Machine learning (ML) over relational data is a booming area of the database industry and academia. While several projects aim to build scalable and fast ML systems, little work has addressed the pains of sourcing data and features for ML tasks. Real-world relational databases typically have many tables (often, dozens) and data scientists often struggle to even obtain and join all possible tables that provide features for ML. In this context, showed recently that key-foreign key dependencies (KFKDs) between tables often lets us avoid such joins without significantly affecting prediction accuracy--an idea they called avoiding joins safely. While initially controversial, this idea has since been used by multiple companies to reduce the burden of data sourcing for ML. But their work applied only to linear classifiers. In this work, we verify if their results hold for three popular complex classifiers: decision trees, SVMs, and ANNs. We conduct an extensive experimental study using both real-world datasets and simulations to analyze the effects of avoiding KFK joins on such models. Our results show that these high-capacity classifiers are surprisingly and counter-intuitively more robust to avoiding KFK joins compared to linear classifiers, refuting an intuition from the prior work's analysis. We explain this behavior intuitively and identify open questions at the intersection of data management and ML theoretical research. All of our code and datasets are available for download from this http URL.
* Data Integration Integrating data and features from different sources for ML and data mining algorithms often requires applying and adapting techniques from the data integration literature @cite_41 @cite_15 . These include integrating features from different data types in recommendation systems @cite_40 , sensor fusion @cite_2 , dimensionality reduction during feature fusion @cite_29 , and techniques to control data quality during data fusion @cite_43 . Avoiding joins safely can be seen as one schema-based mechanism to reduce the integration burden by predicting a priori if a source table is unlikely to improve ML accuracy. It is a major open challenge to devise similar mechanisms can be devised for other types of data sources, say, using other forms of schema constraints, ontology information, and sampling. There is also a growing interest in making data discovery and other forms of metadata management easier @cite_22 @cite_9 . Our work can be seen as a mechanism to verify the potential utility of some of the discovered data sources using their metadata. We hope our work spurs more research in this direction of exploiting ideas from data integration and data discovery to reduce the data sourcing burden for ML tasks.
@cite_22 @cite_41 @cite_29 @cite_9 @cite_43 @cite_40 @cite_2 @cite_15
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Closer integration of machine learning (ML) with data processing is a booming area in both the data management industry and academia. Almost all ML toolkits assume that the input is a single table, but many datasets are not stored as single tables due to normalization. Thus, analysts often perform key-foreign key joins to obtain features from all base tables and apply a feature selection method, either explicitly or implicitly, with the aim of improving accuracy. In this work, we show that the features brought in by such joins can often be ignored without affecting ML accuracy significantly, i.e., we can "avoid joins safely." We identify the core technical issue that could cause accuracy to decrease in some cases and analyze this issue theoretically. Using simulations, we validate our analysis and measure the effects of various properties of normalized data on accuracy. We apply our analysis to design easy-to-understand decision rules to predict when it is safe to avoid joins in order to help analysts exploit this runtime-accuracy trade-off. Experiments with multiple real normalized datasets show that our rules are able to accurately predict when joins can be avoided safely, and in some cases, this led to significant reductions in the runtime of some popular feature selection methods. In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem. We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem. Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers dependence between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracy of data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources. The integration of information acquired with different modalities, spatial resolution and spectral bands has shown to improve predictive accuracies. Data fusion is therefore one of the key challenges in remote sensing. Most prior work focusing on multi-modal fusion, assumes that modalities are always available during inference. This assumption limits the applications of multi-modal models since in practice the data collection process is likely to generate data with missing, incomplete or corrupted modalities. In this paper, we show that Generative Adversarial Networks can be effectively used to overcome the problems that arise when modalities are missing or incomplete. Focusing on semantic segmentation of building footprints with missing modalities, our approach achieves an improvement of about 2 on the Intersection over Union (IoU) against the same network that relies only on the available modality.