Querying and Manipulating Temporal Databases

Time characterizes every aspect of our life and its management when storing and querying data is very important. In this paper we propose a new temporal query language, called T4SQL, supporting multiple temporal dimensions of data. Besides the well-known valid and transaction times, it encompasses two additional temporal dimensions, namely, availability and event times. The availability time records when information is known and treated as true by the information system; the event times record the occurrence times of both the event that starts the valid time and the event that ends it. T4SQL is capable to deal with different temporal semantics (atemporal aka non-sequenced, current, sequenced, next) with respect to every temporal dimension. Moreover, T4SQL provides a novel temporal grouping clause and an orthogonal management of temporal properties when defining the selection condition(s) and the schema for the output relation.

1995

Information Systems, 1990

Temporal databases maintain not only current data of an enterprise but also the history of evolution for the current data (i.e. temporal data). In order to access an object from temporal databases, query languages should have facilities of specifying time. A11 users will use not only ubsolute rime (e.g. My 1987) but also ubsrract time (or relative time) (e.g. last summer) for specifying the time that an event occurred. In this paper, we designed an extended temporal query language (ETQL) which supports both absolute time and abstract time specification. An introduction of the abstract time concept in the temporal query language provides users with friendliness and easiness in specifying time. The ETQL was implemented as front-end system on INGRES DBMS running under the 4.2BSD UNIX system.

Lecture Notes in Computer Science, 1991

A question that always arises when dealing with temporal information is the granularity of the values in the domain type. Many different approaches have been proposed; however, the community has not yet come to a basic agreement. Most published temporal representations simplify the issue which leads to difficulties in practical applications. In this paper, we resolve the issue of temporal representation by requiring two domain types (event times and intervals), formalize useful temporal semantics, and extend the relational operations in such a way that temporal extensions fit into a relational representation. Under these considerations, a database system that deals with temporal data can not only present consistent temporal semantics to users but perform consistent computational sequences on temporal data from diverse sources.

Business Intelligence and Big Data, 2018

Despite the ubiquity of temporal data and considerable research on the effective and efficient processing of such data, database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of temporal data that captures multiple states of reality. The SQL:2011 standard incorporates some temporal support, and commercial DBMSs have started to offer temporal functionality in a step-by-step manner, such as the representation of temporal intervals, temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states. This tutorial gives an overview of state-of-the-art research results and technologies for storing, managing, and processing temporal data in relational database management systems. Following an introduction that offers a historical perspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by existing systems. The tutorial ends by covering a recently proposed framework that provides comprehensive support for processing temporal data and that has been implemented in PostgreSQL.

Conceptual Modeling, 2018

Many real world applications need to capture a mix of temporal and non-temporal entities, relationships and attributes. These concepts add complexity when designing database schemas and existing works are unable to capture the temporal semantics precisely. We propose a new framework for designing SQL databases that distinguishes between temporal and non-temporal concepts while also distinguishing between entities, relationships and attributes at every step. The framework first utilizes an Entity-Relationship (ER) diagram to capture the real world semantics. Temporal constructs in the ER diagram are then annotated. Finally we map the temporal ER diagram to a normal form database schema that reduces redundant data by separating current data from historical data. We also describe how data consistency is maintained during updates. Experiment results show that we can generate database schemas that support efficient access to both current and historical information, and enable better management of temporal data.

Database Systems for Advanced Applications '97 - Proceedings of the Fifth International Conference on Database Systems for Advanced Applications, 1997

A wide range of database applications manage timevarying data, and it is well-known that querying and correctly updating time-varying data is dificult and error-prone when using standard SQL. Temporal extensions of SQL ofSeer substantial benefits over SQL when managing time-varying data. The topic of this paper is the effective implementation of temporally extended SQL's. Traditionally, it has been assumed that a temporal DBMS must be built from scratch, utilizing new technologies for storage, indexing, query optimization, concurrency control, and recovery. In contrast, this paper explores the concepts and techniques involved in implementing a temporally enhanced SQL while maximally reusing the facilities of an existing SQL implementation. The topics covered span the choice of an adequate timestamp domain that includes the time van'able "NOW," a comparison. of query processing architectures, and transaction processing, the latter including how to ensure ACID properties and assign timestamps to updates.

IEEE Transactions on …, 1997

We consider the representation of temporal data based on tuple and attribute timestamping. We identify the requirements in modeling temporal data and elaborate on their implications in the expressive power of temporal query languages. We introduce a temporal relational data model where N1NF relations and attribute timestamping are used and one level of nesting is allowed. For this model, a nested relational tuple calculus (NTC) is defined. We follow a comparative approach in evaluating the expressive power of temporal query languages, using NTC as a metric and comparing it with the existing temporal query languages. We prove that NTC subsumes the expressive power of these query languages. We also demonstrate how various temporal relational models can be obtained from our temporal relations by NTC and give equivalent NTC expressions for their languages. Furthermore, we show the equivalence of intervals and temporal elements (sets) as timestamps in our model. Index Terms-Attribute timestamping, expressive power of temporal query languages, N1NF relations, temporal relational algebra, temporal relational calculus, temporal relational completeness, temporal relations, tuple timestamping.

Information Sciences, 1994

In this paper we identify and present three distinct time concepts for precise and lossless information preservation in temporal databases. We provide a formal definition of temporal validity and further extend it to the notion of interpretation-based validity, with which the confusion among various time concepts introduced earlier for temporal databases is resolved. Then, we discuss the problem of preserving multiple past states of a temporal database, which leads to the identification of a maximal set of time concepts. It is shown that the time concept event time is needed to correctly model retroactive and proactive updates, as it is not possible to model them using only the valid and transaction times as thought earlier. We also show the adequacy of three time concepts (event time, along with valid and transaction times) for completely preserving different past states generated by retroactive and proactive updates, error corrections, and delayed updates. In addition, we define the evolution of an object (i.e., object's history) along a single time dimension (valid time) by using temporal and interpretation-based validity. Finally, we sketch the implementation of history for the relational data model. 'Note that the effective time and registration time, similar to the logical time and physical time, respectively, were proposed earlier in [4].

Sigmod Record, 1994

Temporal databases has been an active area of research for the the last fteen years, with a corpus nearing 800 papers. While most applications need to store time-varying data, there are no widely used commercial temporal databases. A primary reason for the absence of technology transfer from research to practice is the lack of a commonly accepted consensus data model or query language upon which to base research and development. Even the terminology is inconsistent.