...upport)、 简单的应用程序接口(simple API)、最终一致性(或者说支持BASE特性， 不支持ACID)、支持海量数据(Huge amount of data)。
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本文提出了一种有效的支持海量图像数据库Q BE查询的聚类索引算法。
This paper proposes an indexing algorithm of clustering which supports QBE image retrieval for large image databases.
这就对数据库管理系统提出了挑战，即如何有效地存储和管理海量数据并高效的支持上层的查询。
This is a great challenge to DBMS, because it has to store and manage the massive data efficiently and support SQL queries more effectively.
传统支持向量机基于批量训练方法，无法适应环境污染预测中的海量数据与实时性要求。
Traditional Support Vector Machine (SVM), which based on batch training, can't satisfy the real-time requirement of environmental pollution prediction with large scale data.
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