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Extensible Markup Language (XML) is a markup language and for storing, transmitting, and reconstructing arbitrary data. It defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. The World Wide Web Consortium's XML 1.0 Specification of 1998 and several other related specifications—all of them free open standards—define XML. The design goals of XML emphasize simplicity, generality, and usability across the Internet. It is a textual data format with strong support via Unicode for different human languages. Although the design of XML focuses on documents, the language is widely used for the representation of arbitrary data structures such as those used in web services. Several schema systems exist to aid in the definition of XML-based languages, while programmers have developed many application programming interfaces (APIs) to aid the processing of XML data. Overview The main purpose of XML is serialization, i.e. storing, tr

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Grammar-based compression means to find a small grammar that generates a given object. Such a grammar reveals the structure of the object (according to the grammar formalism used); the main advantage of this compression method is that the resulting grammar can often be used in further computations without prior decompression. A linear time bottom-up algorithm is presented which transforms a tree into a particular context-free tree grammar. For common XML documents the algorithm performs well, compressing the tree structure to about 5 per cent of the original size. The validation of an XML document against an XML type can be done without decompression, in linear time w.r.t. the size of the grammar (for a fixed type). While the involved grammars can be double exponentially smaller than the represented trees, testing them for equivalence can be done in polynomial space w.r.t. the sum of their sizes.

2004

Scaling Similarity Joins over Tree-Structured Data

Yu Tang

Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-of-the-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.

Assoc Computing Machinery2015

Realistic modeling of spectator behavior for soccer videogames with CUDA

Eray Molla

Soccer has always been one of the most popular videogame genres. When designing a soccer game, designers tend to focus on the game field and game play due to the limited computational resources, and thus the modelling of virtual spectators is paid less attention. In this study we present a novel approach to the modeling of spectator behavior, which treats each spectator as a unique individual. We also propose an independent software layer for sport-based games that simply obtains the game status from the game engine via a simple messaging protocol and computes the spectator behavior accordingly. The result is returned to the game engine, to be used in the animation and rendering of the spectators. Additionally, we offer a customizable spectator knowledge base with well structured XML to minimize coding efforts, while generating individualized behavior. The employed AI is based on fuzzy inference. In order to overcome additional demand for computing realistic spectator behavior, we use GPU parallel computing with CUDA. (C) 2011 Elsevier Ltd. All rights reserved.

2011

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