# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 6 Performance Evaluation
### Experiment 4--LAR, TLAR, ETLAR  
In the previous experiment, only a standardized conventional LAR approach was used. Particular data were logged and stored in the database based on the defined granularity. Data integrity, collisions, and model reflection were, however, not taken into emphasis. This section deals with the proposed rules TLAR and ETLAR referencing the current state of the art solution defined by the Log Ahead Rule. TLAR extends the definition by identifying and solving transaction rules to limit the overlapping states on the one object. Several rules can be applied to ensure consistency--a new valid state is either refused (**REFUSE**) or the validity of either the existing or new state is shortened (**SHORTEN**) or the states are moved in time (**REPOSITION**). This experiment evaluates the processing time demands or those three rules. The used architecture is group-oriented used as a reference model (100%). 20% of states to be added cause anomaly collision. If the REFUSE rule is used, the demands are even lowered, whereas the data themselves are not loaded into the database, only information about the temporal attempt to insert a new state is noticed. The SHORTEN rule is the most demanding, whereas the data themselves must be changed by locating existing and newly added rows. If the existing row must be updated, the total demands have risen to 109% reflecting the 9% of additional demands in comparison with the reference model LAR. In the newly inserted row, validity is changed, additional demands are 5%, whereas either begin point or end point of the validity must be shifted (shortened). In this criterion, defined states do not force the system to refuse the state completely, so there is only partial overlapping of the existing state. In this case, based on the data, we assume, that only one valid state is influenced. If multiple states are necessary to be evaluated, in general, approximately 0.5% for each state is added to the processing time demands. REPOSITION criterion influences the time positions, validity interval in terms of its length is not influenced, at all, however, the period placement into the timeline is enhanced. In that case, 3% of additional processing time costs are present, as the result of the placement identification. Similarly, we assume, that there is a collision with one state only. The ETLAR rule extends the previously defined criteria by covering the model relevance. If the particular model is not present, the newly added state can be either refused (ETLAR strict) or the current data model can be reflected (ETLAR ignore). Management of the integrity of the time-evolving data model and constraints require an additional 4% of the processing time, however, the consistency can be always ensured, no integrity constraint lacks can be later detected. In the case of using the Ignore option, additional demands are 6%, whereas the new state is always inserted with the specific flag regarding the particular model availability. Note, that by using the Strict rule, the state to be loaded can be refused completely, forcing the system not to insert data into the database, which has namely benefits in the disc operations.  
Figure 22 shows the reached results for the 100 driver data sensor evaluation.  
Figure 21: Data loading (Insert statement).