Temper : uma abordagem para modelagem temporal de banco de dados

Several temporal extensions to the original entity-relationship (ER) approach have been proposed in literature. A temporal ER model includes temporal ER elements (entities, relationships and attributes). Temporal ER elements are ER elements that vary with the time. A temporal ER model describes a database that stores the history of changes of the temporal ER êlements. The temporal extensions proposed in literature have some restrictions. Some of the approaches require ali the elements of the model to be temporal, not allowing the use of temporal and not temporal elements at the same time. Other approaches use temporal intervals instead of temporal instants. The semantics of a model based on temporal intervals is more complex if compared to a model based on temporal instants, This complexity results in complex query and manipulation languages. The paper presents the two fundamental concepts in temporal modeling (temporal intervals and temporal instances), discusses temporal modeling approaches and introduces the TempER approach, a temporal modeling approach defined to overcome the identified problems.Várias extensões à abordagem entidade-relacionamento original têm sido propostas com o objetivo de incorporar a possibilidade de modelar propriedades temporais. Estas abordagens, entretanto, apresentam várias restrições. Algumas exigem que o modelo contenha. exclusivamente elementos temporalizados, não permitindo a combinação de elementos temporalizados com não temporalizados. Outras trabalham com intervalos de tempo, ao invés de pontos no tempo. Modelos baseados em intervalos de tempo, têm semântica mais complexa e tornam linguagens de consulta e manipulação mais complexas. O presente artigo apresenta os modelos fundamentais de temporalização que aparecem na literatura (intervalos de tempo e pontos de tempo), discute as abordagens para modelagem temporal existentes e apresenta a abordagem TempER, modelo de dados temporal que procura corrigir os problemas identificados nas demais abordagens

Query processing in temporal object-oriented databases

This PhD thesis is concerned with historical data management in the context of objectoriented databases. An extensible approach has been explored to processing temporal object queries within a uniform query framework. By the uniform framework, we mean temporal queries can be processed within the existing object-oriented framework that is extended from relational framework, by extending the existing query processing techniques and strategies developed for OODBs and RDBs. The unified model of OODBs and RDBs in UmSQL/X has been adopted as a basis for this purpose. A temporal object data model is thereby defined by incorporating a time dimension into this unified model of OODBs and RDBs to form temporal relational-like cubes but with the addition of aggregation and inheritance hierarchies. A query algebra, that accesses objects through these associations of aggregation, inheritance and timereference, is then defined as a general query model /language. Due to the extensive features of our data model and reducibility of the algebra, a layered structure of query processor is presented that provides a uniforrn framework for processing temporal object queries. Within the uniform framework, query transformation is carried out based on a set of transformation rules identified that includes the known relational and object rules plus those pertaining to the time dimension. To evaluate a temporal query involving a path with timereference, a strategy of decomposition is proposed. That is, evaluation of an enhanced path, which is defined to extend a path with time-reference, is decomposed by initially dividing the path into two sub-paths: one containing the time-stamped class that can be optimized by making use of the ordering information of temporal data and another an ordinary sub-path (without time-stamped classes) which can be further decomposed and evaluated using different algorithms. The intermediate results of traversing the two sub-paths are then joined together to create the query output. Algorithms for processing the decomposed query components, i. e., time-related operation algorithms, four join algorithms (nested-loop forward join, sort-merge forward join, nested-loop reverse join and sort-merge reverse join) and their modifications, have been presented with cost analysis and implemented with stream processing techniques using C++. Simulation results are also provided. Both cost analysis and simulation show the effects of time on the query processing algorithms: the join time cost is linearly increased with the expansion in the number of time-epochs (time-dimension in the case of a regular TS). It is also shown that using heuristics that make use of time information can lead to a significant time cost saving. Query processing with incomplete temporal data has also been discussed

Realizing a Temporal Complex-Object Data Model

Support for temporal data continues to be a requirement posed by many applications such as VLSI design and CAD, but also in conventional applications like banking and sales. Furthermore, the strong demand for complex-object support is known as an inherent fact in design applications, and also emerges for advanced “conventional” applications. Thus, new advanced database management systems should include both features, i.e. should support temporal complex-objects. In this paper, we present such a temporal complex-object data model. The central notion of our temporal complex-object data model is a time slice, representing one state of a complex object. We explain the mapping of time slices onto the complex objects supported by the MAD model (which we use for an example of a non-temporal complex-object data model) as well as the transformation process of operations on temporal complexobjects into MAD model operations. Thereby, the basic properties of the MAD model are a prerequisite for our approach. For example, time slices can only be directly stored, if non-disjunct (i.e. overlapping) complex objects are easily handled in the underlying complex-object data model. 1