Running machine learning algorithms on larger datasets is becomming more and more a necessity. Recently, a practically very relevant line of research has been to look at various programming paradigms for turning well known machine learning algorithms into distributed algorithms - meaning they can run on an infrastructure with no shared memory and slow communication between processing units. This paper introduces a well known pattern called "optimistic concurrency control" into the machine learning literature. As the authors point out, there has been some work on embarrasingly parallel algorithms, distributed algorithm using the locking paradigm and coordination-free approaches to distributed algorithms. Optimistic concurrency control is a technique which starts out by assuming that each individual processing unit can freely access shared state.

Research on distributed machine learning algorithms has focused primarily on one of two extremes--algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this "optimistic concurrency control" paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment.

As market leader in enterprise application software, SAP helps companies of all sizes and industries innovate through simplification. From the back office to the boardroom, warehouse to storefront, on premise to cloud, desktop to mobile device - SAP empowers people and organizations to work together more efficiently and use business insight more effectively to stay ahead of the competition. SAP applications and services enable customers to operate profitably, adapt continuously, and grow sustainably. Purpose and Objective: SAP Labs, LLC seeks a Software Developer at our Dublin, CA location to be responsible for development focusing on design, coding, testing, quality assurance of complex product features in a development team pertaining to Machine Learning as it relates to streaming data. Resolves complex issues within and around machine learning aspects inside a enterprise level systems kernel that is like a database kernel.

Research on distributed machine learning algorithms has focused primarily on one of two extremes---algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this optimistic concurrency control'' paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment. "