While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.
In this paper, we present a purpose-built data management system, MLdp, for all machine learning (ML) datasets. ML applications pose some unique requirements different from common conventional data processing applications, including but not limited to: data lineage and provenance tracking, rich data semantics and formats, integration with diverse ML frameworks and access patterns, trial-and-error driven data exploration and evolution, rapid experimentation, reproducibility of the model training, strict compliance and privacy regulations, etc. Current ML systems/services, often named MLaaS, to-date focus on the ML algorithms, and offer no integrated data management system. Instead, they require users to bring their own data and to manage their own data on either blob storage or on file systems. The burdens of data management tasks, such as versioning and access control, fall onto the users, and not all compliance features, such as terms of use, privacy measures, and auditing, are available. MLdp offers a minimalist and flexible data model for all varieties of data, strong version management to guarantee re-producibility of ML experiments, and integration with major ML frameworks. MLdp also maintains the data provenance to help users track lineage and dependencies among data versions and models in their ML pipelines. In addition to table-stake features, such as security, availability and scalability, MLdp's internal design choices are strongly influenced by the goal to support rapid ML experiment iterations, which
Content personalization is a key tool in creating attractive websites. Synergies can be obtained by integrating personalization between several internet properties. In this paper we propose a hierarchical Bayesian model to address these issues. Our model allows the integration of multiple properties without changing the overall structure, which makes it easily extensible across large internet portals. It relies at its lowest level on Latent Dirichlet Allocation and on latent side features for cross-property integration. We demonstrate the efficiency of our approach by analyzing data from several properties of a major internet portal.
While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees.We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.
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