当前期刊: GeoInformatica Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • 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

    更新日期:2020-10-15
  • 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.

    更新日期:2020-09-25
  • 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

    更新日期:2020-09-18
  • 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

    更新日期:2020-08-23
  • 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

    更新日期:2020-08-15
  • 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

    更新日期:2020-08-08
  • 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

    更新日期:2020-08-02
  • 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

    更新日期:2020-07-24
  • 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;

    更新日期:2020-07-15
  • 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

    更新日期:2020-07-07
  • 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

    更新日期:2020-07-05
  • 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

    更新日期:2020-06-27
  • 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

    更新日期:2020-06-16
  • 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

    更新日期:2020-06-15
  • 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

    更新日期:2020-06-05
  • 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

    更新日期:2020-06-04
  • 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

    更新日期:2020-06-02
  • 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

    更新日期:2020-05-22
  • 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

    更新日期:2020-05-19
  • 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

    更新日期:2020-05-11
  • 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

    更新日期:2020-05-01
  • 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

    更新日期:2020-04-28
  • 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

    更新日期:2020-04-25
  • 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,

    更新日期:2020-03-16
  • 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

    更新日期:2020-03-12
  • 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

    更新日期:2020-03-02
  • 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

    更新日期:2020-02-26
  • 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

    更新日期:2020-02-12
  • 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

    更新日期:2020-02-12
  • 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

    更新日期:2020-01-14
  • 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

    更新日期:2020-01-11
  • 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

    更新日期:2020-01-09
  • 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

    更新日期:2020-01-06
  • 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

    更新日期:2020-01-04
  • 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

    更新日期:2019-11-30
  • 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

    更新日期:2019-09-10
  • 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

    更新日期:2019-07-25
  • 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

    更新日期:2019-07-23
  • 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

    更新日期:2019-07-17
  • 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

    更新日期:2019-07-16
  • 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

    更新日期:2019-07-08
  • 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

    更新日期:2019-07-04
  • 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

    更新日期:2019-06-21
  • 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

    更新日期:2019-06-21
  • 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

    更新日期:2019-06-06
  • 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

    更新日期:2019-05-25
  • 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

    更新日期:2019-05-22
  • 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

    更新日期:2019-05-21
  • 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

    更新日期:2019-05-15
  • 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

    更新日期:2019-05-14
  • 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

    更新日期:2019-05-10
  • 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

    更新日期:2019-05-08
  • 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

    更新日期:2019-05-07
  • 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,

    更新日期:2019-05-02
  • 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.

    更新日期:2019-05-02
  • Spatio-temporal mining of keywords for social media cross-social crawling of emergency events
    GeoInformatica (IF 1.774) Pub Date : 2019-05-01
    Andrea Autelitano; Barbara Pernici; Gabriele Scalia

    Being able to automatically extract as much relevant posts as possible from social media in a timely manner is key in many activities, for example to provide useful information in order to rapidly create crisis maps during emergency events. While most social media support keyword-based searches, the amount and the accuracy of retrieved posts depend largely on the keywords employed. The goal of the

    更新日期:2019-05-01
  • A versatile computational framework for group pattern mining of pedestrian trajectories
    GeoInformatica (IF 1.774) Pub Date : 2019-04-30
    Abdullah Sawas; Abdullah Abuolaim; Mahmoud Afifi; Manos Papagelis

    Mining patterns of large-scale trajectory data streams has been of increase research interest. In this paper, we are interested in mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining task that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. In particular, we focus on

    更新日期:2019-04-30
  • A dynamic approach for presenting local and global information in geospatial network visualizations
    GeoInformatica (IF 1.774) Pub Date : 2019-04-30
    Lingbo Zou; Stephen Brooks

    We present a dynamic approach for revealing the underlying information in locally cluttered areas within a geo-spatial connected graph while maintaining global edge trends. Two time series data-flow visualization approaches at both local and global scales are proposed respectively: a stream model focuses on data flows within the local area while a hub model addresses the relations between groups of

    更新日期:2019-04-30
  • Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility
    GeoInformatica (IF 1.774) Pub Date : 2019-04-27
    Tomas Crols; Nick Malleson

    The ambient population, i.e. the demographics and volume of people in a particular location throughout the day, has been studied less than the night-time residential population. Although the spatio-temporal behaviour of some groups, such as commuters, are captured in sources such as population censuses, much less is known about groups such as retired people who have less documented behaviour patterns

    更新日期:2019-04-27
  • Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models
    GeoInformatica (IF 1.774) Pub Date : 2019-04-26
    Shaheen A. Abdulkareem; Yaseen T. Mustafa; Ellen-Wien Augustijn; Tatiana Filatova

    Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a

    更新日期:2019-04-26
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
spring&清华大学出版社
城市可持续发展前沿研究专辑
Springer 纳米技术权威期刊征稿
全球视野覆盖
施普林格·自然新
chemistry
3分钟学术视频演讲大赛
物理学研究前沿热点精选期刊推荐
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
ACS Publications填问卷
阿拉丁试剂right
麻省大学
西北大学
湖南大学
华东师范大学
陆海华
化学所
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
胡眆昊
杨财广
廖矿标
试剂库存
down
wechat
bug