IJSRST162437 | Received : 28 July 2016 | Accepted : 03 August 2016 | July-August 2016 [(2)4: 180-187] © 2016 IJSRST | Volume 2 | Issue 4 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology 180 Querying Unintelligible Data on Geospatial Trajectory Database Dr. K. Sathesh Kumar 1 , Dr. S. Ramkumar 1 1 Assistant Professor, Department of Computer Science and Information Technology, Kalasalingam University, Krishnankoil, Virudhunagar, Tamil Nadu, India 1 Assistant Professor, Department of Computer Applications, Kalasalingam University, Krishnankoil, Virudhunagar, Tamil Nadu, India ABSTRACT Current GPS technologies collect objects and its movement and store the trajectories periodically in the MOD (Moving Object Database). In such environment, some location errors may arise and some models are unable to capture the changes in trajectories dynamically. Especially, the uncertainty capturing is a challenging one. In order to handle these issues in spatial database, the proposed system develops a new trajectory model to handle the uncertainty and querying on uncertain spatial queries. Initially this develops an adaptable trajectory approach to provide actual positions and temporal changes in uncertainty along with improbable uncertainty ranges. The next part of ongoing implementation provides effective spatial query processing with successful indexing process. This presents the temporal R+ tree indexing with inverted list. This provides an efficient mechanism to evaluate improbable range objects and its spatial queries using Rife-density trajectories. Keywords: RFID sensors, R+Tree, GSM, Trajectory Model, Road Network, Dynamic Route Map. I. INTRODUCTION The spatial database is defines special data types for geometric objects and allows to store geometric data (usually of a geographic nature) in regular database tables. It provides special functions and indexes for querying and manipulating that data using something like Structured Query Language (SQL). While it is often used as just a storage container for spatial data, it can do much more than that. Although a spatial database need not be relational in nature, most of the well-known ones are [1]. Uncertainty management is a central issue in trajectory databases. The research interests, optimization goals, and methodologies in this domain are indeed rich and diverse. Despite this diversity, these studies are generally established upon a common principle location uncertainty is captured by a certain range centered on the position recorded in the database. This principle was initially discussed by Poser and Jensen in the database literature, which is longer than a decade ago [2]. As of today, GPS is no longer the only primary means for positioning, yet a wide spectrum of technologies are being used to produce trajectory data, including RFID sensors, location estimation with 802.11, smart-phone sensors, infrared and ultrasonic systems, GSM beacons, and even vision sensors. These positioning systems typically yield different characteristics of trajectory data, which also exhibit various properties as well as degrees of uncertainty. Uncertain Spatiotemporal Data: Query processing in trajectory databases has received significant interest over the last ten years. Initially, trajectories have been assumed to be certain, by employing linear or more complex types of interpolation to handle missing measurements [12]. These interpolation techniques, however can lead to impossible patterns of movement as, for example, a car might be assumed to drive through a lake. Other solutions such as computing the shortest path between consecutive locations can produce valid results, but do not provide probabilities for quantifying the result quality. As a result, a variety of uncertainty models and query evaluation techniques has been developed for moving object trajectories [28].