scholarly journals An efficient index structure for large-scale geo-tagged video databases

Author(s):  
Ying Lu ◽  
Cyrus Shahabi ◽  
Seon Ho Kim
2013 ◽  
Vol 441 ◽  
pp. 691-694
Author(s):  
Yi Qun Zeng ◽  
Jing Bin Wang

With the rapid development of information technology, data grows explosionly, how to deal with the large scale data become more and more important. Based on the characteristics of RDF data, we propose to compress RDF data. We construct an index structure called PAR-Tree Index, then base on the MapReduce parallel computing framework and the PAR-Tree Index to execute the query. Experimental results show that the algorithm can improve the efficiency of large data query.


Author(s):  
Hung Thanh Vu ◽  
Thanh Duc Ngo ◽  
Thao Ngoc Nguyen ◽  
Duy-Dinh Le ◽  
Shin'ichi Satoh ◽  
...  

2016 ◽  
Vol 20 (4) ◽  
pp. 829-857 ◽  
Author(s):  
Ying Lu ◽  
Cyrus Shahabi ◽  
Seon Ho Kim

2021 ◽  
Vol 11 (16) ◽  
pp. 7658
Author(s):  
Steven Fiorino ◽  
Santasri Bose-Pillai ◽  
Kevin Keefer

Optical turbulence, as determined by the widely accepted practice of profiling the temperature structure constant, CT2, via the measurement of ambient atmospheric temperature gradients, can be found to differ quite significantly when characterizing such gradients via thermal-couple differential temperature sensors as compared to doing so with acoustic probes such as those commonly used in sonic anemometry. Similar inconsistencies are observed when comparing optical turbulence strength derived via CT2 as compared to those through direct optical or imaging measurements of small fluctuations of the index of refraction of air (i.e., scintillation). These irregularities are especially apparent in stable atmospheric layers and during diurnal quiescent periods. Our research demonstrates that when care is taken to properly remove large-scale index of refraction gradients, the sonic anemometer-derived velocity structure constant, Cv2, coupled with the similarly derived turbulence-driven index of refraction and vertical wind shear gradients, provides a refractive index structure constant, Cn2, that can more closely match the optical turbulence strengths inferred by more direct means such as scintillometers or differential image motion techniques. The research also illustrates the utility and robustness of quantifying Cn2 from CT2 at a point using a single sonic anemometer and establishes a clear set of equations to calculate volumetric Cn2 data using instrumentation that measures wind velocities with more spatial/temporal fidelity than temperature.


2018 ◽  
Vol 29 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jing Weipeng ◽  
Tian Dongxue ◽  
Chen Guangsheng ◽  
Li Yiyuan

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability.


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