147,353 research outputs found

    DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

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    Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method

    Mining motifs in temporal networks

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    Temporal networks are mathematical tools used to model complex systems which embed the temporal dimension. In this thesis we address the problem of counting motifs in temporal networks. We provide a new exact parallel algorithm which is both scalable and efficient in practice. We address the problem of approximating an exact count with rigorous guarantees. We provide, to the best of our knowledge, the first rigorous sampling algorithms devised for such task

    Online data mining services for dynamic spatial databases I: system architecture and client applications

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    This paper describes online data mining services for dynamic spatial databases connected to environmental monitoring networks. These services can use Artificial Neural Networks as data mining techniques to find temporal relations in monitored parameters. The execution of the data mining algorithms is performed at the server side and a distributed processing scheme is used to overcome problems of scalability. To support the discovery of temporal relations, two other families of online services are made available: vectorial and raster visualization services and a sonification service. The use of this system is illustrated by the DM Plus client application and the SNIRH Data Mining Web site. The sonification service is described and illustrated in the part II paper

    Mining Significant Temporal Networks Is Polynomial

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    A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism that tackles controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds a CSTNUD to model, validate and execute some temporal plan of interest. Instead, in this paper, we investigate the bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). This paper paves the way for the design of controllable temporal networks mined from traces that also contain information on uncontrollable events

    #mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks

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    We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs - Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015. This is the pre-peer reviewed version and the final version is available at http://wing.comp.nus.edu.sg/publications/2015/lim-et-al-15.pd
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