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Temporal Database

Temporal database systems "are database systems that include special support for the time dimension; in other words, they are systems that provide special facilities for storing, querying, and updating historical and/or future data (referred to generically in this book as temporal data). Conventional database management systems (DBMSs)-- including all of the existing mainstream products-are not temporal in this sense: They provide essentially no special support for temporal data at all, at least at the time of writing."^[1]

The decline in the price of disk storage has led many companies to build data warehouses, this has led many users to encounter problems when trying to access and update historical data, leading “vendors of conventional DBMS products have begun to think about adding temporal database support to those products.”^[2]

Although Research into temporal database systems has existed since the 1980’s, early research was not successful in obtaining results, due to the complexity of obtaining the necessary information to create consistent results, leaving problems unsolved.

Although some have argued that temporal databases are a method of the past, they can still be beneficial in a variety of ways. One way is to manage the time spent by an individual working on an assigned area or machine. The example below shows the workers name, the work area or machine assigned to, their hourly pay, their start time and finish time. Through this example, queries can be established to find the necessary data based on the worker, which will show the individual who worked where and for how long, proving beneficial when determining future budgetary amounts. This can also prove beneficial when trying to determine average time spent by individuals on various tasks, made capable by the ability to use accurate start and finish times when inputting the data. Temporal databases also prevent against loss of data that could occur when an individual, for example Bill in the table below, is no longer needed in his capacity.

Within the framework of temporal databases there are 2 types of databases, a historical database which stores valid times allotted to data and bitemporal database which is used to store data that has both transaction time and valid time.^[3]

Spatial Temporal Database

An example of a temporal database system is a Spatial Temporal Database. This system manages information and data through both space and time, this can include computer networking, chemical experiment data, and time spent working on machine.^[4]In the Journal Information Sciences, the article "Mining Frequent Trajectory Patterns in Spatial–Temporal Databases" touches on a two step process to which Spatial Temporal Databases can be used effectively, by creating an algorithm which mines a database in order to find patterns within the data "Therefore, in this paper, we propose an efficient algorithm that takes into account both spatial and temporal attributes to mine the frequent trajectory patterns. The proposed method comprises two phases. First, we scan the database once to generate a mapping graph and trajectory information lists (TI-lists). Then, we apply the proposed graph-based mining (GBM) algorithm to traverse the mapping graph and mine the frequent trajectory patterns in a depth-first search manner. Since our proposed algorithm uses the adjacency property to reduce the search space, we only need to extend a trajectory to the adjacent neighborhoods of the last location of the trajectory pattern. By using the mapping graph and TI-lists, the GBM algorithm can localize support counting and pattern extension to a small number of TI-lists. Thus, it is more efficient than the Apriori-based and PrefixSpan-based algorithms."^[5] The writers of the article came to the conclusion that although their algorithm provides a faster and more efficient way of searching through data compared to Apriori-based algorithms, there are still flaws within their spatial temporal database algorithm, pertaining to the ability to siphon through data patterns originating from non adjacent points.

After researching the topic one feels that temporal databases are still relevant within todays technological society. Working in a maintenance environment I believe this form of database would be beneficial when analyzing man hours pertaining to an area, an indiviudals hours completed, and the cost of work done by an outside source based on hourly wage and the time frame. Although temporal databases, like the spatial temporal database, encounter issues when trying to analyze how data got from point A to point B, one feels it can be a useful and important tool to buisnesses and individuals alike.

1.Date, C.J., Hugh Darwen, and Nikos A. Lorentzos. Temporal Data & the Relational Model A Detailed Investigation into the Application of Interval and Relation Theory to the Problem of Temporal Database Management. Elsevier, 2003

2.Lee, Anthony J.T., Yi-An Chen, and Weng-Chong Ip. "Mining Frequent Trajectory Patterns in Spatial–temporal Databases." Information Sciences 179.13 (2009): 2218-231.

3.Zhang, L., G. Chen, T. Brijs, and X. Zhang. "Discovering During-temporal Patterns (DTPs) in Large Temporal Databases☆." Expert Systems with Applications 34.2 (2008): 1178-189.