964 research outputs found

    GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data

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    Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. However, the process of mining large graph patterns is a challenge due to the existence of large number of subgraphs. In this paper, we aim to mine only frequent complete graph patterns. A graph g in a database is complete if every pair of distinct vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining algorithm developed to explore interesting pruning techniques to extract maximal complete graphs from large spatial dataset existing in Sloan Digital Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high efficiency especially in the presence of large number of patterns. In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones. To the best of our knowledge, this is the first algorithm used to exploit the extraction of maximal complete graphs in the process of mining complex co-location patterns in large spatial dataset.Comment: 1

    Co-presence Communities: Using pervasive computing to support weak social networks

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    Although the strongest social relationships feature most prominently in our lives, we also maintain a multitude of much weaker connections: the distant colleagues that we share a coffee with in the afternoon; the waitress at a our regular sandwich bar; or the ‘familiar stranger’ we meet each morning on the way to work. These are all examples of weak relationships which have a strong spatial-temporal component but with few support systems available. This paper explores the idea of ‘Co-presence Communities’ - a probabilistic definition of groups that are regularly collocated together - and how they might be used to support weak social networks. An algorithm is presented for mining the Copresence Community definitions from data collected by Bluetooth-enabled mobile phones. Finally, an example application is introduced which utilises these communities for disseminating information

    Visualize online collocation dictionary with force-directed graph

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    For second-language learners, collocational knowledge is very important. Knowing collocational phrases allows learners to speak and write in their targeted language naturally and reduce dramatically side effect of their first language. In order for learners to learn collocations easily, a lot of learning methods have been introduced. Particularly, learning from online-collocational corpus has become popular due to its accessibility and massive database. Although, its current presentation of information is still simple, it can be improved by using optimized representations in order to help users learning. In this thesis, we represent a suitable way to visualize online collocational dictionary by using graph representation in order to facilitate users’ learning and provide flexible exploration. Animation is also used to increase level of engagement for users. We use force-directed model for the layout, but we develop our own graph component and combine some current algorithms in order to create a proper algorithm for our purposes. The implementation is tested by a small group of participants and the results are promising

    Interpreting Spatial Language in Image Captions

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    The map as a tool for accessing data has become very popular in recent years, but a lot of data do not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal, we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague, and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively

    A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor

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    Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH4_{4}) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadirviewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4_{4} are simultaneously retrieved from TROPOMI’s radiance measurements in the 2:3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMDOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH4_{4} data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5:1 ppb (5:8 %) and a systematic error of 1:9 ppb (2:1 %); the XCH4_{4} data set exhibits a random error of 14:0 ppb (0:8 %) and a systematic error of 4:3 ppb (0:2 %). The natural XCO and XCH4_{4} variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R = 0:97 for XCO and R D 0:91 for XCH4_{4} based on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4_{4} emissions from the energy sector, which potentially allows for the advance of emission monitoring and air quality assessments to an entirely new level
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