@article {12162,
	title = {The Use of Empirical Studies in the Development of High End Computing Applications},
	year = {2009},
	month = {2009/12//},
	institution = {University of Maryland, College Park},
	abstract = {This report provides a description of the research and development activities towards learning much about the development and measurement of productivity in high performance computing environments. Many objectives were accomplished including the development of a methodology for measuring productivity in the parallel programming domain. This methodology was tested over 25 times at 8 universities across the United States and can be used to aid other researchers studying similar environments. The productivity measurement methodology incorporates both development time and performance into a single productivity number. An Experiment Manager tool for collecting data on the development of parallel programs, as well as a suite of tools to aid in the capture and analysis of such data was also developed. Lastly, several large scale development environments were studied in order to better understand the environment used to build large parallel programming applications. That work also included several surveys and interviews with many professional programmers in these environments.},
	keywords = {*COMPUTER PROGRAMMING, *EMPIRICAL STUDIES, *HPC(HIGH PERFORMANCE COMPUTING), *METHODOLOGY, *PARALLEL PROCESSING, *PARALLEL PROGRAMMING, *PRODUCTIVITY, *SOFTWARE ENGINEERING, *SOFTWARE METRICS, ADMINISTRATION AND MANAGEMENT, APMS(AUTOMATED PERFORMANCE MEASUREMENT SYSTEM), COMPUTER PROGRAMMING AND SOFTWARE, COMPUTER SYSTEMS MANAGEMENT AND STANDARDS, data acquisition, efficiency, ENVIRONMENTS, HIGH END COMPUTING, HPCBUGBASE, HUMAN FACTORS ENGINEERING \& MAN MACHINE SYSTEM, learning, measurement, MPI(MESSAGE PASSING INTERFACE), PARALLEL ORIENTATION, PE62303E, PROGRAMMERS, SE(SOFTWARE ENGINEERING), SUPERVISORS, TEST AND EVALUATION, TEST FACILITIES, EQUIPMENT AND METEORS, TIME, tools, United States, WUAFRLT810HECA},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA511351},
	author = {Basili, Victor R. and Zelowitz,Marvin V}
}
@article {14506,
	title = {Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks},
	year = {2008},
	month = {2008/06/29/},
	institution = {OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Our work has focused on developing new cost sensitive feature acquisition and classification algorithms, mapping these algorithms onto camera networks, and creating a test bed of video data and implemented vision algorithms that we can use to implement these. First, we will describe a new algorithm that we have developed for feature acquisition in Hidden Markov Models (HMMs). This is particularly useful for inference tasks involving video from a single camera, in which the relationship between frames of video can be modeled as a Markov chain. We describe this algorithm in the context of using background subtraction results to identify portions of video that contain a moving object. Next, we will describe new algorithms that apply to general graphical models. These can be tested using existing test sets that are drawn from a range of domains in addition to sensor networks.},
	keywords = {*ALGORITHMS, *CLASSIFICATION, data acquisition, DATA FUSION, Detectors, Feature extraction, HMM(HIDDEN MARKOV MODELS), NETWORKS, NUMERICAL MATHEMATICS, PE611102, RANDOM FIELDS, STATISTICS AND PROBABILITY, TEST SETS, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA500520},
	author = {Jacobs, David W. and Getoor, Lise}
}
@conference {13367,
	title = {Exploiting Correlated Attributes in Acquisitional Query Processing},
	booktitle = {21st International Conference on Data Engineering, 2005. ICDE 2005. Proceedings},
	year = {2005},
	month = {2005/04/05/08},
	pages = {143 - 154},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate the selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques.},
	keywords = {acquisitional query processing, Computer networks, Costs, data acquisition, Delay, Distributed computing, distributed information system, Distributed information systems, distributed processing, exponential time algorithm, optimization techniques, polynomial-time heuristic, Polynomials, probability, Query processing, real-time systems, real-world sensor-network, Runtime, Sensor phenomena and characterization, Sensor systems},
	isbn = {0-7695-2285-8},
	doi = {10.1109/ICDE.2005.63},
	author = {Deshpande, Amol and Guestrin,C. and Wei Hong and Madden,S.}
}
@conference {14760,
	title = {Dynamic program instrumentation for scalable performance tools},
	booktitle = {Scalable High-Performance Computing Conference, 1994., Proceedings of the},
	year = {1994},
	month = {1994/05//},
	pages = {841 - 850},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Presents a new technique called {\textquoteleft}dynamic instrumentation{\textquoteright} that provides efficient, scalable, yet detailed data collection for large-scale parallel applications. Our approach is unique because it defers inserting any instrumentation until the application is in execution. We can insert or change instrumentation at any time during execution by modifying the application{\textquoteright}s binary image. Only the instrumentation required for the currently selected analysis or visualization is inserted. As a result, our technique collects several orders of magnitude less data than traditional data collection approaches. We have implemented a prototype of our dynamic instrumentation on the CM-5, and present results for several real applications. In addition, we include recommendations to operating system designers, compiler writers, and computer architects about the features necessary to permit efficient monitoring of large-scale parallel systems},
	keywords = {Application software, binary image, compiler writing, Computer architecture, Computer displays, Computerized monitoring, Concurrent computing, data acquisition, data collection, data visualisation, Data visualization, dynamic program instrumentation, efficient monitoring, executing program, Instruments, large-scale parallel applications, Large-scale systems, operating system design, Operating systems, parallel programming, program analysis, program diagnostics, program visualization, Programming profession, Sampling methods, scalable performance tools, software tools},
	isbn = {0-8186-5680-8},
	doi = {10.1109/SHPCC.1994.296728},
	author = {Hollingsworth, Jeffrey K and Miller, B. P and Cargille, J.}
}
@article {18432,
	title = {Software Process Improvement in the NASA Software Engineering Laboratory.},
	year = {1994},
	month = {1994/12//},
	institution = {CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INSTITUTE},
	abstract = {The Software Engineering Laboratory (SEL) was established in 1976 for the purpose of studying and measuring software processes with the intent of identifying improvements that could be applied to the production of ground support software within the Flight Dynamics Division (FDD) at the National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC). The SEL has three member organizations: NASA/GSFC, the University of Maryland, and Computer Sciences Corporation (CSC). The concept of process improvement within the SEL focuses on the continual understanding of both process and product as well as goal-driven experimentation and analysis of process change within a production environment.},
	keywords = {*AWARDS, *SOFTWARE ENGINEERING, *SYSTEMS ANALYSIS, *WORK MEASUREMENT, ADMINISTRATION AND MANAGEMENT, COMPUTER PROGRAMMING AND SOFTWARE, COMPUTER PROGRAMS, COMPUTERS, data acquisition, ENVIRONMENTS, EXPERIMENTAL DATA, GROUND SUPPORT., measurement, Organizations, PE63756E, Production, SOFTWARE PROCESS IMPROVEMENT, SPI(SOFTWARE PROCESS IMPROVEMENT), SPN-19950120014},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA289912},
	author = {McGarry,Frank and Pajerski,Rose and Page,Gerald and Waligora,Sharon and Basili, Victor R.}
}
