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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Automata-based Static Analysis of XML Document Adaptation
2012-10-09 · via cs.DS updates on arXiv.org

The structure of an XML document can be optionally specified by means of XML Schema, thus enabling the exploitation of structural information for efficient document handling. Upon schema evolution, or when exchanging documents among different collections exploiting related but not identical schemas, the need may arise of adapting a document, known to be valid for a given schema S, to a target schema S'. The adaptation may require knowledge of the element semantics and cannot always be automatically derived. In this paper, we present an automata-based method for the static analysis of user-defined XML document adaptations, expressed as sequences of XQuery Update update primitives. The key feature of the method is the use of an automatic inference method for extracting the type, expressed as a Hedge Automaton, of a sequence of document updates. The type is computed starting from the original schema S and from rewriting rules that formally define the operational semantics of a sequence of document updates. Type inclusion can then be used as conformance test w.r.t. the type extracted from the target schema S'.