• Journal of Veterinary Clinics
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• v.31 no.5
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• pp.445-448
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• 2014

A 7-year-old, male Alaskan malamute was referred for a routine dental management. On the physical examination, the right hindlimb ataxia with a mild delay in proprioception was noted. On magnetic resonance images, an extradural ventral cystic structure compressing the spinal cord was found at the level between the first and second lumbar vertebra. The cyst showed hypointense on a T1-weighted image with rim enhancement and hyperintense on a T2-weighted image. The cystic lesion was removed through right-side hemilaminectomy. In the histopathological examination, evenly distributed fibroblasts and collagenous stroma with several cartilaginous materials were seen. The neurological signs of the right hindlimb were successfully recovered within a week in follow-up neurological examination and showed normal gait at 6 months after surgery.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.455-462
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• 2006

Recently, the filtering approach using vector approximation such as VA-file[1] or LPC-file[2] have been proposed to support similarity search in high dimensional data space. This approach filters out many irrelevant vectors by calculating the approximate distance from a query vector using the compact approximations of vectors in database. Accordingly, the total elapsed time for similarity search is reduced because the disk I/O time is eliminated by reading the compact approximations instead of original vectors. However, the search time of the VA-file or LPC-file is not much lessened compared to the brute-force search because it requires a lot of computations for calculating the approximate distance. This paper proposes a new bitmap index structure in order to minimize the calculating time. To improve the calculating speed, a specific value of an object is saved in a bit pattern that shows a spatial position of the feature vector on a data space, and the calculation for a distance between objects is performed by the XOR bit calculation that is much faster than the real vector calculation. According to the experiment, the method that this paper suggests has shortened the total searching time to the extent of about one fourth of the sequential searching time, and to the utmost two times of the existing methods by shortening the great deal of calculating time, although this method has a longer data reading time compared to the existing vector approximation based approach. Consequently, it can be confirmed that we can improve even more the searching performance by shortening the calculating time for filtering of the existing vector approximation methods when the database speed is fast enough.