12 research outputs found
HoloDetect: Few-Shot Learning for Error Detection
We introduce a few-shot learning framework for error detection. We show that
data augmentation (a form of weak supervision) is key to training high-quality,
ML-based error detection models that require minimal human involvement. Our
framework consists of two parts: (1) an expressive model to learn rich
representations that capture the inherent syntactic and semantic heterogeneity
of errors; and (2) a data augmentation model that, given a small seed of clean
records, uses dataset-specific transformations to automatically generate
additional training data. Our key insight is to learn data augmentation
policies from the noisy input dataset in a weakly supervised manner. We show
that our framework detects errors with an average precision of ~94% and an
average recall of ~93% across a diverse array of datasets that exhibit
different types and amounts of errors. We compare our approach to a
comprehensive collection of error detection methods, ranging from traditional
rule-based methods to ensemble-based and active learning approaches. We show
that data augmentation yields an average improvement of 20 F1 points while it
requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages
A Formal Framework for Probabilistic Unclean Databases
Most theoretical frameworks that focus on data errors and inconsistencies follow logic-based reasoning. Yet, practical data cleaning tools need to incorporate statistical reasoning to be effective in real-world data cleaning tasks. Motivated by empirical successes, we propose a formal framework for unclean databases, where two types of statistical knowledge are incorporated: The first represents a belief of how intended (clean) data is generated, and the second represents a belief of how noise is introduced in the actual observed database. To capture this noisy channel model, we introduce the concept of a Probabilistic Unclean Database (PUD), a triple that consists of a probabilistic database that we call the intention, a probabilistic data transformator that we call the realization and captures how noise is introduced, and an observed unclean database that we call the observation. We define three computational problems in the PUD framework: cleaning (infer the most probable intended database, given a PUD), probabilistic query answering (compute the probability of an answer tuple over the unclean observed database), and learning (estimate the most likely intention and realization models of a PUD, given examples as training data). We illustrate the PUD framework on concrete representations of the intention and realization, show that they generalize traditional concepts of repairs such as cardinality and value repairs, draw connections to consistent query answering, and prove tractability results. We further show that parameters can be learned in some practical instantiations, and in fact, prove that under certain conditions we can learn a PUD directly from a single dirty database without any need for clean examples
BClean: A Bayesian Data Cleaning System
There is a considerable body of work on data cleaning which employs various
principles to rectify erroneous data and transform a dirty dataset into a
cleaner one. One of prevalent approaches is probabilistic methods, including
Bayesian methods. However, existing probabilistic methods often assume a
simplistic distribution (e.g., Gaussian distribution), which is frequently
underfitted in practice, or they necessitate experts to provide a complex prior
distribution (e.g., via a programming language). This requirement is both
labor-intensive and costly, rendering these methods less suitable for
real-world applications. In this paper, we propose BClean, a Bayesian Cleaning
system that features automatic Bayesian network construction and user
interaction. We recast the data cleaning problem as a Bayesian inference that
fully exploits the relationships between attributes in the observed dataset and
any prior information provided by users. To this end, we present an automatic
Bayesian network construction method that extends a structure learning-based
functional dependency discovery method with similarity functions to capture the
relationships between attributes. Furthermore, our system allows users to
modify the generated Bayesian network in order to specify prior information or
correct inaccuracies identified by the automatic generation process. We also
design an effective scoring model (called the compensative scoring model)
necessary for the Bayesian inference. To enhance the efficiency of data
cleaning, we propose several approximation strategies for the Bayesian
inference, including graph partitioning, domain pruning, and pre-detection. By
evaluating on both real-world and synthetic datasets, we demonstrate that
BClean is capable of achieving an F-measure of up to 0.9 in data cleaning,
outperforming existing Bayesian methods by 2% and other data cleaning methods
by 15%.Comment: Our source code is available at https://github.com/yyssl88/BClea
BigDansing
Data cleansing approaches have usually focused on detecting and fixing errors with little attention to scaling to big datasets. This presents a serious impediment since data cleansing often involves costly computations such as enumerating pairs of tuples, handling inequality joins, and dealing with user-defined functions. In this paper, we present BigDansing, a Big Data Cleansing system to tackle efficiency, scalability, and ease-of-use issues in data cleansing. The system can run on top of most common general purpose data processing platforms, ranging from DBMSs to MapReduce-like frameworks. A user-friendly programming interface allows users to express data quality rules both declaratively and procedurally, with no requirement of being aware of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic and real datasets show that BigDansing outperforms existing baseline systems up to more than two orders of magnitude without sacrificing the quality provided by the repair algorithms
BigDansing
Data cleansing approaches have usually focused on detecting and fixing errors with little attention to scaling to big datasets. This presents a serious impediment since data cleansing often involves costly computations such as enumerating pairs of tuples, handling inequality joins, and dealing with user-defined functions. In this paper, we present BigDansing, a Big Data Cleansing system to tackle efficiency, scalability, and ease-of-use issues in data cleansing. The system can run on top of most common general purpose data processing platforms, ranging from DBMSs to MapReduce-like frameworks. A user-friendly programming interface allows users to express data quality rules both declaratively and procedurally, with no requirement of being aware of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic and real datasets show that BigDansing outperforms existing baseline systems up to more than two orders of magnitude without sacrificing the quality provided by the repair algorithms
Towards dependable data repairing with fixing rules
One of the main challenges that data cleaning systems face is to automatically identify and repair data errors in a depend-able manner. Though data dependencies (a.k.a. integrity constraints) have been widely studied to capture errors in data, automated and dependable data repairing on these errors has remained a notoriously hard problem. In this work, we introduce an automated approach for dependably repairing data errors, based on a novel class of fixing rules. A fixing rule contains an evidence pattern, a set of nega-tive patterns, and a fact value. The heart of fixing rules is deterministic: given a tuple, the evidence pattern and the negative patterns of a fixing rule are combined to precisely capture which attribute is wrong, and the fact indicates how to correct this error. We study several fundamental prob-lems associated with fixing rules, and establish their com-plexity. We develop ecient algorithms to check whether a set of fixing rules is consistent, and discuss approaches to resolve inconsistent fixing rules. We also devise ecient algorithms for repairing data errors using fixing rules. We experimentally demonstrate that our techniques outperform other automated algorithms in terms of the accuracy of re-pairing data errors, using both real-life and synthetic data. 1