# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 7 Conclusions and Future Work  
The core element influencing the quality and usability of any information system is associated with the data, their correctness, reliability, and accessibility in an effective manner. Currently, it is inevitable to store not only current valid data, but the emphasis should be taken to the whole evolution over time. The temporal database approach forms a robust solution, treating the time delimited states of the objects. In this paper, we summarize existing approaches dealing with the data granularity--the object-level temporal system extending the definition by the validity time frame. Column level architecture deals with data monitoring using the attribute level. The interlayer between object and column architecture is covered by the synchronization groups. In this paper, we propose the extension of the group solution in a spatial manner categorizing data into the regions. When dealing with the data, whole integrity aspects must be cared for. Data models and integrity rules can evolve over time, as well. Therefore, we propose an extended conventional Log Ahead Rule dealing with either data state collisions in a temporal manner (TLAR), whereas each object can be defined just by one valid state anytime or extended version (ETLAR) covering temporal collisions, as well as data model relevance. Thanks to that, complex integrity can be ensured. Such rules are performance evaluated, as well. Undefined values originating from various circumstances can be present in the input data stream, as well. If the untrusted ad-hoc network is present, the problem can be even deeper. In this paper, we discuss the costs and benefits of undefined values. NULL representation is not physically placed in the database, thus it does not additional store demands. On the other hand, dealing with NULL proposes a significant performance problem by locating data. Generally, data are obtained by the indexes, which cover logarithmic complexity instead of linear by using the Table Access Full method scanning block by block sequentially. The most often index used in the relational platform is B+tree, which cannot, however, cover undefined values inside it. As the consequence, when dealing with undefined values, which are most likely present in the systems, the whole block set associated with the table must be scanned, resulting in poor performance. Note, that the data fragmentation can be present there, forcing the system to load the particular block with no relevant data into the memory for the evaluation. In this paper, a complex solution for managing undefined values directly inside the index is present. It is based on an additional structure connected to the root index element. NULL values can have several representations and meanings, therefore we propose undefined data value type categorization. In this paper, we propose the NULL_representation data dictionary object and memory pointer layer interconnected with the index module. Thanks to that, the index can be always used, whereas all references are present there. The benefit of this paper is covered in Section 5, as well, covering the data retrieval process. No additional clauses dealing with the spatio-temporality are present, therefore we propose transformation clauses Event_definitions, Spatial_positions, Transaction_reliability, Model_reflection, Consistency_model, Epsilon_definition, and Monitored_data set operated in the Where condition of the Select statement. Type_of_granualarity and Null_category clause domains can be present in the Having clause, by which the output format can be treated. Evaluation of the proposed methods is divided into multiple experiments--impact granularity, transaction rules definition, indexing managing NULLs. The whole architecture is covered by experiment 6, which deals with the various modules, which are evaluated to emphasize the performance impacts.