{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:48:08Z","timestamp":1772970488947,"version":"3.50.1"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (&lt; 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.<\/jats:p>","DOI":"10.14778\/3476311.3476312","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:43Z","timestamp":1635461323000},"page":"2655-2658","source":"Crossref","is-referenced-by-count":23,"title":["KDV-explorer"],"prefix":"10.14778","volume":"14","author":[{"given":"Tsz Nam","family":"Chan","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pak Lon","family":"Ip","sequence":"additional","affiliation":[{"name":"University of Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leong Hou","family":"U","sequence":"additional","affiliation":[{"name":"University of Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weng Hou","family":"Tong","sequence":"additional","affiliation":[{"name":"University of Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivansh","family":"Mittal","sequence":"additional","affiliation":[{"name":"The University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[{"name":"University of Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reynold","family":"Cheng","sequence":"additional","affiliation":[{"name":"The University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Versioned, P2P File System. CoRR abs\/1407.3561","author":"Benet J.","year":"2014","unstructured":"J. Benet . 2014. IPFS - Content Addressed , Versioned, P2P File System. CoRR abs\/1407.3561 ( 2014 ). arXiv:1407.3561 J. Benet. 2014. IPFS - Content Addressed, Versioned, P2P File System. CoRR abs\/1407.3561 (2014). arXiv:1407.3561"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132757"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"J. Kang Z. Xiong D. Niyato S. Xie and J. Zhang. 2019. Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory. loT-Journal 6 6 (2019) 10700--10714.  J. Kang Z. Xiong D. Niyato S. Xie and J. Zhang. 2019. Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory. loT-Journal 6 6 (2019) 10700--10714.","DOI":"10.1109\/JIOT.2019.2940820"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_5_1","first-page":"429","article-title":"Federated Optimization in Heterogeneous Networks","volume":"2","author":"Li T.","year":"2020","unstructured":"T. Li , A. K. Sahu , M. Zaheer , M. Sanjabi , A. Talwalkar , and V. Smith . 2020 . Federated Optimization in Heterogeneous Networks . In Proc. MLSys , Vol. 2. 429 -- 450 . T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith. 2020. Federated Optimization in Heterogeneous Networks. In Proc. MLSys, Vol. 2. 429--450.","journal-title":"Proc. MLSys"},{"key":"e_1_2_1_6_1","volume-title":"Blockchains: A Crowdsourcing Approach. In Proc. ICDMW. 81--88.","author":"Lu Y.","year":"2018","unstructured":"Y. Lu , Q. Tang , and G. Wang . 2018 . On Enabling Machine Learning Tasks atop Public Blockchains: A Crowdsourcing Approach. In Proc. ICDMW. 81--88. Y. Lu, Q. Tang, and G. Wang. 2018. On Enabling Machine Learning Tasks atop Public Blockchains: A Crowdsourcing Approach. In Proc. ICDMW. 81--88."},{"key":"e_1_2_1_7_1","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume":"54","author":"McMahan H. B.","year":"2017","unstructured":"H. B. McMahan , E. Moore , D. Ramage , S. Hampson , and B. A. y Arcas . 2017 . Communication-Efficient Learning of Deep Networks from Decentralized Data . In Proc. AISTATS , Vol. 54. 1273 -- 1282 . H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. AISTATS, Vol. 54. 1273--1282.","journal-title":"Proc. AISTATS"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"J. Weng J. Weng J. Zhang M. Li Y. Zhang and W. Luo. 2019. DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive. TDSC (2019) 1--18.  J. Weng J. Weng J. Zhang M. Li Y. Zhang and W. Luo. 2019. DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive. TDSC (2019) 1--18.","DOI":"10.1109\/TDSC.2019.2952332"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3476311.3476312","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:26:35Z","timestamp":1672226795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3476311.3476312"}},"subtitle":["a near real-time kernel density visualization system for spatial analysis"],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10.14778\/3476311.3476312"],"URL":"https:\/\/doi.org\/10.14778\/3476311.3476312","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}