Point-versus interval-based temporal data models

10th International Symposium on Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic. Proceedings., 2003

In bitemporal databases, current facts and transaction states are modelled using a special value to represent the current time (such as a minimum or maximum timestamp or NULL). Previous studies indicate that the choice of value for now (i.e. the current time) significantly influences the efficiency of accessing bitemporal data. This paper introduces a new approach to represent now, in which current tuples and facts are represented as points on the transaction time and valid time line respectively. This allows us to exploit the computational advantages of point-based query languages. Via an empirical study, we demonstrate that our new approach to representing now offers considerable performance benefits over existing techniques for accessing bitemporal data.

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.

1995

Abstract When querying a temporal database, a user often makes certain semantic assumptions on stored temporal data. This paper formalizes and studies two types of semantic assumptions: point-based and interval-based. The point-based assumptions include those assumptions that use interpolation methods, while the interval-based assumptions include those that involve different temporal types (time granularities).

1994

Abstract In this paper we describe an implementation of a temporal relational database management system based on attribute timestamping. For this purpose we modify an existing software 6] which supports set-valued attributes. The algebraic language of the system includes relational algebra operators, restructuring operators and temporal operators.

Computing Research Repository, 2011

Many works have focused, for over twenty five years, on the integration of the time dimension in databases (DB). However, the standard SQL3 does not yet allow easy definition, manipulation and querying of temporal DBs. In this paper, we study how we can simplify querying and manipulating temporal facts in SQL3, using a model that integrates time in a native manner. To do this, we propose new keywords and syntax to define different temporal versions for many relational operators and functions used in SQL. It then becomes possible to perform various queries and updates appropriate to temporal facts. We illustrate the use of these proposals on many examples from a real application.

Encyclopedia of Database Technologies and Applications

Databases in general store current data. However, the capability to maintain temporal data is a crucial requirement for many organizations and provides the base for organizational intelligence. A temporal database has a time dimension and maintains time-varying data (i.e., past, present, and future data). In this article, we focus on the relational data model and address the subtle issues in modeling temporal data, such as comparing database states at two different time points, capturing the periods for concurrent events, and accessing to times beyond these periods, handling multivalued attributes, coalescing, and restructuring temporal data (Gadia 1988, Tansel & Tin, 1997). Many extensions to the relational data model have been proposed for handling temporal data.

IEEE Transactions on Knowledge and Data Engineering, 1998

Data explicitly stored in a temporal database are often associated with certain semantic assumptions. Each assumption can be viewed as a way of deriving implicit information from explicitly stored data. Rather than leaving the task of deriving (possibly infinite) implicit data to application programs, as is the case currently, it is desirable that this be handled by the database management system. To achieve this, this paper formalizes and studies two types of semantic assumptions: point-based and interval-based. The point-based assumptions include those assumptions that use interpolation methods over values at different time instants, while the interval-based assumptions include those that involve the conversion of values across different time granularities. The paper presents techniques on: 1) how assumptions on specific sets of attributes can be automatically derived from the specification of interpolation and conversion functions, and 2) given the representation of assumptions, how a user query can be converted into a system query such that the answer of this system query over the explicit data is the same as that of the user query over the explicit and the implicit data. To precisely illustrate concepts and algorithms, the paper uses a logic-based abstract query language. The paper also shows how the same concepts can be applied to concrete temporal query languages.

Arxiv preprint arXiv:1002.1143, 2010

Abstract: Time is one of the most difficult aspects to handle in real world applications such as database systems. Relational database management systems proposed by Codd offer very little built-in query language support for temporal data management. The model itself ...

2016

Modeling temporal database over relational database<br> using 1NF model is considered the most popular approach. This<br> is because of the easy implementation as well as the modeling and<br> querying power of 1NF model. In this paper, we compare a new<br> approach for representing valid-time temporal database (in<br> terms of structure and performance) to the main models in<br> literature with attribute and tuple timestamping. The<br> measurement of the performance is represented by the<br> processing time to get the required temporal data as well as the<br> size of the whole stored temporal data. A test has been performed<br> by running sample queries for the same data in the represented<br> models. Based on the tests, we have found that the new proposed<br> model required less time and used less disk space. Therefore, it is<br> more appropriate for modeling 1NF with interval-based<br> timestamping...

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.

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.

This research try to address several issues related to multiple relations time-stamps temporal databases and the development of temporal databases. A new hash-clustered index structure has been designed to accommodate efficient access for tuples that are indexed on time-stamps. Furthermore, new time intersection equi-join algorithms have been developed. These algorithms have been designed to handle special types of temporal relations, such like continuous and event dependents temporal relations. These algorithms have been implemented and the tests' results prove the correctness of the algorithms.

Several attempts to incorporate temporal extensions into the Structured Query Language, SQL, one of the most popular query languages for databases date back to the nineteenth and twentieth century. Although a lot of work and research has been done on temporal databases and SQL, there exist very limited literature clearly outlining the various events which have taken place with regards to temporal extensions of SQL over the years till the present state in a concise document. Consequently, researchers need to gather several pieces of literature before they can obtain a vivid pictorial timeline of the history and the current state of these temporal extensions for research and software development purposes.

1995

Based on a systematic study of the semantics of temporal attributes of entities, this paper provides new guidelines for the design of temporal relational databases. The notions of observation and update patterns of an attribute capture when the attribute changes value and when the changes are recorded in the database. A lifespan describes when an attribute has a value. And derivation functions describe how the values of an attribute for all times within its lifespan are computed from stored values. The implications for temporal database design of the semantics that may be captured using these concepts are formulated as schema decomposition rules.