A TEMPORAL RELATIONAL ALGEBRA AS A BASIS FOR TEMPORAL RELATIONAL COMPLETENESS

We define a temporal algebra that is applicable to any temporal relational data model supporting discrete linear bounded time. This algebra has the five basic relational algebra operators extended to the temporal domain and an operator of linear recursion. We show that this algebra has the expressive power of a safe temporal calculus based on the predicate temporal logic with the until and since temporal operators. In [CrC189], a historical calculus was proposed as a basis for historical relational completeness. We propose the temporal algebra defined in this paper and the equivalent temporal calculus as an alternative basis for temporal relational completeness.Information Systems Working Papers Serie

Temporal and Contextual Dependencies in Relational Data Modeling

Although a solid theoretical foundation of relational data modeling has existed for decades, critical reassessment from temporal requirements’ perspective reveals shortcomings in its integrity constraints. We identify the need for this work by discussing how existing relational databases fail to ensure correctness of data when the data to be stored is time sensitive. The analysis presented in this work becomes particularly important in present times where, because of relational databases’ inadequacy to cater to all the requirements, new forms of database systems such as temporal databases, active databases, real time databases, and NoSQL (non-relational) databases have been introduced. In relational databases, temporal requirements have been dealt with either at application level using scripts or through manual assistance, but no attempts have been made to address them at design level. These requirements are the ones that need changing metadata as the time progresses, which remains unsupported by Relational Database Management System (RDBMS) to date. Starting with shortcomings of data, entity, and referential integrity in relational data modeling, we propose a new form of integrity that works at a more detailed level of granularity. We also present several important concepts including temporal dependency, contextual dependency, and cell level integrity. We then introduce cellular-constraints to implement the proposed integrity and dependencies, and also how they can be incorporated into the relational data model to enable RDBMS to handle temporal requirements in future. Overall, we provide a formal description to address the temporal requirements’ problem in relational data model, and design a framework for solving this problem. We have supplemented our proposition using examples, experiments and results

ON COMPLETENESS OF HISTORICAL RELATIONAL DATA MODELS

Several proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations that are defined as part of these extended model, and into the operations of the extended relational algebra or calculus component of the models. Because of these differences it has been difficult to compare the proposed models and to make judgements as to which of them is "better" or indeed, the "best." In this paper we propose a notion of historical relational completeness, analogous to Codd's notion of relational completeness, and examine several historical relational proposals in light of this standard.Information Systems Working Papers Serie

Towards a query language for annotation graphs

The multidimensional, heterogeneous, and temporal nature of speech databases raises interesting challenges for representation and query. Recently, annotation graphs have been proposed as a general-purpose representational framework for speech databases. Typical queries on annotation graphs require path expressions similar to those used in semistructured query languages. However, the underlying model is rather different from the customary graph models for semistructured data: the graph is acyclic and unrooted, and both temporal and inclusion relationships are important. We develop a query language and describe optimization techniques for an underlying relational representation.Comment: 8 pages, 10 figure

Temporal and Relational Models for Causality: Representation and Learning

Discovering causal dependence is central to understanding the behavior of complex systems and to selecting actions that will achieve particular outcomes. The majority of work in this area has focused on propositional domains, where data instances are assumed to be independent and identically distributed (i.i.d.). However, many real-world domains are inherently relational, i.e., they consist of multiple types of entities that interact with each other, and temporal, i.e., they change over time. This thesis focuses on causal modeling for these more complex relational and temporal domains. This thesis provides an in-depth investigation of the properties of relational models and is extending their expressivity to include a temporal dimension. Specifically, we first investigate alternative ways to ground relational models, and we provide an in-depth analysis of the impact of alternative grounding semantics for feature construction, causal effect estimation, and model selection. Then, we extend relational models to represent discrete time. We generalize the theory of d-separation for this class of temporal and relational models. Finally, we provide a constraint-based algorithm, TRCD, to learn the structure of temporal relational models from data

Temporal Support in Relational Databases

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. © 2012 Higher Education AcademyThis paper examines the current state of temporal support in relational databases and the type of situations where we need that support. There has been much research in this area and there were attempts in the American National Standards Institute (ANSI) and the International Organisation for Standardisation (ISO) standards committees in the late 1990s to add an extension called TSQL2 to the existing SQL standard. However no agreement could be reached as it was felt that some of the suggested extensions did not fit well with the relational model, as well as being difficult to implement. TSQL2 was abandoned and since then vendors have added their own data types, and if we are lucky, operators too in an attempt to provide support. However, to novice students and database designers it is often not apparent why some temporal concepts are difficult to deal with in a relational database. In teaching these concepts to students we use a Case Study (based on a real example) which illustrates the problems of providing temporal support by using examples of the data types which could be useful to solve temporal problems and the operators which are necessary to provide this

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