Many applications dealing with large data structures can benefit from keeping them in compressed form. Compression has many benefits: it can allow a representation to fit in main memory rather than swapping out to disk, and it improves cache performance since it allows more data to fit

format. The document object model (DOM) defines convenient means to access XML data, but many implementations struggle with performance limitations. The data model implied by the standard needs new data structures and adapted query algorithms to enable native XML databases to perform acceptably

The recent theoretical advances on compact data structures (also called “sketches”) have raised the question of whether they can effectively be applied to content-based image retrieval systems. The main challenge is to derive an algorithm that achieves high-quality similarity searches while using

data structure) and that string matching can be implemented using 20-30 % of the space required by conventional data representations. Compact representations are useful, because they allow the performance-critical part of packet processing algorithms to be implemented using fast, on-chip memory

We show that every comparability graph of any two-dimensional poset over n elements (a.k.a. permutation graph) can be preprocessed in O(n) time, if two linear extensions of the poset are given, to produce an O(n) space data-structure supporting distance queries in constant time. The data-structure

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The way memory hierarchy has evolved in recent decades has opened new challenges in the development of indexing structures in general and spatial access methods in particular. In this paper we propose an original approach to represent geographic data based on compact data structures used in other

Starting from a permutation of f0; : : : ; n \Gamma 1g we compute in parallel with a workload of O(n log n) a compact data structure of size O(n log n). This data structure allows to obtain the associated permutation graph and the transitive closure and reduction of the associated order

is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus

The control state reachability problem is decidable for well-structured infinite-state systems like (Lossy) Petri Nets, Vector Addition Systems, and Broadcast Protocols. An abstract algorithm that solves the problem is the backward reachability algorithm of [1,21]. The algorithm computes