (IF 1.774) Pub Date : 2020-04-25 Bin Zhao, Xintao Liu, Jinping Jia, Genlin Ji, Shengxi Tan, Zhaoyuan Yu
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
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Learning evolving user’s behaviors on location-based social networks GeoInformatica (IF 1.774) Pub Date : 2020-03-16 Ruizhi Wu; Guangchun Luo; Qi Jin; Junming Shao; Chang-Tien Lu
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,
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A template-based approach for the specification of 3D topological constraints GeoInformatica (IF 1.774) Pub Date : 2020-03-12 Alberto Belussi; Sara Migliorini; Mauro Negri; Giuseppe Pelagatti
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
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Building socially-enabled event-enriched maps GeoInformatica (IF 1.774) Pub Date : 2020-03-02 Faizan Ur Rehman; Imad Afyouni; Ahmed Lbath; Sohaib Khan; Saleh Basalamah
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
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Map construction algorithms: a local evaluation through hiking data GeoInformatica (IF 1.774) Pub Date : 2020-02-26 David Duran; Vera Sacristán; Rodrigo I. Silveira
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
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Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network GeoInformatica (IF 1.774) Pub Date : 2020-02-12 Adil Alim; Aparna Joshi; Feng Chen; Catherine T. Lawson
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
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Top- k spatial distance joins GeoInformatica (IF 1.774) Pub Date : 2020-02-12 Shuyao Qi, Panagiotis Bouros, Nikos Mamoulis
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
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Generalized communication cost efficient multi-way spatial join: revisiting the curse of the last reducer GeoInformatica (IF 1.774) Pub Date : 2020-01-14 S. Nagesh Bhattu; Avinash Potluri; Prashanth Kadari; Subramanyam R. B. V.
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
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Influences of different underground station map designs on map-reading and wayfinding GeoInformatica (IF 1.774) Pub Date : 2020-01-11 Meng-Cong Zheng
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
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Enhancing local live tweet stream to detect news GeoInformatica (IF 1.774) Pub Date : 2020-01-09 Hong Wei; Jagan Sankaranarayanan; Hanan Samet
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
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An innovative multi-label learning based algorithm for city data computing GeoInformatica (IF 1.774) Pub Date : 2020-01-06 Mengqing Mei; Yongjian Zhong; Fazhi He; Chang Xu
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
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Toward volume preserving spheroid degenerated-octree grid GeoInformatica (IF 1.774) Pub Date : 2020-01-04 Benjamin Ulmer; Faramarz Samavati
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
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Graph simulation on large scale temporal graphs GeoInformatica (IF 1.774) Pub Date : 2019-11-30 Yuliang Ma; Ye Yuan; Meng Liu; Guoren Wang; Yishu Wang
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
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Local trend discovery on real-time microblogs with uncertain locations in tight memory environments GeoInformatica (IF 1.774) Pub Date : 2019-09-10 Abdulaziz Almaslukh; Amr Magdy; Ahmed M. Aly; Mohamed F. Mokbel; Sameh Elnikety; Yuxiong He; Suman Nath; Walid G. Aref
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
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Online flu epidemiological deep modeling on disease contact network GeoInformatica (IF 1.774) Pub Date : 2019-07-25 Liang Zhao; Jiangzhuo Chen; Feng Chen; Fang Jin; Wei Wang; Chang-Tien Lu; Naren Ramakrishnan
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
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Efficient matching of offers and requests in social-aware ridesharing GeoInformatica (IF 1.774) Pub Date : 2019-07-23 Xiaoyi Fu; Ce Zhang; Hua Lu; Jianliang Xu
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
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Joint hyperspectral unmixing for urban computing GeoInformatica (IF 1.774) Pub Date : 2019-07-17 Jihai Yang; Mingmei Jia; Chang Xu; Shijun Li
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
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Annotating semantic tags of locations in location-based social networks GeoInformatica (IF 1.774) Pub Date : 2019-07-16 Yanhui Li; Xiangguo Zhao; Zhen Zhang; Ye Yuan; Guoren Wang
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
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S 2 R-tree: a pivot-based indexing structure for semantic-aware spatial keyword search GeoInformatica (IF 1.774) Pub Date : 2019-07-08 Xinyu Chen; Jiajie Xu; Rui Zhou; Pengpeng Zhao; Chengfei Liu; Junhua Fang; Lei Zhao
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
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Spatial keyword search: a survey GeoInformatica (IF 1.774) Pub Date : 2019-07-04 Lisi Chen; Shuo Shang; Chengcheng Yang; Jing Li
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
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Preference-aware sequence matching for location-based services GeoInformatica (IF 1.774) Pub Date : 2019-06-21 Hao Wang; Ziyu Lu
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
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Continuous decaying of telco big data with data postdiction GeoInformatica (IF 1.774) Pub Date : 2019-06-21 Constantinos Costa; Andreas Konstantinidis; Andreas Charalampous; Demetrios Zeinalipour-Yazti; Mohamed F. Mokbel
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
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A spatially-pruned vertex expansion operator in the Neo4j graph database system GeoInformatica (IF 1.774) Pub Date : 2019-06-06 Yuhan Sun; Mohamed Sarwat
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
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Behavior-based location recommendation on location-based social networks GeoInformatica (IF 1.774) Pub Date : 2019-05-25 Seyyed Mohammadreza Rahimi; Behrouz Far; Xin Wang
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
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ITISS: an efficient framework for querying big temporal data GeoInformatica (IF 1.774) Pub Date : 2019-05-22 Zhongpu Chen; Bin Yao; Zhi-Jie Wang; Wei Zhang; Kai Zheng; Panos Kalnis; Feilong Tang
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
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Crosstown traffic - supervised prediction of impact of planned special events on urban traffic GeoInformatica (IF 1.774) Pub Date : 2019-05-21 Nicolas Tempelmeier; Stefan Dietze; Elena Demidova
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
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Collective spatial keyword search on activity trajectories GeoInformatica (IF 1.774) Pub Date : 2019-05-15 Xiaozhao Song; Jiajie Xu; Rui Zhou; Chengfei Liu; Kai Zheng; Pengpeng Zhao; Nickolas Falkner
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
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Efficient framework for processing top-k queries with replication in mobile ad hoc networks GeoInformatica (IF 1.774) Pub Date : 2019-05-14 Yuya Sasaki; Takahiro Hara; Yoshiharu Ishikawa
This article addresses the top-k query processing problem on mobile ad hoc networks (MANETs). Top-k query processing is common to retrieve only highly important data items. However, methods for top-k query processing are not enough efficient and accurate in MANET environments. For improving the efficiency and accuracy, replication is a promising technique that each node in MANETs replicates data items
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A hybrid CNN-LSTM model for typhoon formation forecasting GeoInformatica (IF 1.774) Pub Date : 2019-05-10 Rui Chen; Xiang Wang; Weimin Zhang; Xiaoyu Zhu; Aiping Li; Chao Yang
A typhoon is an extreme weather event that can cause huge loss of life and economic damage in coastal areas and beyond. As a consequence, the search for more accurate predictive models of typhoon formation; and, intensity have become imperative as meteorologists, governments, and other agencies seek to mitigate the impact of these catastrophic events. While work in this field has progressed diligently
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Two-sided online bipartite matching in spatial data: experiments and analysis GeoInformatica (IF 1.774) Pub Date : 2019-05-08 Yiming Li; Jingzhi Fang; Yuxiang Zeng; Balz Maag; Yongxin Tong; Lingyu Zhang
With the rapid development of sharing economy and mobile Internet in recent years, a wide range of applications of the Two-sidedOnlineBipartiteMatching (TOBM) problem in spatial data are gaining increasing popularity. To be specific, given a group of workers and tasks that dynamically appear in a 2D space, the TOBM problem aims to find a matching with the maximum cardinality between workers and tasks
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Regularized topic-aware latent influence propagation in dynamic relational networks GeoInformatica (IF 1.774) Pub Date : 2019-05-07 Shuhui Wang; Liang Li; Chenxue Yang; Qingming Huang
On social networks, investigating how the influence is propagated is crucial in understanding the network evolution and the social impact of different topics. In previous study, the influence propagation is either modeled based on the static network structure, or the infection between two connected users is recovered from some given event cascades. Unfortunately, existing solutions are incapable of
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On the composition and recommendation of multi-feature paths: a comprehensive approach GeoInformatica (IF 1.774) Pub Date : 2019-05-02 Vincenzo Cutrona; Federico Bianchi; Michele Ciavotta; Andrea Maurino
Trackers have become popular devices these days. They are extensively used to record sports activities (e.g., hiking, skiing), mainly in terms of GPS trajectories, which can be shared on social networking platforms with other users looking for leisure tips. Notably, as the number of available trajectories drastically increased over time, in many cases, it has become challenging, if not impossible,
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Correction to: FeaturEyeTrack: automatic matching of eye tracking data with map features on interactive maps GeoInformatica (IF 1.774) Pub Date : 2019-05-02 Fabian Göbel, Peter Kiefer, Martin Raubal
The original version of this article unfortunately contained a mistake. Figure 10a and b were interchanged during the publishing process.
Contents have been reproduced by permission of the publishers.