Temporal Databases

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A REPORT
ON

TEMPORAL DATABASES

ABSTRACT

Commercial data bases store data in a relational model. Each record represents a snapshot of the data at an instance. Practical applications need the temporal behavior of the data and hence time becomes another dimension. The SOP aims at different techniques used to store the temporal data in optimum way. A database is a system intended to organize, store, and retrieve large amounts of data easily. A temporal database is a database with built-in time aspects. Temporal aspects usually include valid-time and transaction-time. Databases are managed using database management system, which store database contents, allowing data creation and maintenance, and search and other access. Valid time denotes the time period during which a fact is true with respect to the real world. Transaction time is the time period during which a fact is stored in the database. Temporal Data Models are extension of relational model by adding temporal attributes to each relation. In valid time databases, two-dimensional relational tables are extended to incorporate time as a third dimension. Two types of temporal tables-

Event tables, which hold instant timestamps, and
State tables, which hold interval timestamps.
A spatiotemporal database is a system that manages both space and time information, such as biological Databases, wireless communication networks, and processing of objects with uncertainty. There are many temporal models available today. Some of them are 1NF models, Non 1NF models, parametric model, etc. There are two types of 1NF models- The Timestamp model, the snapshot model. To handle Bi-temporal data, models like Bi-temporal Conceptual Data model (BCDM) are used. A query language called TSQL, which has been designed for querying a temporal database. TSQL was proposed by S.B. Navathe and R. Ahmed, 1993. TSQL is a superset of SQL and introduces several new semantics and syntactic components. TSQL add the following new constructs to standard SQL: - Conditional temporal expressions using the WHEN clause

- Retrieval of timestamp values with or without computation

TABLE OF CONTENTS

1. Introduction to temporal databases
2. Temporal models
2.1 1NF models
2.1.1 The timestamp model
2.1.2 The snapshot model
2.2 Non 1NF models
2.3 Parametric model
3. SQL and Temporal Query Language
4. Conclusions
5. Future scope and work
6. References

CHAPTER 1
INTRODUCTION TO TEMPORAL DATABASES

A wide range of database applications manage time-varying data. In contrast, existing database technology provides little support for managing such data. The research area of temporal databases aims to change this state of affairs by characterizing the semantics of temporal data and providing expressive and efficient ways to model, store, and query temporal data. This chapter offers a brief introduction to temporal database research. It concisely introduces fundamental temporal database concepts, surveys state-of-the-art solutions to challenging aspects of temporal data management, and also offers a look into the future of temporal database research. Most applications of database technology are temporal in nature. Examples include financial applications such as portfolio management, accounting, and banking; record-keeping applications such as personnel, medical-record, and inventory management; scheduling applications such as airline, train, and hotel reservations and project management; and scientific applications such as weather monitoring. Applications such as these rely on temporal databases, which record time referenced data. The facts recorded by the database entities are of fundamental interest. Several different temporal aspects may be associated with these. Most importantly, the valid time of a fact is the collected times—possibly spanning the past, present, and future—when the fact is true in the mini-world. Valid time thus captures the time-varying...
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