**Applications**

Typical applications of fractional cascading involve range search data structures in computational geometry. For example, consider the problem of *half-plane range reporting*: that is, intersecting a fixed set of *n* points with a query half-plane and listing all the points in the intersection. The problem is to structure the points in such a way that a query of this type may be answered efficiently in terms of the intersection size *h*. One structure that can be used for this purpose is the *convex layers* of the input point set, a family of nested convex polygons consisting of the convex hull of the point set and the recursively-constructed convex layers of the remaining points. Within a single layer, the points inside the query half-plane may be found by performing a binary search for the half-plane boundary line's slope among the sorted sequence of convex polygon edge slopes, leading to the polygon vertex that is inside the query half-plane and farthest from its boundary, and then sequentially searching along the polygon edges to find all other vertices inside the query half-plane. The whole half-plane range reporting problem may be solved by repeating this search procedure starting from the outermost layer and continuing inwards until reaching a layer that is disjoint from the query halfspace. Fractional cascading speeds up the successive binary searches among the sequences of polygon edge slopes in each layer, leading to a data structure for this problem with space O(*n*) and query time O(log *n* + *h*). The data structure may be constructed in time O(*n* log *n*) by an algorithm of Chazelle (1985). As in our example, this application involves binary searches in a linear sequence of lists (the nested sequence of the convex layers), so the catalog graph is just a path.

Another application of fractional cascading in geometric data structures concerns point location in a monotone subdivision, that is, a partition of the plane into polygons such that any vertical line intersects any polygon in at most two points. As Edelsbrunner, Guibas & Stolfi (1986) showed, this problem can be solved by finding a sequence of polygonal paths that stretch from left to right across the subdivision, and binary searching for the lowest of these paths that is above the query point. Testing whether the query point is above or below one of the paths can itself be solved as a binary search problem, searching for the x coordinate of the points among the x coordinates of the path vertices to determine which path edge might be above or below the query point. Thus, each point location query can be solved as an outer layer of binary search among the paths, each step of which itself performs a binary search among x coordinates of vertices. Fractional cascading can be used to speed up the time for the inner binary searches, reducing the total time per query to O(log *n*) using a data structure with space O(*n*). In this application the catalog graph is a tree representing the possible search sequences of the outer binary search.

Beyond computational geometry, Lakshman & Stiliadis (1998) and Buddhikot, Suri & Waldvogel (1999) apply fractional cascading in the design of data structures for fast packet filtering in internet routers. Gao et al. (2004) use fractional cascading as a model for data distribution and retrieval in sensor networks.

Read more about this topic: Fractional Cascading

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