Object-relational spatio-temporal databases

We present an object-relational model for uniform handling of dimensional data. Spatial, temporal, spatio-temporal and ordinary data are special cases of dimensional data. The said uniformity is achieved through the concept of dimension alignment, which automatically allows lower dimensional data and queries to be used in a higher dimensional context;Unlike ordinary data, dimensional objects are interwoven. We introduce object identity (oid) fragments to circumvent data redundancy at logical level. Computed types are placed appropriately in a type hierarchy to allow maximal use of existing methods. A query language for spatio-temporal data is presented for associative navigation. A framework for algebraic optimization of the query language is suggested;A pattern matching language is designed for complex querying of spatio-temporal data which seamlessly extends the associative navigation in our query language. The pattern matching language recognizes special features of time and space providing an appropriate level of abstraction for application development compared to traditional languages. This reduces the need for embedding the query language in a lower level language such as C++. The pattern matching language is also dimensionally extensible. The pattern matching allows query of data with multiple granularities and continuous data. It also provides hooks for direct query of scientific data (observations);Our model is dimensionally extensible, and also an extension of a relational model for dimensional data. Moreover the dimensionality and addition of oids are mutually orthogonal concepts. Thus starting from classical ordinary data, one may migrate to higher forms of relational or object-relational data in any sequence, without having to recode application software. Our model does not deal with complex objects, which is left as a future extension

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