Object Clustering Heuristics and Algorithms in Next Generation Database Management Systems

传统(例如，关系)数据库系统将数据聚集在磁盘上，以便将访问信息的成本降至最低；这种机制过于简单，并且是在系统投入使用之前设计的，并且从未改变过。但新的应用程序对数据库系统提出了更大的要求。飞机工程就是一个例子，多个设计师同时对电子和机械工程部件的复杂集成执行不断变化的操作。这与数据处理形成对比，在数据处理中，通常执行高度可预测的数据操作。这使得数据集群成为一个需要更新和更强大的算法结果的关键领域。自适应集群项目的目标是考虑在考虑集群算法时发挥作用的各种因素，这些算法不是预先构建的，并且可以适应不断变化的、不寻常的数据库访问模式。这将导致自适应集群理论的发展，这将对各种数据库用户，特别是CAD/CAM、软件、印刷电路板和VLSI设计者具有重要意义。这将提供一系列软件算法，将过于昂贵的应用程序转变为具有成本效益的系统。

Traditional (e.g., relational) database systems cluster data on disks in order to minimize the cost of accessing information; such mechanisms are simplistic and are designed before the system is put into use - and are never changed. But new applications are making bigger demands on database systems. An example is aircraft engineering, where multiple designers simultaneously perform ever-changing manipulations on complex integrations of electronic and mechanical engineering components. This is in contrast to data processing, where highly-predictable data manipulations are typically performed. This has caused data clustering to emerge as a critical area demanding newer and more powerful algorithmic results. The goal of the self-adaptive clustering project is to consider the wide variety of factors that come into play when one considers clustering algorithms which are not constructed in advance and can adapt themselves to changing, unusual database access patterns. This will lead to the development of a theory of self-adaptive clustering, which will be of great significance to a wide variety of database users, in particular CAD/CAM, software, printed circuit board, and VLSI designers. This will provide a spectrum of software algorithms which will transform overly-expensive applications into cost-effective systems.