Selectivity estimation of temporal data manipulations (original) (raw)

Querying and Manipulating Temporal Databases

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.

SQL and Temporal Database Research: Unified Review and Future Directions

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.

CME: A Temporal Relational Model for Efficient Coalescing

12th International Symposium on Temporal Representation and Reasoning (TIME'05), 2005

Coalescing is a data restructuring operation applicable to temporal databases. It merges timestamps of adjacent or overlapping tuples that have identical attribute values. The likelihood that a temporal query employs coalescing is very high. However, coalescing is an expensive and time consuming operation. In this paper 1 we present a novel temporal relational model through which coalescing becomes quite simple. The basic idea is to augment each timevarying attribute in a temporal relation with two additional attributes that trace changes in values of the corresponding time-varying attribute. One attribute traces changes in values with respect to each individual instance (i.e. tuples having the same key value), while the other attribute traces changes in values globally for all instances (i.e. all tuples in the temporal relation). Using these tracing attributes, coalescing could be easily implemented through a quite simple join-free group-by query. The coalescing query is fully processed and optimized by the underlying database management system.

A SCHEME FOR TEMPORAL DATABASES

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.

An Empirical Study of the Performance of Temporal Relational Databases

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.

The time index: An access structure for temporal data

Proceedings of the 16th International …, 1990

In this paper, we describe a new indexing technique, the time indez, for improving the performance of certain classes of temporal queries. The time index can be used to retrieve versions of objects that are valid during a specific time period. It supports the processing of the temporal WHEN operator and temporal aggregate functions efficiently. The time indexing scheme is also extended to improve the performance of the temporal SELECT operator, which retrieves objects that satisfy a certain condition during a specific time period. We will describe the indexing technique, and its search and insertion algorithms. We also describe an algorithm for processing a commonly used temporal JOIN operation. Some results of a simulation for comparing the performance of the time index with other proposed temporal access structures are presented.

Dealing with granularity of time in temporal databases

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.

Temporal Data Management – An Overview

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.

Modeling and Querying Temporal Data

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.

A novel approach to model NOW in temporal databases

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.