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Extra info for An image registration technique for recovering rotation, scale and translation parameters
1 Loading into Heaps One approach to achieving fast loads and queries is to load data directly into physically separate unsorted heaps, then create small summaries of those heaps that can be used to guide, or even answer, queries. Fast queries are then achieved by avoiding scans of heaps that don’t contain data relevant to a given query. These solutions diﬀer in terms of the nature of the summary information. Small materialized aggregates. Moerkotte [M 98] seems to have been the ﬁrst to suggest parsimonious use of materialized views for query answering and for eﬃciency in query execution plans.
In other words, the lowest value in the zone ﬁlter is Null if and only if the Null value occurs in the zone. In such cases, or if the search key is smaller than the largest among the m lowest values or larger than the smallest among the n highest values, the set 46 G. Graefe and H. Kuno of extreme values in the zone ﬁlter supports not only range (“<”, “between”) predicates but also equality (“=”, “in”) predicates, even for query constants that are within the range between low and high values. For example, if the m = 3 lowest values retained in a zone ﬁlter are (4, 7, 12), a search for value 9 can safely skip the zone.
Those are separated by an artiﬁcial leading key ﬁeld containing partition identiﬁers. Incremental index reorganization may exploit idle times between loading and query processing or may be a preparatory side eﬀect of query processing. The required logic is the same as merging in a traditional external merge sort. Fig. 9. Partitioned B-tree Figure 9 illustrates a partitioned B-tree with 3 partitions, labeled 0, 3, and 4. B-tree reorganization into a single partition uses the merge logic of an external merge sort.
An image registration technique for recovering rotation, scale and translation parameters by McGuire