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Readings

Below you can find a list of recommended readings and resources the lectures are based on. Parts of the resources listed here will be given as (non-graded) reading assignments during the course. You should be able to access all of them for free when connected to the campus network. Please contact the instructor if any of the links below is broken.

Books and Surveys

• Provenance in Databases: Why, How, and Where
• A survey on provenance: What for? What form? What from?
• So, you want to trace your distributed system? Key design insights from years of practical experience
• Provenance in Scientific Workflow Systems
• Towards Explainable Artificial Intelligence
• Interpretable Machine Learning

Data Provenance and Causality

• Why and Where: A characterization of data provenance
• Why not?
• Why so? or Why no? Functional causality for explaining query answers
• Explaining outputs in modern data analytics
• Provenance for generalized Map and Reduce workflows
• Tiresias: The database oracle for how-to queries
• SMOKE: Fine-grained lineage at interactive speed
• Provenance for natural language queries
• SubZero: A fine-grained lineage system for scientific databases
• Titian: Data provenance support in Spark

Explainable AI and Recommender Systems

• Intelligible Models for Classification and Regression
• Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
• “Why should I trust you?” Explaining the predictions of any classifier
• A unified approach to interpreting model predictions
• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
• Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
• Explaining collaborative filtering recommendations
• DeepBase: Deep inspection of neural networks

System Profiling and Performance Analysis

• Coz: finding code that counts with causal profiling
• Dapper, a Large-Scale Distributed Systems Tracing Infrastructure
• X-Trace: A pervasive network tracing framework
• Pivot Tracing: Dynamic causal monitoring for distributed systems
• Scalability! But at what COST?
• Making sense of performance in data analytics frameworks
• SnailTrail: Generalizing critical paths for online analysis of distributed dataflows
• The Mystery Machine: End-to-end performance analysis of large-scale internet services
• Performance debugging for distributed systems of black boxes
• The good, the bad, and the differences: Better network diagnostics with differential provenance
• Automating failure testing research at internet scale
• Canopy: An end-to-end performance tracing and analysis system
• 3MileBeach: A Tracer with Teeth