A part of the National Science Foundation Scientific Database Initiative Grant IRI 9116849
Last updated on April 5th, 2001

Sequence Query Processing
In this experiment we shall show MQuery tool to construct a sequence query then using our approximate sequence matching technology to generate a set of matched sequences, which are ranked according to the nearest measure as shown in the following set of figures.


Fig. 1 shows the lung tumor sequence template.


Query 1: Query Sequence with two images

Fig. 2 A query sequence of two CT lung images showing a tumor growing, constructed from the template in fig 1.
Executing the query returns a result viewer window with which we can view the results of the query.

Fig. 3 KMeD searches the sequence database with the approximate matching technique and the best results are returned and shown in the Result Viewer.  The features used for the lung tumor are the distance from the x and y centroid, and lung tumor area.  The features which are represented in a column.  The first four columns represent the features for the first image, and the next four represent the features of the second image.  The distance is the nearness of the answer sequence with the target sequence.  Each row represents an answer, their ranked according the distance.  The corresponding first three image result sequences are shown in Figure 4.

First query result

Second query result


Third query result

Figure 4

Query 2: Query Sequence with three images

Figure 5 A query sequence of three CT lung images showing a tumor growing, constructed from the template in fig 1.

Figure 6  KMeD searches the sequence database with the approximate matching technique and the best results are returned and shown in the Result Viewer.  The features used for the lung tumor are the distance from the x and y centroid, and lung tumor area.  The features which are represented in a column.  The first four columns represent the features for the first image, and the next four represent the features of the second image, and the next four represent the features of the third image.  The distance is the nearness of the answer sequence with the target sequence.  Each row represents an answer, their ranked according the distance.  The corresponding first two image result sequences are shown in Figure 7.

First Result of Query 

Second Result of Query

Figure 7

  1. Image Segmentation
  2. TAH generation
  3. Visual Query/Schema Language
  4. MQuery Results and Visualiation
  5. The OITL Application
  6. Sequence Query
  7. Sequence Query Scalability
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