Joseph Hellerstein

Yifan Wu, Remco Chang, Joseph Hellerstein et al.

Interactive visualization design and research have primarily focused on local data and synchronous events. However, for more complex use cases---e.g., remote database access and streaming data sources---developers must grapple with distributed data and asynchronous events. Currently, constructing these use cases is difficult and time-consuming; developers are forced to operationally program low-level details like asynchronous database querying and reactive event handling. This approach is in stark contrast to modern methods for browser-based interactive visualization, which feature high-level declarative specifications. In response, we present DIEL, a declarative framework that supports asynchronous events over distributed data. Like many declarative visualization languages, DIEL developers need only specify what data they want, rather than procedural steps for how to assemble it; uniquely, DIEL models asynchronous events (e.g., user interactions or server responses) as streams of data that are captured in event logs. To specify the state of a user interface at any time, developers author declarative queries over the data and event logs; DIEL compiles and optimizes a corresponding dataflow graph, and synthesizes necessary low-level distributed systems details. We demonstrate DIEL's performance and expressivity through ex-ample interactive visualizations that make diverse use of remote data and coordination of asynchronous events. We further evaluate DIEL's usability using the Cognitive Dimensions of Notations framework, revealing wins such as ease of change, and compromises such as premature commitments.

Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola et al.

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.