Causal inference can quantify cause-effect relationships in domains as varied as medicine, economics and public policy. Production computer systems exhibit a similar level of complexity and a recurring need to diagnose problems quickly. However, systems are only observed imperfectly, often via long, messy, semi-structured logs. In this work, we want to accelerate large systems debugging by applying causal inference over logs, enabling engineers to diagnose problems and assess interventions in a principled manner. Our framework achieves this through two human-in-the-loop modules: (1) The Candidate Cause Ranker , through which one can determine the causes of a variable without running a full causal discovery algorithm; and (2) the Interactive Causal Graph Refiner , which helps engineers compute an unbiased estimation of their effect of interest without extensive manual causal graph verification. Both modules are powered by the insight that only part of the causal graph of the system is needed to correctly quantify a given effect of interest. We also provide a data preparation pipeline, the Log Converter , which transforms raw, messy, real-world logs into an appropriate tabular input for causal inference, using methods drawn from data transformation, cleaning, and extraction. We evaluate LOGos, a prototype implementation, on both real-world and synthetic logs and find that: (1) The Candidate Cause Ranker achieved an average precision 1.08×-18× higher than the baselines, in interactive time; (2) The Interactive Causal Graph Refiner required a number of causal judgments 1.61 × - 16.83× lower than the baselines; and (3) The latency of Log Converter scaled linearly with three measures of the complexity of a log: length, distinct templates, and fraction of tokens that are variables.

Recommendation systems rely on user-provided data to learn about item quality and provide personalized recommendations. An implicit assumption when aggregating ratings into item quality is that ratings are strong indicators of item quality. In this work, we test this assumption using data collected from a music discovery application. Our study focuses on two factors that cause rating inflation: heterogeneous user rating behavior and the dynamics of personalized recommendations. We show that user rating behavior substantially varies by user, leading to item quality estimates that reflect the users who rated an item more than the item quality itself. Additionally, items that are more likely to be shown via personalized recommendations can experience a substantial increase in their exposure and potential bias toward them. To mitigate these effects, we analyze the results of a randomized controlled trial in which the rating interface was modified. The test resulted in a substantial improvement in user rating behavior and a reduction in item quality inflation. These findings highlight the importance of carefully considering the assumptions underlying recommendation systems and designing interfaces that encourage accurate rating behavior.

Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For example, if a video connection has high tail (e.g., 99'th percentile) latency, the perceived quality will suffer, even if the average and median latencies are low. In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, value) tuples, and we wish to track the quantiles for each ID. Existing quantile sketches are designed for a plain number stream (i.e., containing just a value). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand. We first present a couple of simple and effective algorithms that serve as baselines, a sampling approach and a sketching approach. Then, we present SQUAD, an algorithm that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the quantiles of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. The algorithms are rigorously analyzed, and we demonstrate SQUAD's superiority using extensive~simulations on real-world traces.

Stream frequency measurements are fundamental in many data stream applications such as financial data trackers, intrusion-detection systems, and network monitoring. Typically, recent data items are more relevant than old ones, a notion we can capture through a sliding window abstraction. This paper considers a generalized sliding window model that supports stream frequency queries over an interval given at query time. This enables drill-down queries, in which we can examine the behavior of the system in finer and finer granularities. For this model, we asymptotically improve the space bounds of existing work, reduce the update and query time to a constant, and provide deterministic solutions. When evaluated over real Internet packet traces, our fastest algorithm processes items 90--250 times faster, serves queries at least 730 times quicker and consumes at least 40% less space than the best known method.