# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 6 Performance Evaluation
### Experiment 5--null management  
Undefined value management forms a crucial aspect limiting the performance and evaluation process. As stated, NULL values are not part of the B+index structure. Furthermore, it does not have a specific meaning. We cannot state in the temporal sphere, whether such event will be in the future or not, even whether such events have not occurred yet, particular states cannot be sorted, mathematically treated, and evaluated by the origin of a given undefined value. Therefore, this experiment covers the performance definition protected by undefined value management. The core architecture uses the group granularity evaluated in experiment 1 (Model_group). The environment solution deals with the three models. The reference model (**Model_no_null**) is delimited by the pure spatio-temporal architecture, which cannot cover undefined values in the index structure, at all. **Model_null** covers only undefined values in one category with no origin reflection. It does not have any storage demands, whereas such NULL values are not treated physically. By adding an external module associated with the index root node, NULL values can be placed in the structure and located by the index itself. The demands, in comparison with Model_no_null, are lowered to the value 72%, as the result of the undefined value accessibility by the index by replacing Table Access scanning necessity by the index approach managing data range. Thus, it uses the Index Range Scan method or Unique Scan, respectively, if the condition is based on a unique value set. **Model_null_cat** reflects the memory structure NULL_representation as the pointer layer delimiting the meaning and origin of the undefined values. In this case, 3 categories for treating NULLs are used, however, based on the evaluation, it can be stated, that the number of categories has minimal impact on the performance. The difference is just the pointer destination, however, the size and principle reflection remains still the same. When dealing with the NULL categorization, reached performance results are 73% in comparison with Model_no_null. Comparing index structures dealing with undefined values inside the system (Model_null and Model_null_cat), there is only a slight performance difference, which is, however, fulfilled by obtaining more complex information regarding the undefined value origin and reliability of the data. If the Model_null is referential (treated as 100%), then the Model_null_cat requires additional power ranging 1.38%. Figure 23 shows the results reflecting group granularity with various NULL management definition principles. Figure 22: Insert—Log Ahead Rule (LAR), Temporal Log Ahead Rule (TLAR), Extended Temporal Log Ahead Rule (ETLAR). **Experiment 6--whole solution architecture**  
A specific feature of the spatio-temporal system is just the requirement for extremely fast and efficient processing. Input data are often obtained from sensory networks or systems themselves, where the emphasis is on the almost immediate processing with no delays. Data retrieval efficiency covered by the speed is a critical factor as the states of objects change dynamically. Experiment 6 focuses on the efficiency of obtaining data from the database, comprehensively monitors the process of identification and localization of individual object state records. It uses the modules defined in this paper, the benefits of which are evaluated in the retrieval process depending on their presence in the system. The first used module reflects the Epsilon definition covering just relevant data changes. We used the 0.1% threshold for shifting data into the database or reuse the original value, respectively. In principle, data are provided in the defined time frames, however, the input does not need to be stored in the database, if the significant change is not detected. In that case, the amount of data in the database is lowered with no impact on the data quality. Each data value is then extended by the 0.1% precision interval.