Georgian Electronic Scientific Journal: Computer Science and Telecommunications 2007|No. 3(14)
A Spatial Regression analysis Model for temporal Data mining in
estimation of traffic data over a busy area
1A.V.N.Krishna, 2M.Pavan Roy.
1Professor, Indur Institute of Engg. & Tech. Siddipet, Medak Dist. Andhra Pradesh, India.
2Asst.Professor, , Indur Institute of Engg. & Tech. Siddipet, Medak Dist. Andhra Pradesh, India.
Cell : 9849520995. Email: email@example.com
Many applications maintain temporal & spatial features in their databases.
These features cannot be treated as any other attributes and need special attention.
Temporal data mining has the capability to infer casual and temporal proximity
relationships among different components of data.
In this work a model is going to be developed which helps in measuring traffic
data distributed over a wide area. This model considers the assumption that the data
fallow ordered sequence. The area is divided into a set of grid points. Each grid point
is identified by a set of coefficients. The traffic data at a set of locations is measured.
The coefficients at the identified locations are calculated from the measured traffic
data value. These coefficients are used to generate the traffic data at the other grid
points by spatial regression analysis method. The procedure is repeated till the values
cease to change for unit time. The procedure is repeated for different intervals of
time. Thus traffic data is obtained over the wide area for different times and at
Spatial regression analysis , traffic data, temporal and spatial data, example.
Temporal data mining is an important extension of data mining and it can be defined as the non
trivial extraction of implicit, potentially useful and previously unrecorded information with an
implicit or explicit temporal content from large quantities of data. It has the capability to infer
casual and temporal proximity relationships and t