ACM Transactions on Spatial Algorithms and Systems最新文献_第2页

Regionalization-based Collaborative Filtering: Harnessing Geographical Information in Recommenders 基于区域化的协同过滤：在推荐器中利用地理信息

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-08 DOI: 10.1145/3656641

Rodrigo Alves

{"title":"Regionalization-based Collaborative Filtering: Harnessing Geographical Information in Recommenders","authors":"Rodrigo Alves","doi":"10.1145/3656641","DOIUrl":"https://doi.org/10.1145/3656641","url":null,"abstract":"Regionalization, also known as spatially constrained clustering, is an unsupervised machine learning technique used to identify and define spatially contiguous regions. In this work, we introduce a methodology to regionalize recommendation systems (RSs) based on a collaborative filtering approach. Two main challenges arise when performing regionalization based on users’ preferences in RSs: (1) unstructured data, as interactions are often scarce and observed on a smaller scale; and (2) the difficulty of evaluation of the quality of the clustering results. To address these challenges, our methodology relies on inductive matrix completion (IMC), a fundamental approach to recover unknown entries of a rating matrix while utilizing region information to extract a region-based feature matrix. With this feature matrix, our method becomes adaptive and seamlessly integrates with various regionalization algorithms to create regionalization candidates. This enables us to derive more accurate recommendations that consider regionalized effects and discover interesting patterns in localized user behavior. We experimentally evaluate our model on synthetic datasets to demonstrate its efficacy in settings where our underlying assumptions are correct. Furthermore, we present a real-world case study illustrating the interpretable information the model can derive in terms of regionalized recommendation relevance.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Let’s Speak Trajectories: A Vision To Use NLP Models For Trajectory Analysis Tasks 让我们说说轨迹：将 NLP 模型用于轨迹分析任务的愿景

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-04-08 DOI: 10.1145/3656470

Mashaal Musleh, M. Mokbel

{"title":"Let’s Speak Trajectories: A Vision To Use NLP Models For Trajectory Analysis Tasks","authors":"Mashaal Musleh, M. Mokbel","doi":"10.1145/3656470","DOIUrl":"https://doi.org/10.1145/3656470","url":null,"abstract":"The availability of trajectory data combined with various real life practical applications have sparked the interest of the research community to design a plethora of algorithms for various trajectory analysis techniques. However, there is an apparent lack of full-fledged systems that provide the infrastructure support for trajectory analysis techniques, which hinders the applicability of most of the designed algorithms. Inspired by the tremendous success of the BERT deep learning model in solving various Natural Language Processing (NLP) tasks, our vision is to have a BERT-like system for trajectory analysis tasks. We envision that in a few years, we will have such system, where no one needs to worry again about each specific trajectory analysis operation. Whether it is trajectory imputation, similarity, clustering, or whatever, it would be one system that researchers, developers, and practitioners can deploy to get high accuracy for their trajectory operations. Our vision stands on a solid ground that trajectories in a space are highly analogous to statements in a language. We outline the challenges and the road to our vision. Exploratory results confirm the promise and possibility of our vision.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper) 论 GeoAI 基础模型的机遇与挑战（远景规划论文）

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-03-20 DOI: 10.1145/3653070

Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak Mishra, Ninghao Liu, Song Gao, Tianming Liu, Gao Cong, Yingjie Hu, Chris Cundy, Ziyuan Li, Rui Zhu, Ni Lao

{"title":"On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)","authors":"Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak Mishra, Ninghao Liu, Song Gao, Tianming Liu, Gao Cong, Yingjie Hu, Chris Cundy, Ziyuan Li, Rui Zhu, Ni Lao","doi":"10.1145/3653070","DOIUrl":"https://doi.org/10.1145/3653070","url":null,"abstract":"\u0000 Large pre-trained models, also known as\u0000 foundation models\u0000 (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial domains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, the task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a foundation model for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.\u0000","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel Topology-aware Mesh Simplification on Terrain Trees 地形树上的并行拓扑感知网格简化

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-03-13 DOI: 10.1145/3652602

Yunting Song, Riccardo Fellegara, F. Iuricich, Leila De Floriani

{"title":"Parallel Topology-aware Mesh Simplification on Terrain Trees","authors":"Yunting Song, Riccardo Fellegara, F. Iuricich, Leila De Floriani","doi":"10.1145/3652602","DOIUrl":"https://doi.org/10.1145/3652602","url":null,"abstract":"We address the problem of performing a topology-aware simplification algorithm on a compact and distributed data structure for triangle meshes, the Terrain trees. Topology-aware operators have been defined to coarsen a Triangulated Irregular Network (TIN) without affecting the topology of its underlying terrain, i.e., without modifying critical features of the terrain, such as pits, saddles, peaks, and their connectivity. However, their scalability is limited for large-scale meshes. Our proposed algorithm uses a batched processing strategy to reduce both the memory and time requirements of the simplification process and thanks to the spatial decomposition on the basis of Terrain trees, it can be easily parallelized. Also, since a Terrain tree after the simplification process becomes less compact and efficient, we propose an efficient post-processing step for updating hierarchical spatial decomposition. Our experiments on real-world TINs, derived from topographic and bathymetric LiDAR data, demonstrate the scalability and efficiency of our approach. Specifically, topology-aware simplification on Terrain trees uses 40% less memory and half the time compared to the most compact and efficient connectivity-based data structure for TINs. Furthermore, the parallel simplification algorithm on the Terrain trees exhibits a 12x speedup with an OpenMP implementation. The quality of the output mesh is not significantly affected by the distributed and parallel simplification strategy of Terrain trees, and we obtain similar quality levels compared to the global baseline method.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Challenge of Data Analytics with Climate-Neutral Urban Mobility (Vision Paper) 气候中和城市交通数据分析的挑战（远景规划论文）

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-02-23 DOI: 10.1145/3649312

Stephan Winter, Monika Sester, M. Tomko, Alexandra Millonig

引用次数: 0

Scalable Spatio-Temporal Top-k Interaction Queries on Dynamic Communities 动态社区上的可扩展时空 Top-k 交互查询

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-02-16 DOI: 10.1145/3648374

Abdulaziz Almaslukh, Yongyi Liu, A. Magdy

引用次数: 0

RE-Trace : Re-Identification of Modified GPS Trajectories RE-Trace : 重新识别修改后的 GPS 轨迹

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-02-05 DOI: 10.1145/3643680

Stefan Schestakov, Simon Gottschalk, Thorben Funke, Elena Demidova

{"title":"RE-Trace\u0000 : Re-Identification of Modified GPS Trajectories","authors":"Stefan Schestakov, Simon Gottschalk, Thorben Funke, Elena Demidova","doi":"10.1145/3643680","DOIUrl":"https://doi.org/10.1145/3643680","url":null,"abstract":"\u0000 GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely the re-identification of leaked and potentially modified GPS trajectories. We present\u0000 RE-Trace\u0000 – a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin.\u0000 RE-Trace\u0000 utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the\u0000 RE-Trace\u0000 re-identification approach on three real-world datasets. Our evaluation results demonstrate that\u0000 RE-Trace\u0000 significantly outperforms state-of-the-art baselines on all data sets and identifies modified GPS trajectories effectively and efficiently.\u0000","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RE-Trace : Re-Identification of Modified GPS Trajectories RE-Trace : 重新识别修改后的 GPS 轨迹

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-02-05 DOI: 10.1145/3643680

Stefan Schestakov, Simon Gottschalk, Thorben Funke, Elena Demidova

{"title":"RE-Trace\u0000 : Re-Identification of Modified GPS Trajectories","authors":"Stefan Schestakov, Simon Gottschalk, Thorben Funke, Elena Demidova","doi":"10.1145/3643680","DOIUrl":"https://doi.org/10.1145/3643680","url":null,"abstract":"\u0000 GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely the re-identification of leaked and potentially modified GPS trajectories. We present\u0000 RE-Trace\u0000 – a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin.\u0000 RE-Trace\u0000 utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the\u0000 RE-Trace\u0000 re-identification approach on three real-world datasets. Our evaluation results demonstrate that\u0000 RE-Trace\u0000 significantly outperforms state-of-the-art baselines on all data sets and identifies modified GPS trajectories effectively and efficiently.\u0000","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets 双面乘车市场中的因果概率时空融合变换器

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-02-03 DOI: 10.1145/3643848

Shixiang Wan, S. Luo, Hongtu Zhu

引用次数: 0

Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets 双面乘车市场中的因果概率时空融合变换器

IF 1.9

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-02-03 DOI: 10.1145/3643848

Shixiang Wan, S. Luo, Hongtu Zhu

引用次数: 0