Publications

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This report is the final summation of Sandia's Grand Challenge LDRD project No.119351, 'Network Discovery, Characterization and Prediction' (the 'NGC') which ran from FY08 to FY10. The aim of the NGC, in a nutshell, was to research, develop, and evaluate relevant analysis capabilities that address adversarial networks. Unlike some Grand Challenge efforts, that ambition created cultural subgoals, as well as technical and programmatic ones, as the insistence on 'relevancy' required that the Sandia informatics research communities and the analyst user communities come to appreciate each others needs and capabilities in a very deep and concrete way. The NGC generated a number of technical, programmatic, and cultural advances, detailed in this report. There were new algorithmic insights and research that resulted in fifty-three refereed publications and presentations; this report concludes with an abstract-annotated bibliography pointing to them all. The NGC generated three substantial prototypes that not only achieved their intended goals of testing our algorithmic integration, but which also served as vehicles for customer education and program development. The NGC, as intended, has catalyzed future work in this domain; by the end it had already brought in, in new funding, as much funding as had been invested in it. Finally, the NGC knit together previously disparate research staff and user expertise in a fashion that not only addressed our immediate research goals, but which promises to have created an enduring cultural legacy of mutual understanding, in service of Sandia's national security responsibilities in cybersecurity and counter proliferation.

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The Feature Characterization Library (FCLib) is a software library that simplifies the process of interrogating, analyzing, and understanding complex data sets generated by finite element applications. This document provides an overview of the library, a description of both the design philosophy and implementation of the library, and examples of how the library can be utilized to extract understanding from raw datasets.

Link analysis typically focuses on a single type of connection, e.g., two journal papers are linked because they are written by the same author. However, often we want to analyze data that has multiple linkages between objects, e.g., two papers may have the same keywords and one may cite the other. The goal of this paper is to show that multilinear algebra provides a tool for multilink analysis. We analyze five years of publication data from journals published by the Society for Industrial and Applied Mathematics. We explore how papers can be grouped in the context of multiple link types using a tensor to represent all the links between them. A PARAFAC decomposition on the resulting tensor yields information similar to the SVD decomposition of a standard adjacency matrix. We show how the PARAFAC decomposition can be used to understand the structure of the document space and define paper-paper similarities based on multiple linkages. Examples are presented where the decomposed tensor data is used to find papers similar to a body of work (e.g., related by topic or similar to a particular author's papers), find related authors using linkages other than explicit co-authorship or citations, distinguish between papers written by different authors with the same name, and predict the journal in which a paper was published.

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We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties of the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.

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In this paper we describe a data analysis toolkit constructed to meet the needs of data discovery in large scale spatio-temporal data. The toolkit is a C library of building blocks that can be assembled into data analyses. Our goals were to build a toolkit which is easy to use, is applicable to a wide variety of science domains, supports feature-based analysis, and minimizes low-level processing. The discussion centers on the design of a data model and interface that best supports these goals and we present three usage examples. © Springer-Verlag Berlin Heidelberg 2005.

This paper describes the integration of lookmarks into the ParaView visualization tool. Lookmarks are pointers to views of specific parts of a dataset. They were so named because lookmarks are to a visualization tool and dataset as bookmarks are to a browser and the World Wide Web. A lookmark can be saved and organized among other lookmarks within the context of ParaView. Then at a later time, either in the same ParaView session or in a different one, it can be regenerated, displaying the exact view of the data that had previously been saved. This allows the user to pick up where they left off, to continue to adjust the view or otherwise manipulate the data. Lookmarks facilitate collaboration between users who wish to share views of a dataset. They enable more effective data comparison because they can be applied to other datasets. They also serve as a way of organizing a user's data. Ultimately, a lookmark is a time-saving tool that automates the recreation of a complex view of the data.