Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Clustering in Object-Oriented Databases: An Advocacy for
  Simplicity   We present in this paper three dynamic clustering techniques for
Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical &
Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage
statistics and the inter-object reference graph. They are quite elaborate.
However, they are also complex to implement and induce a high overhead. The
third clustering technique, called Detection & Reclustering of Objects (DRO),
is based on the same principles, but is much simpler to implement. These three
clustering algorithm have been implemented in the Texas persistent object store
and compared in terms of clustering efficiency (i.e., overall performance
increase) and overhead using the Object Clustering Benchmark (OCB). The results
obtained showed that DRO induced a lighter overhead while still achieving
better overall performance.

5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
18
The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess