Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can make database more intelligent (AI4DB). For example, traditional empirical database optimization techniques (e.g., cost estimation, join order selection, knob tuning, index and view selection) cannot meet the high-performance requirement for large-scale database instances, various applications and diversified users, especially on the cloud. Fortunately, learning-based techniques can alleviate this problem. On the other hand, database techniques can optimize AI models (DB4AI). For example, AI is hard to deploy in real applications, because it requires developers to write complex codes and train complicated models. Database techniques can be used to reduce the complexity of using AI models, accelerate AI algorithms and provide AI capability inside databases. Thus both DB4AI and AI4DB have been extensively studied recently. In this article, we review existing studies on AI4DB and DB4AI. For AI4DB, we review the techniques on learning-based configuration tuning, optimizer, index/view advisor, and security. For DB4AI, we review AI-oriented declarative language, AI-oriented data governance, training acceleration, and inference acceleration. Finally, we provide research challenges and future directions.

In this work, we present a self-driving data visualization system, called DEEPEYE, that automatically generates and recommends visualizations based on the idea of visualization by examples. We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend DEEPEYE to be easily steerable by allowing the user to use keyword search and providing click-based faceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system.

Given a dataset with incomplete data (e.g., missing values), training a machine learning model over the incomplete data requires two steps. First, it requires a data-effective step that cleans the data in order to improve the data quality (and the model quality on the cleaned data). Second, it requires a data-efficient step that selects a core subset of the data (called coreset) such that the trained models on the entire data and the coreset have similar model quality, in order to improve the training efficiency. The first-data-effective-then-data-efficient methods are too costly, because they are expensive to clean the whole data; while the first-data-efficient-then-data-effective methods have low model quality, because they cannot select high-quality coreset for incomplete data. In this paper, we investigate the problem of coreset selection over incomplete data for data-effective and data-efficient machine learning. The essential challenge is how to model the incomplete data for selecting high-quality coreset. To this end, we propose the GoodCore framework towards selecting a good coreset over incomplete data with low cost. To model the unknown complete data, we utilize the combinations of possible repairs as possible worlds of the incomplete data. Based on possible worlds, GoodCore selects an expected optimal coreset through gradient approximation without training ML models. We formally define the expected optimal coreset selection problem, prove its NP-hardness, and propose a greedy algorithm with an approximation ratio. To make GoodCore more efficient, we further propose optimization methods that incorporate human-in-the-loop imputation or automatic imputation method into our framework. Experimental results show the effectiveness and efficiency of our framework with low cost.

Sufficient good features are indispensable to train well-performed machine learning models. However, it is com-mon that good features are not always enough, where feature augmentation is necessary to enrich high-quality features by joining with other tables. There are two main challenges for the problem. Given a set of tables where we can augment features from, the first challenge is that there are a lot of ways of joining multiple tables and deciding which features (or attributes) to use - selecting the best set of features to augment is hard. Moreover, we may need to materialize the join results for different join options, doing full materialization might be time consuming - efficient but approximate methods are needed. In this paper, we first introduce the design space of the feature augmentation problem. Then, to address the above challenges, we propose a reinforcement learning based framework, namely AutoFeature, to augment the features following an exploration-exploitation strategy. AutoFeature keeps exploring the features in tables that have led to performance improvement. At the same time, AutoFeature also exploits the tables (features) that are rarely selected. In this way, the search space of tables (features) to be augmented can be well explored and a subset of good features can be selected. AutoFeature utilizes sampling techniques to achieve high efficiency. We implement two algorithms, one with multi-arm bandit and the other with branch Deep Q Networks (branch DQN), to realize the framework of AutoFeature. We conducted experiments on three real-world datasets School/XuetangE/Air using 16/23/34 candidate tables with 695/204/338 candidate features. Extensive results show that AutoFeature outperforms other methods by 12.4% and 9.8% on AUC values on two classification datasets (School and XuetangE) and by 0.113 on the MSE value on Air in terms of the model performance.

Data preparation is crucial in achieving optimized results for machine learning (ML). However, having a good data preparation pipeline is highly non-trivial for ML practitioners, which is not only domain-specific, but also dataset-specific. There are two common practices. Human-generated pipelines (HI-pipelines) typically use a wide range of any operations or libraries but are highly experience- and heuristic-based. In contrast, machine-generated pipelines (AI-pipelines), a.k.a. AutoML, often adopt a predefined set of sophisticated operations and are search-based and optimized. These two common practices are mutually complementary. In this paper, we study a new problem that, given an HI-pipeline and an AI-pipeline for the same ML task, can we combine them to get a new pipeline (HAI-pipeline) that is better than the provided HI-pipeline and AI-pipeline? We propose HAIPipe, a framework to address the problem, which adopts an enumeration-sampling strategy to carefully select the best performing combined pipeline. We also introduce a reinforcement learning (RL) based approach to search an optimized AI-pipeline. Extensive experiments using 1400+ real-world HI-pipelines (Jupyter notebooks from Kaggle) verify that HAIPipe can significantly outperform the approaches using either HI-pipelines or AI-pipelines alone.

Finding line-chart images similar to a given line-chart image query is a common task in data exploration and image query systems, e.g. finding similar trends in stock markets or medical Electroencephalography images. The state-of-the-art approaches consider either data-level similarity (when the underlying data is present) or image-level similarity (when the underlying data is absent). In this paper, we study the scenario that during query time, only line-chart images are available. Our goal is to train a neural network that can turn these line-chart images into representations that are aware of the data used to generate these line charts, so as to learn better representations. Our key idea is that we can collect both data and line-chart images to learn such a neural network (at training step), while during query (or inference) time, we support the case that only line-chart images are provided. To this end, we present LineNet, a Vision Transformer-based Triplet Autoencoder model to learn data-aware image representations of line charts for similarity search. We design a novel pseudo labels selection mechanism to guide LineNet to capture both data-aware and image-level similarity of line charts. We further propose a diversified training samples selection strategy to optimize the learning process and improve the performance. We conduct both quantitative evaluation and case studies, showing that LineNet significantly outperforms the state-of-the-art methods for searching similar line-chart images.