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GeoInformatica
Journal Prestige (SJR): 0.479
Citation Impact (citeScore): 3
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1384-6175 - ISSN (Online) 1573-7624
Published by Springer-Verlag Homepage  [2467 journals]
  • A topology-based approach to individual tree segmentation from airborne
           LiDAR data

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      Abstract: Abstract Light Detection and Ranging (LiDAR) sensors emit laser signals to calculate distances based on the time delay of the returned laser pulses. They can generate dense point clouds to map forest structures at a high level of spatial resolution. In this work, we consider the problem of segmenting out individual trees in Airborne Laser Scanning (ALS) point clouds. Several techniques have been proposed for this purpose which generally require time-consuming parameter tuning and intense user interaction. Our goal is to design an automated, intuitive, and robust approach requiring minimal user interaction. To this aim, we define a new segmentation approach based on topological tools, namely on the watershed transform and on persistence-based simplification. The approach follows a divide-and-conquer paradigm, splitting a LiDAR point cloud into regions with uniform densities. Our algorithm is validated on coniferous forests collected in the NEW technologies for a better mountain FORest timber mobilization (NEWFOR) dataset, and deciduous forests collected in the Smithsonian Environmental Research Center (SERC) dataset. When compared to four state-of-the-art tree segmentation algorithms, our method performs best in both ecosystem types. It provides more accurate stem estimations and single tree segmentation results at various of stem and point densities. Also, our method requires only a single (Boolean) parameter, which makes it extremely easy to use and very promising for various forest analysis applications, such as biomass estimation and field inventory surveys.
      PubDate: 2023-01-28
       
  • Test-data generation and integration for long-distance e-vehicle routing

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      Abstract: Abstract Advanced route planning algorithms are one of the key enabling technologies for emerging electric and autonomous mobility. Large realistic data sets are needed to test such algorithms under conditions that capture natural time-varying traffic patterns and corresponding travel-time and energy-use predictions. Further, the time-varying availability of charging infrastructure and vehicle-specific charging-power curves may be necessary to support advanced planning. While some data sets and synthetic data generators capture some of the aspects mentioned above, no integrated testbeds include all of them. We contribute with a modular testbed architecture. First, it includes a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic volume distribution patterns, EV-specific data, and elevation data. These elements support the generation of time-dependent travel-time and energy-use weights in a road-network graph. The generator ensures that the data satisfies the FIFO property, which is essential for time-dependent routing. Next, the testbed provides a thin layer of services that can serve as building blocks for future advanced routing algorithms. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.
      PubDate: 2023-01-26
       
  • Correction to: GeoImageNet: a multi-source natural feature benchmark
           dataset for GeoAI and supervised machine learning

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      PubDate: 2023-01-25
       
  • Optimizing pedestrian simulation based on expert trajectory guidance and
           deep reinforcement learning

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      Abstract: Abstract Most traditional pedestrian simulation methods suffer from short-sightedness, as they often choose the best action at the moment without considering the potential congesting situations in the future. To address this issue, we propose a hierarchical model that combines Deep Reinforcement Learning (DRL) and Optimal Reciprocal Velocity Obstacle (ORCA) algorithms to optimize the decision process of pedestrian simulation. For certain complex scenarios prone to local optimality, we include expert trajectory imitation degree in the reward function, aiming to improve pedestrian exploration efficiency by designing simple expert trajectory guidance lines without constructing databases of expert examples and collecting priori datasets. The experimental results show that the proposed method presents great stability and generalizability, evidenced by its capability to adjust the behavioral strategy earlier for the upcoming congestion situations. The overall simulation time for each scenario is reduced by approximately 8-44% compared to traditional methods. After including the expert trajectory guidance, the convergence speed of the model is greatly improved, evidenced by the reduced 56-64% simulation time from the first exploration to the global maximum cumulative reward value. The expert trajectory establishes the macro rules while preserving the space for free exploration, avoiding local dilemmas, and achieving optimized training efficiency.
      PubDate: 2023-01-16
       
  • Online meta-learning for POI recommendation

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      Abstract: Abstract Studying the POI recommendation in an online setting becomes meaningful because large volumes of user-POI interactions are generated in a chronological order. Although a few online update strategies have been developed, they cannot be applied in POI recommendation directly because they can hardly capture the long-term user preference only by updating the model with the current data. Besides, some latent POI information is ignored because existing update strategies are designed for traditional recommder systems without considering the addtional factors in POIs. In this paper, we propose an Online Meta-learning POI Recommendation (OMPR) method to solve the problem. To consider the geographical influences among POIs, we use a location-based self-attentive encoder to learn the complex user-POI relations. To capture the drift of user preference in online recommendation, we propose a meta-learning based transfer network to capture the knowledge transfer from both historical and current data. We conduct extensive experiments on two real-world datasets and the results show the superiority of our approaches in online POI recommendation.
      PubDate: 2023-01-01
       
  • Unified active and semi-supervised learning for hyperspectral image
           classification

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      Abstract: Abstract The large-scale labeled data is very crucial to train a classification model with strong generalization ability. However, the collection of large-scale labeled data is very expensive, especially in the remote sensing fields. The available of labeled data is very limited for the hyperspectal image classification. To address such a challenge, active learning and semi-supervised learning are two popular techniques in machine learning community. In this paper, we integrate active learning and semi-supervised learning into a framework by improving the quality of pseudo-labels for hyperspectral remote sensing images. In the proposed method, the collaboration of the spatial features and spectral features are adopted to improve the ability of classifier. Specifically, we train two classifiers with spatial feature and spectral feature respectively based on the labeled data. Then the prediction probabilities of the two classifiers are combined for strong prediction. With active learning technique, we can select a batch of the most informative samples and obtain a new labeled dataset. Two classifiers based on the new labeled dataset can be obtained. With these two classifiers, another prediction results by combining their predictions can be obtained. To guarantee the quality of the pseudo-labels, the samples that are predicted with the same labels before and after active learning are assigned with pseudo-labels. The samples that can not be assigned with high confident samples are regarded as the candidate pool for active learning. The final predictions are obtained by the classification models trained on the pseudo-labeled samples and the labeled samples with both the spatial features and spectral features. The experiments on two popular hyperspectral images show that the proposed method outperforms the state-of-the-art and baseline methods.
      PubDate: 2023-01-01
       
  • ASNN-FRR: A traffic-aware neural network for fastest route recommendation

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      Abstract: Abstract Fastest route recommendation (FRR) is an important task in urban computing. Despite some efforts are made to integrate A∗ algorithm with neural networks to learn cost functions by a data driven approach, they suffer from inaccuracy of travel time estimation and admissibility of model, resulting sub-optimal results accordingly. In this paper, we propose an ASNN-FRR model that contains two powerful predictors for g(⋅) and h(⋅) functions of A* algorithm respectively. Specifically, an adaptive graph convolutional recurrent network is used to accurately estimate the travel time of the observed path in g(⋅). Toward h(⋅), the model adopts a multi-task representation learning method to support origin-destination (OD) based travel time estimation, which can achieve high accuracy without the actual path information. Besides, we further consider the admissibility of A* algorithm, and utilize a rational setting of the loss function for h(⋅) estimator, which is likely to return a lower bound value without overestimation. At last, the two predictors are fused into the A∗ algorithm in a seamlessly way to help us find the real-time fastest route. We conduct extensive experiments on two real-world large scale trip datasets. The proposed approach clearly outperforms state-of-the-art methods for FRR task.
      PubDate: 2023-01-01
       
  • joinTree: A novel join-oriented multivariate operator for spatio-temporal
           data management in Flink

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      Abstract: Abstract In the era of intelligent Internet, the management and analysis of massive spatio-temporal data is one of the important links to realize intelligent applications and build smart cities, in which the interaction of multi-source data is the basis of realizing spatio-temporal data management and analysis. As an important carrier to achieve the interactive calculation of massive data, Flink provides the advanced Operator Join to facilitate user program development. In a Flink job with multi-source data connection operations, the selection of join sequences and the data communication in the repartition phase are both key factors that affect the efficiency of the job. However, Flink does not provide any optimization mechanism for the two factors, which in turn leads to low job efficiency. If the enumeration method is used to find the optimal join sequence, the result will not be obtained in polynomial time, so the optimization effect cannot be achieved. We investigate the above problems, design and implement a more advanced Operator joinTree that can support multi-source data connection in Flink, and introduce two optimization strategies into the Operator. In summary, the advantages of our work are highlighted as follows: (1) the Operator enables Flink to support multi-source data connection operation, and reduces the amount of calculation and data communication by introducing lightweight optimization strategies to improve job efficiency; (2) with the optimization strategy for join sequence, the total running time can be reduced by 29% and the data communication can be reduced by 34% compared with traditional sequential execution; (3) the optimization strategy for data repartition can further enable the job to bring 35% performance improvement, and in the average case can reduce the data communication by 43%.
      PubDate: 2023-01-01
       
  • MTMGNN: Multi-time multi-graph neural network for metro passenger flow
           prediction

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      Abstract: Abstract The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods.
      PubDate: 2023-01-01
       
  • Cost-effective and adaptive clustering algorithm for stream processing on
           cloud system

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      Abstract: Abstract Clustering is a fundamental operation that plays an essential role in data management and analysis. Clustering algorithms have been well studied over the past two decades, but the real-time clustering has yet to be maturely applied. For applications based on clustering calculations, capturing the dynamic changes of clusters and trends of moving objects in a real-time manner can maximize the value of the data. Although the DSPE (D istributed S tream P rocessing E ngine) is capable of such workloads, it still faces the problems of fixed window size and computational resources waste. In this paper, we introduce a new C ost-e ffective and A daptive C lustering method (CeAC), which can improve computational efficiency while ensuring the accuracy of the clustering result. Specifically, we design a composite window model which contains the latest data records and maintains historical states. To achieve a lightweight clustering, we propose a fully online clustering algorithm based on grid density, which can capture clusters with arbitrary shape and effectively handle outliers in parallel. We further introduce an adaptive calculation model to accelerate the clustering operation by shedding workload according to the incoming data characteristic. Experimental results show that the proposed method is accurate and efficient in real-time data stream clustering.
      PubDate: 2023-01-01
       
  • A segmented parallel expansion algorithm for keyword-aware optimal route
           query

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      Abstract: Abstract Keyword-aware Optimal Route Query (KOR) searches for an optimal route with the shortest traveling time under the conditions of full coverage of keywords and route budget, and is a high-frequency query in numerous map applications. Shortening the execution time is the significant goal of KOR optimization. The state-of-the-art algorithms primarily utilize various route expansion approaches to evaluate KORs, and focus on pruning strategies to reduce the search scale and shorten the execution time. Those strategies are effective in controlling the search scale for short routes, however, ineffective for long routes, because the search scale increases exponentially with the search depth. Therefore, this paper proposes PSE-KOR, a segmented parallel expansion algorithm for KOR, to address the issue for long routes. PSE-KOR constructs the routes with keyword vertexes as necessary passing nodes to satisfy the full coverage of keywords and budget, and divides the route into multiple segments taking the keyword vertexes as the boundary to limit the search scale and expands them in parallel to accelerate execution. For each route segment, a local budget limit pruning strategy is proposed to constrain the expansion direction and search depth, while reducing the interference among multiple segments. Extensive experiments verify the efficiency and effectiveness of PSE-KOR.
      PubDate: 2022-12-01
       
  • Big mobility data analytics: recent advances and open problems

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      PubDate: 2022-11-18
      DOI: 10.1007/s10707-022-00483-0
       
  • A spatially-aware algorithm for location extraction from structured
           documents

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      Abstract: Abstract Place names facilitate locating and distinguishing geographic space where human activities and natural phenomena occur. Extracting place names at multiple spatial resolutions from text is beneficial in several tasks such as identifying the location of events, enriching gazetteers, discovering connections between events and places, etc. Most modern place name extraction approaches generalize the linguistic rules and lexical features as a universal rule and ignore patterns inherent in place names in the geographic contexts. As a result, they lack spatial awareness to effectively identify place names from different geographic contexts, especially the lesser-known place names. In this research, we develop a novel Spatially-Aware Location Extraction (SALE) algorithm for place name extraction from structured documents that uses a hybrid approach comprising of knowledge-driven and data-driven methods. We build a custom named entity recognition (NER) system based on the conditional random field (CRF) and train/ fine-tune it using spatial features extracted from a dataset based on a given geographic region. SALE uses multiple pathways, including the use of the spatially tuned NER to enhance the efficacy in our place names extraction. The experimental results using a large geographic region show that our algorithm outperforms well-known state-of-the-art place name recognizers.
      PubDate: 2022-11-04
      DOI: 10.1007/s10707-022-00482-1
       
  • Towards general-purpose representation learning of polygonal geometries

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      Abstract: Abstract Neural network representation learning for spatial data (e.g., points, polylines, polygons, and networks) is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made in representation learning for points, polylines, and networks, whereas little progress has been made for polygons, especially complex polygonal geometries. In this work, we focus on developing a general-purpose polygon encoding model, which can encode a polygonal geometry (with or without holes, single or multipolygons) into an embedding space. The result embeddings can be leveraged directly (or finetuned) for downstream tasks such as shape classification, spatial relation prediction, building pattern classification, cartographic building generalization, and so on. To achieve model generalizability guarantees, we identify a few desirable properties that the encoder should satisfy: loop origin invariance, trivial vertex invariance, part permutation invariance, and topology awareness. We explore two different designs for the encoder: one derives all representations in the spatial domain and can naturally capture local structures of polygons; the other leverages spectral domain representations and can easily capture global structures of polygons. For the spatial domain approach we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons. For the spectral domain approach we develop NUFTspec based on Non-Uniform Fourier Transformation (NUFT), which naturally satisfies all the desired properties. We conduct experiments on two different tasks: 1) polygon shape classification based on the commonly used MNIST dataset; 2) polygon-based spatial relation prediction based on two new datasets (DBSR-46K and DBSR-cplx46K) constructed from OpenStreetMap and DBpedia. Our results show that NUFTspec and ResNet1D outperform multiple existing baselines with significant margins. While ResNet1D suffers from model performance degradation after shape-invariance geometry modifications, NUFTspec is very robust to these modifications due to the nature of the NUFT representation. NUFTspec is able to jointly consider all parts of a multipolygon and their spatial relations during prediction while ResNet1D can recognize the shape details which are sometimes important for classification. This result points to a promising research direction of combining spatial and spectral representations.
      PubDate: 2022-10-22
      DOI: 10.1007/s10707-022-00481-2
       
  • Semi-supervised geological disasters named entity recognition using few
           labeled data

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      Abstract: Abstract The geological disasters Named Entity Recognition (NER) method aims to recognize entities reflecting disaster event information in unstructured texts to construct a geohazard knowledge graph that can provide a reference for disaster emergency response. Without training on large-scale labeled data, current NER methods based on deep learning models cannot identify specific geological disaster entities from geological disaster situation reports. However, manually labeling geohazard situation reports is tedious and time-consuming. As a result, we present Semi-GDNER, a semi-supervised geological disasters NER approach that can effectively extract six kinds of geological disaster entities when a few manually labeled and unlabeled in-domain data are available. It is divided into two stages: (1) transferring the parameters of the pre-trained BERT-base model to the BERT layer of the backbone model BERT-BiLSTM-CRF and training the backbone model with a few labeled data; (2) continuing training the backbone model by expanding the training set with unlabeled data using a self-training (ST) strategy. To reduce noise in the second stage, we select the pseudo-labeled samples with high confidence to join the training set in each ST iteration. Experiments on our constructed Geological Disaster NER data show that our approach achieves a higher F1 (0.88) than other NER approaches (including five supervised NER approaches and a semi-supervised NER approach using the ST strategy of expanding the training set with all pseudo-labeled data), demonstrating the effectiveness of our approach. Furthermore, experiments on four general Chinese NER datasets show that the framework of our approach is transferable.
      PubDate: 2022-10-18
      DOI: 10.1007/s10707-022-00474-1
       
  • Knowledge distillation based lightweight building damage assessment using
           satellite imagery of natural disasters

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      Abstract: Abstract Accurate and timely assessment of post-disaster building damage is of great significance for national development and social security concerns. However, due to the high timeliness requirements of disaster emergency response and the conflict that sufficient computing resources are not easily available in harsh environments, and therefore the lightweight AI-driven post-disaster building damage assessment model is highly needed. In this paper, we introduced a knowledge distillation-based lightweight approach for assessing building damage from xBD high-resolution satellite images with the purpose of reducing the dependence on computing resources in disaster emergency response scenarios. Specifically, an ensemble Teacher-Student knowledge distillation method was designed and compared with the xBD baseline model. The result has shown that, the knowledge distillation reduces the parameter number of the original model by 30%, and the inference speed is increased by 30%-40%. In the building localization task, the accuracy of teacher and student model are 0.879 and 0.832 (IOU) respectively. In the damage classification task, the accuracy of teacher and student are 0.798 and 0.775 respectively. In addition, we proposed a dual-teacher-student knowledge distillation strategy, which cannot use the pre-training skills of curriculum learning in student model training, but achieve the same effect through more direct knowledge transfer. In the experiment, our dual-teacher-student method improves the knowledge distillation baseline by 3.7% with 30 epoch training. With only 70% parameters, our student model performs close to the teacher model at a degradation within 5%.This study verifies the effectiveness and prospect of knowledge distillation method in building damage assessment for disaster emergency.
      PubDate: 2022-10-17
      DOI: 10.1007/s10707-022-00480-3
       
  • City indicators for geographical transfer learning: an application to
           crash prediction

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      Abstract: Abstract The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution.
      PubDate: 2022-10-01
       
  • Spatial regression graph convolutional neural networks: A deep learning
           paradigm for spatial multivariate distributions

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      Abstract: Abstract Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI.
      PubDate: 2022-10-01
       
  • Multi-type clustering using regularized tensor decomposition

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      Abstract: Abstract Geospatial analytics increasingly rely on data fusion methods to extract patterns from data; however robust results are difficult to achieve because of the need for spatial and temporal regularization and latent structures within data. Tensor decomposition is a promising approach because it can accommodate multidimensional structure of data (e.g., trajectory information about users, locations, and time periods). To address these challenges, we introduce Multi-Type Clustering using Regularized tensor Decomposition (MCRD), an innovative method for data analysis that provides insight not just about groupings within data types (e.g., clusters of users), but also about the interactions between data types (e.g., clusters of users and locations) in the latent features of complex multi-type datasets. This is done by combining two innovations. First, a tensor representing spatiotemporal data is decomposed using a novel regularization method to account for structure within the data. Next, within- and cross-type groups are found through the application of novel hypergraph community detection methods to the decomposed results. Experimentation on both synthetic and real trajectory data demonstrates MCRD’s capacity to reveal the within- and cross-type grouping in data, and MCRD outperforms related methods including tensor decomposition without regularization, unfolding of tensors, Laplacian regularization, and tensor block models. The robust and versatile analysis provided by combining new regularization and clustering techniques outlined in this paper likely have utility in geospatial analytics beyond the movement applications explicitly studied.
      PubDate: 2022-10-01
       
  • Online fleet monitoring with scalable event recognition and forecasting

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      Abstract: Abstract Moving object monitoring is becoming essential for companies and organizations that need to manage thousands or even millions of commercial vehicles or vessels, detect dangerous situations (e.g., collisions or malfunctions) and optimize their behavior. It is a task that must be executed in real-time, reporting any such situations or opportunities as soon as they appear. Given the growing sizes of fleets worldwide, a monitoring system must be highly efficient and scalable. It is becoming an increasingly common requirement that such monitoring systems should be able to automatically detect complex situations, possibly involving multiple moving objects and requiring extensive background knowledge. Building a monitoring system that is both expressive and scalable is a significant challenge. Typically, the more expressive a system is, the less flexible it becomes in terms of its parallelization potential. We present a system that strikes a balance between expressiveness and scalability. Going beyond event detection, we also present an approach towards event forecasting. We show how event patterns may be given a probabilistic description so that our system can forecast when a complex event is expected to occur. Our proposed system employs a formalism that allows analysts to define complex patterns in a user-friendly manner while maintaining unambiguous semantics and avoiding ad hoc constructs. At the same time, depending on the problem at hand, it can employ different parallelization strategies in order to address the issue of scalability. It can also employ different training strategies in order to fine-tune the probabilistic models constructed for event forecasting. Our experimental results show that our system can detect complex patterns over moving entities with minimal latency, even when the load on our system surpasses what is to be realistically expected in real-world scenarios.
      PubDate: 2022-10-01
       
 
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