AMPLab Publications

• KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics
• ActiveClean: Interactive Data Cleaning For Statistical Modeling
• Kira: Processing Astronomy Imagery Using Big Data Technology
• SparkR: Scaling R Programs with Spark
• MLlib: Machine Learning in Apache Spark
• ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning (Demonstration Paper)
• PrivateClean: Data Cleaning and Differential Privacy
• Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics
• Embarrassingly Parallel Time Series Analysis for Large Scale Weak Memory Systems
• Clamshell: Scaling Up Crowds for Low Latency Data Labeling
• SampleClean: Fast and Reliable Analytics on Dirty Data
• Scientific Computing Meets Big Data Technology: An Astronomy Use Case
• Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views
• Wisteria: Nurturing Scalable Data Cleaning Infrastructure
• Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views
• Automating Model Search for Large Scale Machine Learning
• Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity
• Spark SQL: Relational Data Processing in Spark
• Rethinking Data-Intensive Science Using Scalable Analytics Systems
• The missing piece in complex analytics: Low latency, scalable model management and serving with Velox
• Coordination Avoidance in Database Systems
• A Partitioning Framework for Aggressive Data Skipping
• Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning
• The Power of Choice in Data-Aware Cluster Scheduling
• GraphX: Graph Processing in a Distributed Dataflow Framework
• A Methodology for Learning, Analyzing, and Mitigating Social Influence Bias in Recommender Systems
• A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data
• Fine-grained Partitioning for Aggressive Data Skipping
• GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
• PLANET: Making Progress with Commit Processing in Unpredictable Environments
• Scaling the Mobile Millennium System in the Cloud
• MLI: An API for Distributed Machine Learning
• GraphX: A Resilient Distributed Graph System on Spark
• PBS at Work: Advancing Data Management with Consistency Metrics (Demo)
• Leveraging Transitive Relations for Crowdsourced Joins
• Generalized Scale Independence Through Incremental Precomputation
• RTP: Robust Tenant Placement for Elastic In-Memory Database Clusters
• Fast and Interactive Analytics over Hadoop Data with Spark
• MDCC: Multi-Data Center Consistency
• Shark: SQL and Rich Analytics at Scale
• CrowdQ: Crowdsourced Query Understanding
• MLbase: A Distributed Machine-learning System
• Crowdsourced Enumeration Queries (Best Paper Award)
• CrowdER: Crowdsourcing Entity Resolution
• Probabilistically Bounded Staleness for Practical Partial Quorums
• Shark: Fast Data Analysis Using Coarse-grained Distributed Memory (Best Demo Award)
• Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (Best Paper Award)
• PIQL: Success-Tolerant Query Processing in the Cloud
• Crowdsourcing Applications and Platforms: A Data Management Perspective (Tutorial)
• Scaling the Mobile Millennium System in the Cloud
• CrowdDB: Query Processing with the VLDB Crowd (Best Demo Award)
• The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements
• CrowdDB: Answering Queries with Crowdsourcing
• Hybrid In-Database Inference for Declarative Information Extraction
• Spark: Cluster Computing with Working Sets