Jason Crawford

A Highly Efficient Runtime and Graph Library for Large Scale Graph Analytics

Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing systems have emerged targeting various perspectives of a graph application such as in memory and on disk representations, persistent storage, database capability, runtimes and execution models for exploiting parallelism, etc. In this paper we discuss a novel graph processing system called System G Native Store which allows for efficient graph data organization and processing on modern computing architectures. In particular we describe a runtime designed to exploit multiple levels of parallelism and a generic infrastructure that allows users to express graphs with various in memory and persistent storage properties. We experimentally show the efficiency of System G Native Store for processing graph queries on state-of-the-art platforms.

Graph analytics and storage

Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Data scenarios. To address the performance issues in large scale graph analytics, we develop a graph processing system called System G, which explores efficient graph data organization for parallel computing architectures. We discuss various graph data organizations and their impact on data locality during graph traversals, which results in various cache performance behavior on processor side. In addition, we analyze data parallelism from architectures perspective and experimentally show the efficiency for System G based graph analytics. We present experimental results for commodity multicore clusters and IBM PERCS supercomputers to illustrate the performance of System G for large scale graph analytics.

Expediting expertise: supporting informal social learning in the enterprise

In this paper, we present Expediting Expertise, a system designed to provide structured support to the otherwise informal process of social learning in the enterprise. It employs a data-driven approach where online content is automatically analyzed and categorized into relevant topics, topic-specific user expertise is calculated by comparing the models of individual users against those of the experts, and personalized recommendation of learning activities is created accordingly to facilitate expertise development. The systems UI is designed to provide users with ongoing feedback of current expertise, progress, and comparison with others. Learning recommendation is visualized with an interactive treemap which presents estimated return on investment and distance to current expertise for each recommended learning activity. Evaluation of the system showed very positive results.

IBM system G Social Media Solution: Analyze multimedia content, people, and network dynamics in context

We present IBM System G Social Media Solution, which includes a suite of applications designed for in-context monitoring, exploration, and analysis of social multimedia content as well as related people and network dynamics. Each individual application focuses on a unique aspect of social media data analysis in relevant context; collectively, they provide a comprehensive set of tools for exploring and analyzing real-time and historical social media data at large scale. The solution is empowered by a unified data management platform, based on a property graph model, to efficiently handle a large variety of social media applications.