# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis
### LSI Model-Based Metadata Retrieval Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Subgroup** & \multicolumn{3}{c}{**Query Time Frames**} \\ \hline
**Metadata Volume (Million)** & **Spatial Parameters** & **1 Day** & **1 Month** & **6 Months** \\ \hline \multirow{3}{*}{1} & 1 GeoSOT Grid & 133 ms & 144 ms & 145 ms \\  & 2 GeoSOT Grids & 139 ms & 144 ms & 151 ms \\  & 4 GeoSOT Grids & 151 ms & 154 ms & 155 ms \\ \hline \multirow{3}{*}{3} & 1 GeoSOT Grid & 211 ms & 213 ms & 215 ms \\  & 2 GeoSOT Grids & 218 ms & 224 ms & 235 ms \\  & 4 GeoSOT Grids & 220 ms & 239 ms & 261 ms \\ \hline \multirow{3}{*}{5.5} & 1 GeoSOT Grid & 310 ms & 324 ms & 325 ms \\  & 2 GeoSOT Grids & 340 ms & 359 ms & 375 ms \\  & 4 GeoSOT Grids & 365 ms & 398 ms & 421 ms \\ \hline \multirow{3}{*}{7.5} & 1 GeoSOT Grid & 340 ms & 350 ms & 355 ms \\  & 2 GeoSOT Grids & 401 ms & 405 ms & 421 ms \\  & 4 GeoSOT Grids & 457 ms & 476 ms & 510 ms \\ \hline \multirow{3}{*}{10} & 1 GeoSOT Grid & 480 ms & 495 ms & 525 ms \\  & 2 GeoSOT Grids & 566 ms & 589 ms & 603 ms \\  & 4 GeoSOT Grids & 650 ms & 668 ms & 691 ms \\ \hline \multirow{3}{*}{15} & 1 GeoSOT Grid & 613 ms & 655 ms & 681 ms \\  & 2 GeoSOT Grids & 850 ms & 856 ms & 861 ms \\  & 4 GeoSOT Grids & 965 ms & 994 ms & 1110 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The search conditions and time consumed of each retrieval. \begin{table}
\begin{tabular}{c c c c} \hline
**Satellite** & **Data Type** & **Volume of Metadata** & **Metadata Format** \\ \hline Landsat 8 & OLLTIRS & 896,981 & HDF-EOS \\ HJ-1A & HSI & 85,072 & Customized XML \\ CEBERS-1/2 & CCD & 889,685 & Customized XML \\ Landsat 7 & ETM+ & 2,246,823 & HDF-EOS \\ Landsat-5 & MSS & 1,306,579 & HDF-EOS \\ HJ-1A/B & CCD & 2,210,352 & Customized XML \\ Landsat 5 & TM & 2,351,899 & HDF-EOS \\ FY-3A/B & VIRR & 2,343,288 & Customized HDF5-FY \\ Aster & L1T & 2,951,298 & HDF-EOS \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of experimental remote sensing metadata. As can be seen in Table 6, in each group and subgroup, with the increase of query time frames, the time consumed showed an upward trend as a whole. However, the increase was not obvious. This type of situation could benefit from the inverted index of SolrCloud. The small amount of time increment was mainly spent in the query results return process. As for the spatial query parameters changing, the time consumed within the 4-GeoSOT grid query was clearly greater than that of within 1 GeoSOT grid, and this gap increased with the amount of metadata. This is perfectly understandable. The 4-GeoSOT grid query was the worst condition, and the comparison with the center point GeoSOT code of each remote sensing image should be made four times. However, within a 1 GeoSOT grid query, a one-time comparison would obviously be faster. Whether it is the spatial query parameters or query time frames that are changing, the retrieval times increase linearly with the increase of metadata volume. More specifically, the times for an increase rate below 10 million are a little shorter than those for an increase rate of 10 to 15 million.