TIME-SERIES DATABASE OPERATORS AND MANAGEMENT

Abstract

Time-series is an important aspect of a decision support system. Unfortunately, traditional relational database management systems (RDBMS) allows only two dimensions query processing. It does not support complex multidimensional data. Much of the researches is aimed at extending a new dimension which able to handle the multidimensional data. Temporal Database and its relevant research areas: Multidimensional database, OLAP (On-line Analytical processing) etc. have been areas of active research and are producing useful results. This paper discusses some new time-series database operators which are based on the relational database. It simulates the time dimension by adding two new special attributes (time begin and time end) and offers its retrieval, creative and computation capabilities. Taking advantage of a traditional database SQL and some procedures, time series database operators handle time-related queries, analyze the time-series data and create new time-series for business forecasting. The operators related algorithms are proposed. This research makes a number of contributions to the area of time-series database, including new operators combine relational SQL and some new procedures for creating new time-series and query processing.