## 4 Experimental Evaluation
### Performance of Hot-Cold Segment Identification Method  
We measure the overall performance of our hot-cold segment identification algorithm using a currently available real LOD dataset. We used various data to obtain realistic results on the performance of large-scale RDF storage structures. To be as general as possible, this dataset was chosen from several RDF data used in the LOD cloud. Table 2 lists the characteristics of the collected dataset. To describe the characteristics of the dataset, we provide the size and the number of triples, subjects, predicates, and objects. DBpedia contains RDF information extracted from Wikipedia. DrugBank contains RDF information on  
Figure 11: Storage amount. drugs. It is widely used by the drug industry, medicinal chemists, pharmacists, physicians, students, and public institutions. LinkedGeData is a large spatial knowledge base which has been derived from OpenStreetMap for Semantic Web. These three data are widely used for developing Semantic Web applications. The hot-cold segment identification algorithm determines how to allocate data between SSD and HDD. The storage system composed of a single storage medium cannot meet the demand of high capacity, low cost, and high-performance at the same time. Therefore, if multiple storage media are integrated to form a hybrid storage structure, using their respective advantages, the requirements for large capacity, low cost, and high-performance can be met. Figure 12 shows the join query performance of the real LOD dataset listed in Table 2, when the 3D R*-tree is kept in SSD and HDD, respectively. We find that the performance difference of the 3D R*-tree on SSD and HDD is not apparent through this experimental result. Hence, we put the 3D R*-tree in HDD since the cost of SSD is higher than that of the HDD. Figure 13 shows the results of relocating each datum to an appropriate storage device using hot-cold data identified by the decision tree. For different benchmark complexities, DBpedia, DrugBank, and LinkedGeoData in Table 2 are considered. The results show that an average of 42% of the SSD is used to store hot segments, and an average of 58% of the HDD is used to store cold segments. Thus, SSD is used to the minimum. Figure 14 illustrates the search performance of storage structures obtained from our experimental dataset. The experiments perform comparing response times using SSD only, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Size (MB)** & **Number of Triples** & **Number of Subjects** & **Number of Predicates** & **Number of Objects** \\ \hline
**DBpedia** & 3.94 & 31,050 & 4008 & 23 & 16,644 \\
**DrugBank** & 144 & 766,920 & 19,693 & 119 & 274,864 \\
**LinkedGeoData** & 327 & 2,207,295 & 552,541 & 1320 & 1,308,214 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the Linked Open Data (LOD) dataset. Figure 12: Three-dimensional R*-tree performance on SSD and HDD. Figure 13: Results of relocating data in SSD and HDD. our hybrid storage structure (referred to as HSS), and HDD only, respectively. The results show that the search performance of SSD always beats the performance of other storage devices. HSS performs slightly worse than SSD because our hybrid storage structure is based on the hot-cold segment identification strategy. HDD performs significantly worse than the other storage structures. For all data, SSD performs 1.6 times faster than HDD, but it costs 2.6 times more. The performance of HSS, which balances cost and performance optimally, shows more than 70% of SSD search performance with minimal use of the expensive SSD. If the response time and cost are considered as main criteria in comparisons, we conclude that a reasonable storage structure is possible with HSS. The storage system should be able to handle read-write operations of data efficiently. The time needed for write of a certain number of RDF triples is shown in Figure 15. This figure is a graph comparing the performance of \"page size write\" and \"immediate write\" operations.