TPC-BiH: A Benchmark for Bitemporal Databases (original) (raw)

Towards an infrastructure for temporal databases-a workshop report

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.

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.

Benchmarking Bitemporal Database Systems: Ready for the Future or Stuck in the Past?

After more than a decade of a virtual standstill, the adoption of temporal data management features has recently picked up speed, driven by customer demand and the inclusion of temporal expressions into SQL:2011. Most of the big commercial DBMS now include support for bitemporal data and operators. In this paper, we perform a thorough analysis of these commercial temporal DBMS: We investigate their architecture, determine their performance and study the impact of performance tuning. This analysis utilizes our recent (TPCTC 2013) benchmark proposal, which includes a comprehensive temporal workload definition. The results of our analysis show that the support for temporal data is still in its infancy: All systems store their data in regular, statically partitioned tables and rely on standard indexes as well as query rewrites for their operations. As shown by our measurements, this causes considerable performance variations on slight workload variations and significant overhead even after extensive tuning.

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.

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.

Towards an infrastructure for temporal databases: report of an invitational ARPA/NSF workshop

1994

Temporal data. bases has been an active a. rea of research for the last fifteen years, with a corpus nearing 700 papers [Kline93]. Most d&abase conferences include at least one paper on temporal databases (TDB). Temporal databases are now discussed in several undergraduate database textbooks. There are perhaps one hundred researchers actively studying temporal databases.

A History-oriented Temporal SQL Extension

Dozens of temporal extension of the relational data model and of the query language SQL have appeared in recent years. Recently, a committee formed by researchers from the academic and the industrial worlds designed a consensual extension of the SQL-92 standard to include time, epitomized as TSQL2.

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.

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.