压缩位图索引：超越并集和交集
Compressed bitmap indexes: beyond unions and intersections
摘要：
压缩位图索引用于加快数据库中的简单聚合查询。事实上，像交集、并集和补码这样的集合运算可以表示为逻辑运算（AND、OR、NOT），非常适合位图。但是，如何将位图应用于更高级的查询就不那么明显了。例如，我们可能会在商店中寻找符合某些（但可能不是全部）标准的产品。这种阈值查询概括了交集和并集;它们经常用于信息检索和数据挖掘应用程序。我们引入新的算法，有时三个数量级的速度比一个天真的方法。我们的工作表明，位图索引比通常认为的适用范围更广。
英文摘要：
Compressed bitmap indexes are used to speed up simple aggregate queries in databases. Indeed, set operations like intersections, unions and complements can be represented as logical operations (AND,OR,NOT) that are ideally suited for bitmaps. However, it is less obvious how to apply bitmaps to more advanced queries. For example, we might seek products in a store that meet some, but maybe not all, criteria. Such threshold queries generalize intersections and unions; they are often used in information-retrieval and data-mining applications. We introduce new algorithms that are sometimes three orders of magnitude faster than a naive approach. Our work shows that bitmap indexes are more broadly applicable than is commonly believed.
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