# A Distributed Indexing Method for Timeline Similarity Query
## 5 Experimental Evaluation
It is about 1.6 TB and represents the long timelines. The experimental results of index construction performance test are shown in Figure 13. The X axis is the number of timeline objects involved in the experiment. The Y axis is the time cost. The DTI-Tree and R-Tree were constructed with the number of timeline objects varying from 1,000,000 to 10,000,000. There are eight fixed partitions for running this test. The result shows that the DTI-Tree has higher construction performance than the R-Tree. The result also shows that the DTI-Tree has lower performance when it handles the long timeline dataset DS2. The reason is that the longer the average length of the intervals, the bigger the dataset, and the computation takes a longer time to construct the DTI-Tree. Therefore, it is better to implement the DTI-Tree construction by bulk loading of the dataset to reduce the Spark RDD construction time cost. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline maxTimeValue & the maximum time value of all the timelines generated by generator, the default value is 0. \\ \hline minTimeValue & the minimum time value of all the timelines generated by generator, the default value is 10,000. \\ \hline timelineMinLength & the shortest length of a timeline generated by generator, the default is 10. \\ \hline timelineMaxLength & the longest length of a timeline generated by generator, the default is 50. \\ \hline intervalMinLength & the shortest length of an interval generated by generator, the default is 2. \\ \hline intervalMaxLength & the longest length of an interval generated by generator, the default is 10. \\ \hline intervalAttachment & the range of the attachment data size, the default is [1K, 2K] \\ \hline labelTypes & the maximum types involved in the dataset generated by generator, the default value is 5. \\ \hline numberTimelines & the total number of the timelines generated by generator, the default value is 10,000,000. \\ \hline outputFileName & the generated timeline data file. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of TimelineGenerator. Figure 13: Construction comparison. The experimental results of insertion performance comparison by varying number of timelines are shown in Figure 14. The X axis represents time cost, it is the average time for 100 times operations. The number of timelines varies from 1,000,000 to 10,000,000. The result shows that the DTI-Tree has higher performance than R-tree. In addition, the average length of the timelines affects the insertion performance. Because the TI-Tree manages all of the interval datasets, the leaf node map and empty leaf node list of the TI-Tree have a bigger size, while the Number of intervals increases. The insertion operations on the map and list will take more time. Another we can observe from the results is that the average time cost of deletion is slightly smaller than the insertion's. It is because that the empty leaf node list may be scanned when a new leaf node appears, and the empty leaf node list will involve every splitting in the process of insertion. However, there are not these operations in the process of deletion. There is only one case that the empty leaf node list will be add an element when the leaf node only contains one interval object, which will be deleted from the leaf node. Figure 15 shows the relationship between the average time cost of similarity query and the total number of timeline objects that are involved in each query. The number of timelines varies from 1,000,000 to 10,000,000 in DS1 and DS2. The DTI-Tree has better performance when it is indexing the short timelines dataset DS1. The main time cost of similarity query is to receive the intervals that are related to the query timeline. The major influenced factor is the leaf node map size in each TI-Tree. The more the intervals are, the bigger the leaf node map size is. The test result proves this deduction. Figure 14: Insertion comparison. Figure 15: Similarity query performance.