Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral graphs (MAGs). To date, existing differentiable MAG learning algorithms have been limited to small datasets and failed to scale to larger ones (e.g., with more than 50 variables).

Supervised Causal Learning (SCL) aims to learn causal relations from observational data by accessing previously seen datasets associated with ground truth causal relations. This paper presents a first attempt at addressing a fundamental question: What are the benefits from supervision and how does it benefit? Starting from seeing that SCL is not better than random guessing if the learning target is non-identifiable a priori, we propose a two-phase paradigm for SCL by explicitly considering structure identifiability. Following this paradigm, we tackle the problem of SCL on discrete data and propose ML4C. The core of ML4C is a binary classifier with a novel learning target: it classifies whether an Unshielded Triple (UT) is a v-structure or not. Specifically, starting from an input dataset with the corresponding skeleton provided, ML4C orients each UT once it is classified as a v-structure. These v-structures are together used to construct the final output. To address the fundamental question of SCL, we propose a principled method for ML4C featurization: we exploit the vicinity of a given UT (i.e., the neighbors of UT in the skeleton), and derive features by considering the conditional dependencies and structural entanglement within the vicinity. We further prove that ML4C is asymptotically correct. Thorough experiments conducted on benchmark datasets demonstrate that ML4C remarkably outperforms other state-of-the-art algorithms in terms of accuracy, reliability, robustness and tolerance. In summary, ML4C shows promising results on validating the effectiveness of supervision for causal learning. Our codes are publicly available at https://github.com/microsoft/ML4C.

In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the underlying causes of the knowledge acquired by EDA is crucial. However, it remains under-researched. This study promotes a transparent and explicable perspective on data analysis, called eXplainable Data Analysis (XDA). For this reason, we present XInsight, a general framework for XDA. XInsight provides data analysis with qualitative and quantitative explanations of causal and non-causal semantics. This way, it will significantly improve human understanding and confidence in the outcomes of data analysis, facilitating accurate data interpretation and decision making in the real world. XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact. XInsight uses a set of design concepts and optimizations to address the inherent difficulties associated with integrating causality into XDA. Experiments on synthetic and real-world datasets as well as a user study demonstrate the highly promising capabilities of XInsight.

Causal skeleton learning aims to identify the undirected graph of the underlying causal Bayesian network (BN) from observational data. It plays a pivotal role in causal discovery and many other downstream applications. The methods for causal skeleton learning fall into three primary categories: constraint-based, score-based, and gradient-based methods. This paper, for the first time, advocates for learning a causal skeleton in a supervision-based setting, where the algorithm learns from additional datasets associated with the ground-truth BNs (complementary to input observational data). Concretizing a supervision-based method is non-trivial due to the high complexity of the problem itself, and the potential "domain shift" between training data (i.e., additional datasets associated with ground-truth BNs) and test data (i.e., observational data) in the supervision-based setting. First, it is well-known that skeleton learning suffers worst-case exponential complexity. Second, conventional supervised learning assumes an independent and identical distribution (i.i.d.) on test data, which is not easily attainable due to the divergent underlying causal mechanisms between training and test data. Our proposed framework, ML4S, adopts order-based cascade classifiers and pruning strategies that can withstand high computational overhead without sacrificing accuracy. To address the "domain shift" challenge, we generate training data from vicinal graphs w.r.t. the target BN. The associated datasets of vicinal graphs share similar joint distributions with the observational data. We evaluate ML4S on a variety of datasets and observe that it remarkably outperforms the state of the arts, demonstrating the great potential of the supervision-based skeleton learning paradigm.

Including pairwise or higher-order interactions among predictors of a Generalized Additive Model (GAM) is gaining increasing attention in the literature. However, existing models face anidentifiability challenge. In this paper, we propose pureGAM, an inherently pure additive model of both main effects and higher-order interactions. By imposing thepureness condition to constrain each component function, pureGAM is proved to be identifiable without compromising accuracy. Furthermore, the pureness condition introduces additional interpretability in terms of simplicity. Practically, pureGAM is a unified model to support both numerical and categorical features with a novel learning procedure to achieve optimal performance. Evaluations show that pureGAM outperforms other GAMs and has very competitive performance even compared with opaque models, and its interpretability remarkably outperforms competitors in terms of pureness. We also share a successful adoption of pureGAM in one real-world application.

Automatic Exploratory Data Analysis (EDA) focuses on automatically discovering pieces of knowledge in the form of interesting data patterns. However, the conveyed knowledge by these suggested data patterns are disjointed or lack organization. Therefore, it is difficult for users to gain structured knowledge, and as the number of suggested patterns grows, these stand-alone patterns are less likely to motive users to conduct follow-up analysis, which hinders it from being effectively utilized to facilitate EDA. In this paper, we propose MetaInsight, a structured representation of knowledge extracted from multi-dimensional data aiming to facilitate EDA automatically and effectively. Specifically, we propose a novel formulation of basic data pattern to capture essential characteristics of raw data distribution to achieve knowledge extraction. Then based on the mined Homogeneous Data Patterns (HDP) and inter-pattern similarity, MetaInsight is identified by categorizing basic data patterns (within an HDP) into commonness(es) and exceptions thus achieving structured knowledge representation. The commonness(es) and exceptions concretize the knowledge obtained by induction and validation processes which are two typical analysis mechanisms conducted in EDA. We propose a novel scoring function to quantify the usefulness of MetaInsight, an effective and efficient mining procedure and a ranking algorithm to automatically discover high-quality MetaInsights from multi-dimensional data. We demonstrate the effectiveness and efficiency of MetaInsights (w.r.t. facilitating EDA) through evaluation on real-world datasets and user studies on both expert users and non-expert users.

Discovering interesting data patterns is a common and important analytical need in data, with increasing user demand for automated discovery abilities. However, automatically discovering interesting patterns from multi-dimensional data remains challenging. Existing techniques focus on mining individual types of patterns. There is a lack of unified formulation for different pattern types, as well as general mining frameworks to derive them effectively and efficiently. We present a novel technique QuickInsights, which quickly and automatically discovers interesting patterns from multi-dimensional data. QuickInsights proposes a unified formulation of interesting patterns, called insights, and designs a systematic mining framework to discover high-quality insights efficiently. We demonstrate the effectiveness and efficiency of QuickInsights through our evaluation on 447 real datasets as well as user studies on both expert users and non-expert users. QuickInsights is released in Microsoft Power BI.

OLAP tools have been extensively used by enterprises to make better and faster decisions. Nevertheless, they require users to specify group-by attributes and know precisely what they are looking for. This paper takes the first attempt towards automatically extracting top-k insights from multi-dimensional data. This is useful not only for non-expert users, but also reduces the manual effort of data analysts. In particular, we propose the concept of insight which captures interesting observation derived from aggregation results in multiple steps (e.g., rank by a dimension, compute the percentage of measure by a dimension). An example insight is: ``Brand B's rank (across brands) falls along the year, in terms of the increase in sales''. Our problem is to compute the top-k insights by a score function. It poses challenges on (i) the effectiveness of the result and (ii) the efficiency of computation. We propose a meaningful scoring function for insights to address (i). Then, we contribute a computation framework for top-k insights, together with a suite of optimization techniques (i.e., pruning, ordering, specialized cube, and computation sharing) to address (ii). Our experimental study on both real data and synthetic data verifies the effectiveness and efficiency of our proposed solution.

Fast and scalable analysis techniques are becoming increasingly important in the era of big data, because they are the enabling techniques to create real-time and interactive experiences in data analysis. Time series are widely available in diverse application areas. Due to the large number of time series instances (e.g., millions) and the high dimensionality of each time series instance (e.g., thousands), it is challenging to conduct clustering on large-scale time series, and it is even more challenging to do so in real-time to support interactive exploration. In this paper, we propose a novel end-to-end time series clustering algorithm, YADING, which automatically clusters large-scale time series with fast performance and quality results. Specifically, YADING consists of three steps: sampling the input dataset, conducting clustering on the sampled dataset, and assigning the rest of the input data to the clusters generated on the sampled dataset. In particular, we provide theoretical proof on the lower and upper bounds of the sample size, which not only guarantees YADING's high performance, but also ensures the distribution consistency between the input dataset and the sampled dataset. We also select L 1 norm as similarity measure and the multi-density approach as the clustering method. With theoretical bound, this selection ensures YADING's robustness to time series variations due to phase perturbation and random noise. Evaluation results have demonstrated that on typical-scale (100,000 time series each with 1,000 dimensions) datasets, YADING is about 40 times faster than the state-of-the-art, sampling-based clustering algorithm DENCLUE 2.0, and about 1,000 times faster than DBSCAN and CLARANS. YADING has also been used by product teams at Microsoft to analyze service performance. Two of such use cases are shared in this paper.

For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.

As online services have more and more popular, incident diagnosis has emerged as a critical task in minimizing the service downtime and ensuring high quality of the services provided. For most online services, incident diagnosis is mainly conducted by analyzing a large amount of telemetry data collected from the services at runtime. Time series data and event sequence data are two major types of telemetry data. Techniques of correlation analysis are important tools that are widely used by engineers for data-driven incident diagnosis. Despite their importance, there has been little previous work addressing the correlation between two types of heterogeneous data for incident diagnosis: continuous time series data and temporal event data. In this paper, we propose an approach to evaluate the correlation between time series data and event data. Our approach is capable of discovering three important aspects of event-timeseries correlation in the context of incident diagnosis: existence of correlation, temporal order, and monotonic effect. Our experimental results on simulation data sets and two real data sets demonstrate the effectiveness of the algorithm.

Understanding the behaviors of a software system is very important for performing daily system maintenance tasks. In practice, one way to gain knowledge about the runtime behavior of a system is to manually analyze system logs collected during the system executions. With the increasing scale and complexity of software systems, it has become challenging for system operators to manually analyze system logs. To address these challenges, in this paper, we propose a new approach for contextual analysis of system logs for understanding a system's behaviors. In particular, we first use execution patterns to represent execution structures reflected by a sequence of system logs, and propose an algorithm to mine execution patterns from the program logs. The mined execution patterns correspond to different execution paths of the system. Based on these execution patterns, our approach further learns essential contextual factors (e.g., the occurrences of specific program logs with specific parameter values) that cause a specific branch or path to be executed by the system. The mining and learning results can help system operators to understand a software system's runtime execution logic and behaviors during various tasks such as system problem diagnosis. We demonstrate the feasibility of our approach upon two real-world software systems (Hadoop and Ethereal).