Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture

Abstract

Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to the granularity level is proposed. Object, attribute, and synchronization group perspectives are discussed. An extension of the group temporal architecture shifting the processing in the spatio-temporal level synchronization is proposed. A data reflection model is proposed to cover the transaction integrity with reflection to the data model evolving over time. It is supervised by our own Extended Temporal Log Ahead Rule, evaluating not only collisions themselves, but the data model is reflected, as well. The main emphasis is on the data retrieval process and indexing with regards to the non-reliable data. Undefined value categorization supervised by the NULL_representation data dictionary object and memory pointer layer is provided. Therefore, undefined (NULL) values can be part of the index structure. The definition and selection of the technology of the master index is proposed and discussed. It allows the index to be used as a way to identify blocks with relevant data, which is of practical importance in temporal systems where data fragmentation often occurs. The last part deals with the syntax of the Select statement extension covering temporal environment with regards on the conventional syntax reflection. Event_definition, spatial_positions, model_reflection, consistency_model, epsilon_definition, monitored_data_set, type_of_granularity, and NULL_category clauses are introduced. Impact on the performance of the data manipulation operations is evaluated in the performance section highlighting temporal architectures, Insert, Update and Select statements forming core performance characteristics.

Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors

“…SEQ is a component of the PREDATOR database system that supports relational and other kinds of complex data [37]. Recently, extensive work dealing with temporal data at a lower level has been researched by Kvet et al [38].…”