面向对象数据库中的动态聚类：对简单性的倡导
Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity
摘要：
本文提出了三种面向对象数据库（OODBs）的动态聚类技术。前两个，动态，统计和可调聚类（DSTC）和StatClust，利用全面的使用统计和对象间引用图。它们是相当精细的。然而，它们实现起来也很复杂，并且引起很高的开销。第三种聚类技术称为对象检测和重新聚类（DRO），基于相同的原理，但实现起来要简单得多。这三种聚类算法已经在Texas持久对象存储中实现，并在聚类效率方面进行了比较（即，总体性能提高）和开销。获得的结果表明，DRO引起了更轻的开销，同时仍然实现了更好的整体性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
