David Ediger

David Ediger
Georgia Institute of Technology | GT · Information and Communications Laboratory (ICL)

Doctor of Philosophy

About

30
Publications
9,088
Reads
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827
Citations
Introduction
Skills and Expertise
Additional affiliations
June 2013 - present
Georgia Tech Research Institute
Position
  • Research Engineer II
August 2008 - May 2013
Georgia Institute of Technology
Position
  • Research Assistant
Education
August 2008 - May 2013
Georgia Institute of Technology
Field of study
  • Electrical & Computer Engineering
August 2004 - May 2008
George Washington University
Field of study
  • Computer Engineering

Publications

Publications (30)
Article
With the growth of social media, embedded sensors, and 'smart' devices, those responsible for managing resources during emergencies, such as weather-related disasters, are transitioning from an era of data scarcity to data deluge. During a crisis situation, emergency managers must aggregate various data to assess the situation on the ground, evalua...
Conference Paper
Analytics applications for reporting and human interaction with big data rely upon scalable frameworks for data ingest, storage, and computation. Batch processing of analytic workloads increases latency of results and can perform redundant computation. In real-world applications, new data points are continuously arriving and a suite of algorithms m...
Conference Paper
Full-text available
With the proliferation of large, irregular, and sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users t...
Article
Full-text available
The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing these large data sets, even to answer simple analytic queries, often pushes the limits of algorithms a...
Article
Full-text available
With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to inge...
Conference Paper
Full-text available
In this paper we propose a new methodology for gaining insight into the temporal aspects of social networks. In order to develop higher-level, large-scale data analysis methods for classification, prediction, and anomaly detection, a solid foundation of analytical techniques is required. We present a novel approach to the analysis of these networks...
Chapter
Full-text available
In this chapter the author presents a new, extensible and flexible data structure for massive graphs called STINGER (Spatio-Temporal Interaction Networks and Graphs (STING) Extensible Representation). Two studies are discussed: the first study, computing a widely used network analysis metric called clustering coefficients, and the second study, the...
Conference Paper
Full-text available
Implementing parallel graph algorithms in large, shared memory machines, such as the Cray XMT, can be challenging for programmers. Synchronization, deadlock, hot spotting, and others can be barriers to obtaining linear scalability. Alternative programming models, such as the bulk synchronous parallel programming model used in Google's Pregel, have...
Conference Paper
Full-text available
Turning large volumes of data into actionable knowledge is a top challenge in high performance computing. Our previous work in this area demonstrated algorithmic techniques for massively parallel graph analysis on multithreaded systems. This work led to the development of GraphCT, the first end-to-end graph analytics platform for the Cray XMT and x...
Article
Full-text available
Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, financial transactions, and other valuable data sources. Our software framework, STING (Spatio-Temporal Interaction Networks and Graphs), uses a scalable, high-performance graph data structure to enable these applications. STING support...
Conference Paper
Full-text available
The current research focus on “big data” problems highlights the scale and complexity of analytics required and the high rate at which data may be changing. In this paper, we present our high performance, scalable and portable software, Spatio-Temporal Interaction Networks and Graphs Extensible Representation (STINGER), that includes a graph data s...
Article
Full-text available
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. The volume and richness of data combined with its rate of change renders monitoring properties at scale by static recomputation infeasible....
Conference Paper
Full-text available
Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, financial transactions, and other valuable data sources. We introduce a framework, STING (Spatio-Temporal Interaction Networks and Graphs), and evaluate its performance on multicore, multisocket Intel\textregistered-based platforms. STI...
Conference Paper
Full-text available
An increasingly fast-paced, digital world has produced an ever-growing volume of petabyte-sized datasets. At the same time, terabytes of new, unstructured data arrive daily. As the desire to ask more detailed questions about these massive streams has grown, parallel software and hardware have only recently begun to enable complex analytics in this...
Article
Current tools for analyzing graph-structured data and semantic networks focus on static graphs. Our STING package tackles analysis of streaming graphs like today's social networks and communication tools. STING maintains a massive graph under changes while coordinating analysis kernels to achieve analysis at real-world data rates. We show examples...
Article
Full-text available
Modern multicore and manycore systems have the strong potential to deliver both high performance and high power efficiency. The large variance in memory access latency, resource sharing, and the heterogeneity of processor architectures in modern multicore and manycore systems raise significant algorithm engineering challenges. In this article, we o...
Conference Paper
Full-text available
Tackling the current volume of graph-structured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices and nearly 2 billion edges in under 7300 seconds on a massively mu...
Conference Paper
Full-text available
Current online social networks are massive and still growing. For example, Face book has over 500 million active users sharing over 30 billion items per month. The scale within these data streams has outstripped traditional graph analysis methods. Real-time monitoring for anomalies may require dynamic analysis rather than repeated static analysis....
Conference Paper
An increasingly fast-paced, digital world has produced an ever-growing volume of petabyte-sized datasets. At the same time, terabytes of new, unstructured data arrive daily. As the desire to ask more detailed questions about these massive streams has grown, parallel software and hardware have only recently begun to enable complex analytics in this...
Conference Paper
Full-text available
Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Toolkit for massive graphs representing social...
Conference Paper
Full-text available
We present a new approach for parallel massive graph analysis of streaming, temporal data with a dynamic and extensible representation. Handling the constant stream of new data from health care, security, business, and social network applications requires new algorithms and data structures. We examine data structure and algorithm trade-offs that ex...
Conference Paper
Full-text available
We present a new parallel algorithm that extends and gen- eralizes the traditional graph analysis metric of betweenness centrality to include additional non-shortest paths according to an input parameter k. Betweenness centrality is a useful kernel for analyzing the importance of vertices or edges in a graph and has found uses in social networks, b...
Conference Paper
Full-text available
We present a new lock-free parallel algorithm for com- puting betweenness centrality of massive complex networks that achieves better spatial locality compared with previ- ous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applica- tions ranging from social networks, to power grids, to the i...

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