We propose a computational framework for identifying approximate functional dependencies (AFDs) in a relation, leveraging the frequency distribution information of individual attributes.This framework operates without requiring access to the full database, processing records one at a time as necessary.Our approach generalizes existing measures for quantifying errors in perfect dependencies and formalizes two primary problems: finding top-𝑘 AFDs and identifying all AFDs within a specified error threshold, 𝜖.Our proposed framework provides anytime solutions, meaning it returns results after processing each record.A key innovation of our work lies in effectively estimating error bounds of the candidate AFDs, which allows to produce anytime solutions.We present an exact algorithm that delivers precise solutions when possible.We also develop an algorithm that always returns a solution albeit with some imprecision in the output.We demonstrate the applicability of these algorithms under various data organization strategies, such as indexing by key or key-like attributes.Our experimental results, based on both real-world and synthetic datasets, validate the effectiveness of our approach and show that it outperforms state-of-the-art solutions.

We are being constantly judged by automated decision systems that have been widely criticised for being discriminatory and unfair. Since an algorithm is only as good as the data it works with, biases in the data can significantly amplify unfairness issues. In this paper, we take initial steps towards integrating fairness conditions into database query processing and data management systems. Specifically, we focus on selection bias in range queries. We formally define the problem of fairness-aware range queries as obtaining a fair query which is most similar to the user's query. We propose a sub-linear time algorithm for single-predicate range queries and efficient algorithms for multi-predicate range queries. Our empirical evaluation on real and synthetic datasets confirms the effectiveness and efficiency of our proposal.

Cardinality estimation is a fundamental and challenging problem in query optimization. Recently, a number of learned models have been proposed for this task. Often, these models significantly outperform traditional approaches in terms of accuracy. One of the stumbling blocks that prevents their increased adoption is that the learned models do not quantify the uncertainty of their estimates. It is desirable to associate each cardinality estimate of the model with a prediction interval that will contain the true cardinality with an user-specified probability. The size of the prediction interval encodes the uncertainty allowing the query optimizer to make an informed decision. For example, knowing that the cardinality of a query$q$lies between 1–3% of the relation size with high probability is more informative than a single point estimate of 2%. While there has been some prior work on deriving bounds for traditional methods (such as sampling or histograms), they are not directly applicable for the learned models for cardinality estimation. In this paper, we conduct a systematic investigation of potential approaches for obtaining prediction intervals. We enumerate the list of desirable properties such as the ability to wrap around a learned model without significant internal modification and providing bounds with theoretical guarantees in a distribution agnostic manner among others. Based on an extensive literature survey, we identify four practical and high quality approaches for uncertainty quantification that satisfies these criteria. They span a wide spectrum in terms of theoretical guarantees, width of prediction interval and time taken for computing the prediction intervals. We conduct extensive experimental analysis of the efficacy of these approaches over three diverse and representative cardinality estimation algorithms. Our experiments covers diverse workloads involving both point and range queries and highlights the inherent trade-offs. Our results show that it is possible to obtain accurate prediction intervals in an efficient manner thereby opening up new avenues for future research.

In this paper, we revisit a suite of popular proximity problems (such as, KNN, clustering, minimum spanning tree) that repeatedly perform distance computations to compare distances during their execution. Our effort here is to design principled solutions to minimize distance computations for such problems in general metric spaces, especially for the scenarios where calling an expensive oracle to resolve unknown distances are the dominant cost of the algorithms for these problems. We present a suite of techniques, including a novel formulation of the problem, that studies how distance comparisons between objects could be modelled as a system of linear inequalities that assists in saving distance computations, multiple graph based solutions, as well as a practitioners guide to adopt our solution frameworks to proximity problems. We compare our designed solutions conceptually and empirically with respect to a broad range of existing works. We finally present a comprehensive set of experimental results using multiple large scale real-world datasets and a suite of popular proximity algorithms to demonstrate the effectiveness of our proposed approaches.

Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results.

Given a database with numeric attributes, it is often of interest to rank the tuples according to linear scoring functions. For a scoring function and a subset of tuples, the regret of the subset is defined as the (relative) difference in scores between the top-1 tuple of the subset and the top-1 tuple of the entire database. Finding the regret-ratio minimizing set (RRMS), i.e., the subset of a required size k that minimizes the maximum regret-ratio across all possible ranking functions, has been a well-studied problem in recent years. This problem is known to be NP-complete and there are several approximation algorithms for it. Other NP-complete variants have also been investigated, e.g., finding the set of size k that minimizes the average regret ratio over all linear functions. Prior work have designed customized algorithms for different variants of the problem, and are unlikely to easily generalize to other variants. In this paper we take a different path towards tackling these problems. In contrast to the prior, we propose a unified algorithm for solving different problem variants. Unification is done by localizing the customization to the design of variant-specific subroutines or "oracles" that are called by our algorithm. Our unified algorithm takes inspiration from the seemingly unrelated problem of clustering from data mining, and the corresponding k-medoid algorithm. We make several innovative contributions in designing our algorithm, including various techniques such as linear programming, edge sampling in graphs, volume estimation of multi-dimensional convex polytopes, and several others. We provide rigorous theoretical analysis, as well as substantial experimental evaluations over real and synthetic data sets to demonstrate the practical feasibility of our approach.