TY - JOUR T1 - Balancing Systematic and Flexible Exploration of Social Networks JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2006 A1 - Perer,A. A1 - Shneiderman, Ben KW - Aggregates KW - algorithms KW - attribute ranking KW - Cluster Analysis KW - Computer Graphics KW - Computer simulation KW - Coordinate measuring machines KW - coordinated views KW - Data analysis KW - data visualisation KW - Data visualization KW - exploratory data analysis KW - Filters KW - Gain measurement KW - graph theory KW - Graphical user interfaces KW - Information Storage and Retrieval KW - interactive graph visualization KW - matrix algebra KW - matrix overview KW - Models, Biological KW - Navigation KW - network visualization KW - Pattern analysis KW - Population Dynamics KW - Social Behavior KW - social network analysis KW - Social network services KW - social networks KW - social sciences computing KW - Social Support KW - SocialAction KW - software KW - statistical analysis KW - statistical methods KW - User-Computer Interface AB - Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks VL - 12 SN - 1077-2626 CP - 5 M3 - 10.1109/TVCG.2006.122 ER - TY - CONF T1 - A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories T2 - Visual Analytics Science And Technology, 2006 IEEE Symposium On Y1 - 2006 A1 - Fails,J. A A1 - Karlson,A. A1 - Shahamat,L. A1 - Shneiderman, Ben KW - ball-and-chain visualization KW - Chromium KW - Computer science KW - Data analysis KW - data visualisation KW - Data visualization KW - Database languages KW - event pattern discovery KW - Graphical user interfaces KW - History KW - Information Visualization KW - Medical treatment KW - multivariate temporal data KW - Pattern analysis KW - pattern recognition KW - PatternFinder integrated interface KW - Query processing KW - query visualization KW - result-set visualization KW - Spatial databases KW - tabular visualization KW - temporal pattern discovery KW - temporal pattern searching KW - Temporal query KW - user interface KW - User interfaces KW - visual databases KW - visual interface AB - Finding patterns of events over time is important in searching patient histories, Web logs, news stories, and criminal activities. This paper presents PatternFinder, an integrated interface for query and result-set visualization for search and discovery of temporal patterns within multivariate and categorical data sets. We define temporal patterns as sequences of events with inter-event time spans. PatternFinder allows users to specify the attributes of events and time spans to produce powerful pattern queries that are difficult to express with other formalisms. We characterize the range of queries PatternFinder supports as users vary the specificity at which events and time spans are defined. Pattern Finder's query capabilities together with coupled ball-and-chain and tabular visualizations enable users to effectively query, explore and analyze event patterns both within and across data entities (e.g. patient histories, terrorist groups, Web logs, etc.) JA - Visual Analytics Science And Technology, 2006 IEEE Symposium On PB - IEEE SN - 1-4244-0591-2 M3 - 10.1109/VAST.2006.261421 ER - TY - JOUR T1 - An integrated runtime and compile-time approach for parallelizing structured and block structured applications JF - IEEE Transactions on Parallel and Distributed Systems Y1 - 1995 A1 - Agrawal,G. A1 - Sussman, Alan A1 - Saltz, J. KW - Bandwidth KW - block structured applications KW - block structured codes KW - compile-time approach KW - compiling applications KW - data access patterns KW - Data analysis KW - Delay KW - distributed memory machines KW - distributed memory systems KW - FORTRAN KW - Fortran 90D/HPF compiler KW - High performance computing KW - HPF-like parallel programming languages KW - integrated runtime approach KW - irregularly coupled regular mesh problems KW - multigrid code KW - Navier-Stokes solver template KW - Parallel machines KW - parallel programming KW - Pattern analysis KW - performance evaluation KW - program compilers KW - Program processors KW - Runtime library KW - Uninterruptible power systems AB - In compiling applications for distributed memory machines, runtime analysis is required when data to be communicated cannot be determined at compile-time. One such class of applications requiring runtime analysis is block structured codes. These codes employ multiple structured meshes, which may be nested (for multigrid codes) and/or irregularly coupled (called multiblock or irregularly coupled regular mesh problems). In this paper, we present runtime and compile-time analysis for compiling such applications on distributed memory parallel machines in an efficient and machine-independent fashion. We have designed and implemented a runtime library which supports the runtime analysis required. The library is currently implemented on several different systems. We have also developed compiler analysis for determining data access patterns at compile time and inserting calls to the appropriate runtime routines. Our methods can be used by compilers for HPF-like parallel programming languages in compiling codes in which data distribution, loop bounds and/or strides are unknown at compile-time. To demonstrate the efficacy of our approach, we have implemented our compiler analysis in the Fortran 90D/HPF compiler developed at Syracuse University. We have experimented with a multi-bloc Navier-Stokes solver template and a multigrid code. Our experimental results show that our primitives have low runtime communication overheads and the compiler parallelized codes perform within 20% of the codes parallelized by manually inserting calls to the runtime library VL - 6 SN - 1045-9219 CP - 7 M3 - 10.1109/71.395403 ER -