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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
The OverRelational Manifesto
Evgeniy Grigoriev · 2006-02-14 · via cs.DS updates on arXiv.org

The OverRelational Manifesto (below ORM) proposes a possible approach to creation of data storage systems of the next generation. ORM starts from the requirement that information in a relational database is represented by a set of relation values. Accordingly, it is assumed that the information about any entity of an enterprise must also be represented as a set of relation values (the ORM main requirement). A system of types is introduced, which allows one to fulfill the main requirement. The data are represented in the form of complex objects, and the state of any object is described as a set of relation values. Emphasize that the types describing the objects are encapsulated, inherited, and polymorphic. Then, it is shown that the data represented as a set of such objects may also be represented as a set of relational values defined on the set of scalar domains (dual data representation). In the general case, any class is associated with a set of relation variables (R-variables) each one containing some data about all objects of this class existing in the system. One of the key points is the fact that the usage of complex (from the user's viewpoint) refined names of R-variables and their attributes makes it possible to preserve the semantics of complex data structures represented in the form of a set of relation values. The most important part of the data storage system created on the approach proposed is an object-oriented translator operating over a relational DBMS. The expressiveness of such a system is comparable with that of OO programming languages.