@conference {17162,
	title = {Finding comparable temporal categorical records: A similarity measure with an interactive visualization},
	booktitle = {IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009},
	year = {2009},
	month = {2009/10/12/13},
	pages = {27 - 34},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher{\textquoteright}s intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M\&M (Match \& Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M\&M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M\&M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M\&M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants.},
	keywords = {data visualisation, Educational institutions, Feedback, Information retrieval, interactive search tool, interactive systems, interactive visualization tool, large databases, M\&M Measure, Match \& Mismatch measure, Medical services, numerical time series, parameters customization, Particle measurements, Similan, similarity measure, Similarity Search, temporal categorical databases, Temporal Categorical Records, temporal databases, Testing, Time measurement, time series, transportation, usability, very large databases, visual databases, Visualization},
	isbn = {978-1-4244-5283-5},
	doi = {10.1109/VAST.2009.5332595},
	author = {Wongsuphasawat,K. and Shneiderman, Ben}
}
@article {14805,
	title = {A tool to help tune where computation is performed},
	journal = {IEEE Transactions on Software Engineering},
	volume = {27},
	year = {2001},
	month = {2001/07//},
	pages = {618 - 629},
	abstract = {We introduce a new performance metric, called load balancing factor (LBF), to assist programmers when evaluating different tuning alternatives. The LBF metric differs from traditional performance metrics since it is intended to measure the performance implications of a specific tuning alternative rather than quantifying where time is spent in the current version of the program. A second unique aspect of the metric is that it provides guidance about moving work within a distributed or parallel program rather than reducing it. A variation of the LBF metric can also be used to predict the performance impact of changing the underlying network. The LBF metric is computed incrementally and online during the execution of the program to be tuned. We also present a case study that shows that our metric can accurately predict the actual performance gains for a test suite of six programs},
	keywords = {Computational modeling, Current measurement, Distributed computing, distributed program, distributed programming, load balancing factor, Load management, parallel program, parallel programming, Performance analysis, performance evaluation, Performance gain, performance metric, Programming profession, software metrics, software performance evaluation, Testing, Time measurement, tuning},
	isbn = {0098-5589},
	doi = {10.1109/32.935854},
	author = {Eom, Hyeonsang and Hollingsworth, Jeffrey K}
}
@conference {17535,
	title = {Predicting the CPU availability of time-shared Unix systems on the computational grid},
	booktitle = {The Eighth International Symposium on High Performance Distributed Computing, 1999. Proceedings},
	year = {1999},
	month = {1999///},
	pages = {105 - 112},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Focuses on the problem of making short- and medium-term forecasts of CPU availability on time-shared Unix systems. We evaluate the accuracy with which availability can be measured using the Unix load average, the Unix utility {\textquotedblleft}vmstat{\textquotedblright} and the Network Weather Service (NWS) CPU sensor that uses both. We also examine the autocorrelation between successive CPU measurements to determine their degree of self-similarity. While our observations show a long-range autocorrelation dependence, we demonstrate how this dependence manifests itself in the short- and medium-term predictability of the CPU resources in our study},
	keywords = {accuracy, Application software, Autocorrelation, Availability, Central Processing Unit, computational grid, correlation methods, CPU availability prediction, CPU resources predictability, CPU sensor, Dynamic scheduling, grid computing, Load forecasting, long-range autocorrelation dependence, medium-term forecasts, network operating systems, Network Weather Service, NWS, performance evaluation, self-similarity degree, short-term forecasts, successive CPU measurements, Time measurement, Time sharing computer systems, time-shared Unix systems, time-sharing systems, Unix, Unix load average, vmstat utility, Weather forecasting},
	isbn = {0-7803-5681-0},
	doi = {10.1109/HPDC.1999.805288},
	author = {Wolski,R. and Spring, Neil and Hayes,J.}
}
@article {14770,
	title = {Critical path profiling of message passing and shared-memory programs},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {9},
	year = {1998},
	month = {1998/10//},
	pages = {1029 - 1040},
	abstract = {We introduce a runtime, nontrace-based algorithm to compute the critical path profile of the execution of message passing and shared-memory parallel programs. Our algorithm permits starting or stopping the critical path computation during program execution and reporting intermediate values. We also present an online algorithm to compute a variant of critical path, called critical path zeroing, that measures the reduction in application execution time that improving a selected procedure will have. Finally, we present a brief case study to quantify the runtime overhead of our algorithm and to show that online critical path profiling can be used to find program bottlenecks},
	keywords = {Computer Society, Concurrent computing, critical path computation, critical path profile, critical path zeroing, distributed processing, distributed shared memory systems, Instruments, Message passing, Monitoring, online algorithm, online critical path profiling, Parallel algorithms, program bottlenecks, Runtime, runtime nontrace-based algorithm, runtime overhead, shared-memory programs, system monitoring, Time measurement, Yarn},
	isbn = {1045-9219},
	doi = {10.1109/71.730530},
	author = {Hollingsworth, Jeffrey K}
}
