# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 6 Performance Evaluation
In object-level architecture, the whole data image is present there, so it can be directly provided. Thus, from the size demands, requirements are significantly high, however, when dealing with the whole data image, particular data are directly in the database with no necessity for state composition  
Figure 19: Data retrieval (change monitoring). Figure 18: Size demands. and calculation. On the other hand, there is a relevant demand for the data loading from the database into the instance memory Buffer cache. Figure 20 shows the results. Reference model is Model_obj (100%). Improved model using object architecture dealing with the real change identification brings additional demands ranging 2%. The reason is based on the transformation necessary to the original form during the retrieval. It requires 10%, however, the demands are lowered using 8%, whereas the size of the structure is less, thus the number of blocks to be loaded is smaller. Attribute-oriented architecture, in comparison with object architecture, requires an additional 33%. The processing itself can be divided into two parts--object state composition from the individual attribute set--41% lowered by the block loading necessity--8%. Similar solutions are obtained by the group architecture, as well, additional necessity to decompose group is identified. Based on the results, it requires an additional 4 or 5%, respectively, in comparison with column granularity. Figure 20 shows the results. **Experiment 3--adding new states**  
In conventional systems, individual changes are defined by the Update statement replacing the original state with the newer one. The new object is created by invoking the Insert statement. In a spatial environment, each object is delimited by the temporal scheme expressing validity, respectively of the period of occurrence. Thus, any change is physically modeled by the Insert statement, either in the case of adding a new state or the whole object definition, as well. This experiment aims to evaluate the demands of adding a new state into the database in terms of processing time. As defined, 5.4 million rows are present in the database after the operation by using various granularity levels. We compare the processing time to load the whole data set into the database with emphasis on the real change identification for the column level or synchronization detection used in group-level granularity. Each change is delimited by the separate transaction, data are logged and afterward stored in the database. No temporal consistency and model management is done, at all. Reference model is Model_obj (100%). If the same values are not stored physically, just the specific NULL category pointer is used, demands are 99% (Model_obj_imp). Physically, it requires additional demands in 7%, which is, however, lowered by the fewer size demands, whereas physically, the only pointer is used. By using column architecture, only real change is stored. In comparison with object architecture, a new row into the temporal layer is added only in the case of the real change. Object-level structure stores data values for each attribute. Column level architecture Insert statement demands are 64% in comparison with object architecture. When dealing with group management, demands can be lowered, slightly. Processing time value of the group architecture dealing with only time representation reflects 58%, spatial categorization into the regions 60%. It is the result of another processing layer and region management segmentation. Figure 21 shows the results. Figure 20: Data retrieval (change monitoring).