Tabular data within enterprises or open data repositories provide a huge opportunity for feature augmentation. Using these data sources to augment training data often boosts model performance, which is crucial in data-centric AutoML systems. Recent works on automatic feature augmentation have limited capabilities in utilizing useful features that cannot be joined with the base table without connecting through intermediate tables. We present Featpilot, a novel framework that explores and integrates high-quality features in tabular data for ML models. Featpilot evaluates a candidate feature from two aspects: (1) the efficacy of a join path connecting the feature to the base table and (2) the intrinsic value of a feature towards an ML task. Featpilot efficiently identifies high-quality features and their optimized join paths to augment the base table. Our experimental results show that Featpilot achieves up to a 10.27% improvement in ML model performance compared to state-of-the-art solutions across six public datasets.

We propose OmniMatch , a novel joinability discovery technique, specifically tailored for the needs of data products : cohesive curated collections of tabular datasets. OmniMatch combines multiple column-pair similarity measures leveraging self-supervised Graph Neural Networks (GNNs). OmniMatch 's GNN captures column relatedness by leveraging graph neighborhood information, significantly improving the recall of joinability discovery tasks. At the same time, OmniMatch increases its precision by augmenting its training data with negative column join examples through an automated negative example generation process. Compared to the state-of-the-art, OmniMatch exhibits up to 14% higher effectiveness in F1 score and AUC without relying on individual, user-provided thresholds for each similarity metric.

This paper introduces OrthoReg, a simple yet effective Graph-regularized MLP model for semi-supervised node representation learning. We first demonstrate, through empirical observations and theoretical analysis, that node embeddings learned from conventional GR-MLPs suffer from the over-correlation issue. This issue arises when a few dominant singular values overwhelm the embedding space, leading to the limited expressive power of the learned node representations. To mitigate this problem, we propose a novel GR-MLP model called OrthoReg. By incorporating a soft regularization loss on the correlation matrix of node embeddings, OrthoReg explicitly encourages orthogonal node representations, effectively avoiding over-correlated representations. Compared to the currently popular GNN models, our OrthoReg possesses two distinct advantages: 1) Much faster inference speed, particularly for large-scale graphs. 2) Significantly superior performance in inductive cold-start settings. Experiments on semi-supervised node classification tasks, together with the extensive ablation studies, have demonstrated the effectiveness of the proposed designs.

How can we effectively generate missing data transformations among tables in a data repository? Multiple versions of the same tables are generated from the iterative process when data scientists and machine learning engineers fine-tune their ML pipelines, making incremental improvements. This process often involves data transformation and augmentation that produces an augmented table based on its base version and related tables. However, data transformations are often not well-documented or completely missing, resulting in poor traceability, reproducibility and explainability of ML pipelines. In this paper, we propose DATALoRE, a framework that explains data changes between an initial dataset and its augmented version to improves traceability. Given a base table, DATALoRE first discovers its potentially related tables from the data repository using a variety of data discovery techniques. DATALoRE then effectively leverages a large language model (LLM) to generate a variety of data transformations that lead to the augmented table. DATALoRE validates these transformations and selects the minimum number of related tables to ensure traceability and reproducibility of the ML pipelines. A preliminary experiment shows that DATALoRE is able to effectively recovery data transformations on two benchmark datasets.

Graph neural networks (GNNs) have accomplished great success in learning complex systems of relations arising in broad problem settings ranging from e-commerce, social networks to data management. Training GNNs over large-scale graphs poses challenges for constrained compute resources due to the heavy data dependencies between the nodes. Moreover, modern relational data is constantly evolving, which creates an additional layer of learning challenges with respect to the model scalability and expressivity. This paper introduces a simple and efficient learning algorithm for large discrete-time dynamic graphs (DTDGs) – a widely adopted data model for many applications. We particularly tackle two critical challenges: (1) how the model can be efficiently trained on large-scale DTDGs to exploit hardware accelerators with small memory footprint, and (2) how the model can effectively capture the changing dynamics of the graphs. To the best of our knowledge, existing GNNs fail to address both challenges in their models. Hence, we propose a scalable evolving inception GNN, called SEIGN. Specifically, SEIGN features two connected evolving components that adapt the graph model to the arriving snapshot and capture the changing dynamics of the node embeddings, respectively. To scale up the model training, SEIGN introduces a parameter-free message passing step for DTDGs to substantially remove the data dependencies in training. Furthermore, it significantly reduces the training memory footprint and allows us to construct a succinct graph mini-batch without performing neighborhood sampling. We further optimize the proposed evolving strategies by extracting features from neighbors at varying scales to increase the expressive power of the node representations. Our experimental evaluation, on both public benchmark and real industrial datasets, demonstrates that SEIGN achieves 2%–20% improvement in Area Under Curve (AUC) and Average Precision (AP) on the prediction task over the state-of-the-art baselines. SEIGN also supports efficient graph mini-batch training and gains 2–16 times speedup in epoch computation time over the entire DTDGs.

Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions, are expected to provide high-quality information to facilitate clinical decision making. Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs. However, existing medical entity disambiguation methods are not adequate due to word discrepancies between the entities in the KB and the text snippets in the source documents. Recently, graph neural networks (GNNs) have proven to be very effective and provide state-of-the-art results for many real-world applications with graph-structured data. In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We develop two optimization techniques to fine-tune and improve ED-GNN. First, we introduce a novel strategy to represent entities that are mentioned in text snippets as a query graph. Second, we design an effective negative sampling strategy that identifies hard negative samples to improve the model's disambiguation capability. Compared to the best performing state-of-the-art solutions, our ED-GNN offers an average improvement of 7.3% in terms of F1 score on five real-world datasets.

Clinical narratives that describe complex medical events are often accompanied by meta-information such as a patient’s demographics, diagnoses and medications. This structured information implicitly relates to the logical and semantic structure of the entire narrative, and thus affects vocabulary choices for the narrative composition. To leverage this meta-information, we propose a supervised topic compositional neural language model, called MeTRNN, that integrates the strength of supervised topic modeling in capturing global semantics with the capacity of contextual recurrent neural networks (RNN) in modeling local word dependencies. MeTRNN generates interpretable topics from global meta-information and uses them to facilitate contextual RNNs in modeling local dependencies of text. For efficient training of MeTRNN, we develop an autoencoding variational Bayes inference method. We evaluate MeTRNN on the word prediction tasks using public text datasets. MeTRNN consistently outperforms all baselines across all datasets in perplexity ranging from 5% to 40%. Our case studies on real world electronic health records (EHR) data show that MeTRNN can learn and benefit from meaningful topics.

© 2019 Copyright held by the owner/author(s). Local outlier techniques are known to be effective for detecting outliers in skewed data, where subsets of the data exhibit diverse distribution properties. However, existing methods are not well equipped to support modern high-velocity data streams due to the high complexity of the detection algorithms and their volatility to data updates. To tackle these shortcomings, we propose local outlier semantics that operate at an abstraction level by leveraging kernel density estimation (KDE) to effectively detect local outliers from streaming data. A strategy to continuously detect top-N KDE-based local outliers over streams is designed, called KELOS – the first linear time complexity streaming local outlier detection approach. The first innovation of KELOS is the abstract kernel center-based KDE (aKDE) strategy. aKDE accurately yet efficiently estimates the data density at each point – essential for local outlier detection. This is based on the observation that a cluster of points close to each other tend to have a similar influence on a target point’s density estimation when used as kernel centers. These points thus can be represented by one abstract kernel center. Next, the KELOS’s inlier pruning strategy early prunes points that have no chance to become top-N outliers. This empowers KELOS to skip the computation of their data density and of the outlier status for every data point. Together aKDE and the inlier pruning strategy eliminate the performance bottleneck of streaming local outlier detection. The experimental evaluation demonstrates that KELOS is up to 6 orders of magnitude faster than existing solutions, while being highly effective in detecting local outliers from streaming data.

Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our state- of-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics - empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.

Adverse drug reactions (ADRs) caused by drug-drug interactions (DDI) are a major cause of morbidity and mortality worldwide. There is a growing need for computing-supported methods that facilitate the automated signaling of DDI related ADRs (DIARs) that otherwise would remain undiscovered in millions of ADR reports. In this demonstration, we showcase our MeDIAR technology - an end-to-end DIAR signal generation, exploration and validation solution for pharmaceutical regulatory agencies to detect true DIAR signals from a drug surveillance database. MeDIAR's innovations include an efficient rule-driven learning algorithm for deriving DIAR signals from ADR reports, an innovative scoring methodology based on the proposed contextual association cluster model to rank the generated signals by their importance. Further, these ranked signals are augmented with meta information such as their significance level and their severity, along with links to their supporting ADR reports. Lastly, MeDIAR features an interactive visual analytics interface to support drug safety evaluators in reviewing and discovering unknown severe DIARs.

There is a growing need for computing-supported methods that facilitate the automated signaling of Adverse Drug Reactions (ADRs) otherwise left undiscovered from the exploding amount of ADR reports filed by patients, medical professionals and drug manufacturers. In this research, we design a Multi-Drug Adverse Reaction Analytics Strategy, called MARAS, to signal severe unknown ADRs triggered by the usage of a combination of drugs, also known as Multi-Drug Adverse Reactions (MDAR). First, MARAS features an efficient signal generation algorithm based on association rule learning that extracts non-spurious MDAR associations. Second, MARAS incorporates contextual information to detect drug combinations that are strongly associated with a set of ADRs. It groups related associations into Contextual Association Clusters (CACs) that then avail contextual information to evaluate the significance of the discovered MDAR Associations. Lastly, we use this contextual significance to rank discoveries by their notion of interestingness to signal the most compelling MDARs. To demonstrate the utility of MARAS, it is compared with state-of-the-art techniques and evaluated via case studies on datasets collected by U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).