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Articles 1 - 6 of 6
Full-Text Articles in Databases and Information Systems
Where Are The Passengers? A Grid-Based Gaussian Mixture Model For Taxi Bookings, Meng-Fen Chiang, Tuan Anh Hoang, Ee-Peng Lim
Where Are The Passengers? A Grid-Based Gaussian Mixture Model For Taxi Bookings, Meng-Fen Chiang, Tuan Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Taxi bookings are events where requests for taxis are made by passengers either over voice calls or mobile apps. As the demand for taxis changes with space and time, it is important to model both the space and temporal dimensions in dynamic booking data. Several applications can benefit from a good taxi booking model. These include the prediction of number of bookings at certain location and time of the day, and the detection of anomalous booking events. In this paper, we propose a Grid-based Gaussian Mixture Model (GGMM) with spatio-temporal dimensions that groups booking data into a number of spatio-temporal …
From Sensors To Sense Making: Leveraging Open-Access Scientific Data To Assess Arctic Maritime Risks, Mark A. Stoddard, Melanie Fournier Ph.D, Laurent Etienne Ph.D, Leah Beveridge Ph.D
From Sensors To Sense Making: Leveraging Open-Access Scientific Data To Assess Arctic Maritime Risks, Mark A. Stoddard, Melanie Fournier Ph.D, Laurent Etienne Ph.D, Leah Beveridge Ph.D
ShipArc 2015 Conference
No abstract provided.
On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim
On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Large cities today are facing major challenges in planning and policy formulation to keep their growth sustainable. In this paper, we aim to gain useful insights about people living in a city by developing novel models to mine user lifestyles represented by the users' activity centers. Two models, namely ACMM and ACHMM, have been developed to learn the activity centers of each user using a large dataset of bus and subway train trips performed by passengers in Singapore. We show that ACHMM and ACMM yield similar accuracies in location prediction task. We also propose methods to automatically predict "home", "work" …
Fast Optimal Aggregate Point Search For A Merged Set On Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu, Weimo Liu, Yan Huang
Fast Optimal Aggregate Point Search For A Merged Set On Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu, Weimo Liu, Yan Huang
Research Collection School Of Computing and Information Systems
Aggregate nearest neighbor query, which returns an optimal target point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes two algorithms to process MANN query on road networks when aggregate function is max. Then, we extend the algorithms …
Efficient Reverse Top-K Boolean Spatial Keyword Queries On Road Networks, Yunjun Gao, Xu Qin, Baihua Zheng, Gang Chen
Efficient Reverse Top-K Boolean Spatial Keyword Queries On Road Networks, Yunjun Gao, Xu Qin, Baihua Zheng, Gang Chen
Research Collection School Of Computing and Information Systems
Reverse k nearest neighbor (RkNN) queries have a broad application base such as decision support, profile-based marketing, and resource allocation. Previous work on RkNN search does not take textual information into consideration or limits to the Euclidean space. In the real world, however, most spatial objects are associated with textual information and lie on road networks. In this paper, we introduce a new type of queries, namely, reverse top-k Boolean spatial keyword (RkBSK) retrieval, which assumes objects are on the road network and considers both spatial and textual information. Given a data set P on a road network and a …
Major Challenges And Solutions For Utilizing Big Data In The Maritime Industry, Sadaharu Koga
Major Challenges And Solutions For Utilizing Big Data In The Maritime Industry, Sadaharu Koga
World Maritime University Dissertations
The dissertation is a study of big data for the use in the maritime industry. Today’s society is information-intensive. The term “big data” is becoming more common. In fact, some maritime companies and institutions have already been trying to utilize big data for enhancing maritime safety and environmental protection. In order to promote this trend, the dissertation tries to identify common and important challenges for the whole maritime industry in terms of the utilization of big data and propose corresponding solutions. First, by reviewing the definitions of big data, three major features are identified. Big data takes electronic form, is …