231152
Capturing the spatio-temporal behavior of real traffic data
Traffic data, such as disk and memory accesses, typically exhibits burstiness, temporal locality, and spatial locality. However, except for qualitative speculations, it is not even known how to measure the spatio-temporal correlation, let alone how to re-produce it realistically. In this paper, we propose the "entropy plots" to quantify the correlation and develop a new statistical model, the "PQRS" model, to capture the burstiness and correlation of the real spatio-temporal traffic. Moreover, the model requires very few parameters and offers linear scalability. Experiments with multiple real data sets show that our model can mimic real traces very well.
1930293
Data mining meets performance evaluation: fast algorithms for modeling bursty traffic
Network, Web, and disk I/O traffic are usually bursty and self-similar and therefore cannot be modeled adequately with Poisson arrivals. However, we wish to model these types of traffic and generate realistic traces, because of obvious applications for disk scheduling, network management, and Web server design. Previous models (like fractional Brownian motion and FARIMA, etc.) tried to capture the 'burstiness'. However, the proposed models either require too many parameters to fit and/or require prohibitively large (quadratic) time to generate large traces. We propose a simple, parsimonious method, the b-model, which solves both problems: it requires just one parameter, and can easily generate large traces. In addition, it has many more attractive properties: (a) with our proposed estimation algorithm, it requires just a single pass over the actual trace to estimate b. For example, a one-day-long disk trace in milliseconds contains about 86 Mb data points and requires about 3 minutes for model fitting and 5 minutes for generation. (b) The resulting synthetic traces are very realistic: our experiments on real disk and Web traces show that our synthetic traces match the real ones very well in terms of queuing behavior.
37451209
Storage Device Performance Prediction with CART Models
17954201
Dynamic Supply Chain Integration through Intelligent Agents
The success of supply chains depends on the match between the requirements and offerings that deliver the services. It can be ensured by separating requirements from the means of realization as well as dynamically assigning available resources to requests. To achieve this, we need coordinate the flow of information among the services and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The problem is complicated as a result of undetermined requirements of decomposed individual services and unpredictable solutions contributed by service providers. This paper examines an agent-mediated coordination approach to dynamic supply chain integration. Each agent works as a broker for each service involved in the chain, exploring service requests as well as identifying suitable resources for achieving compatibility and coherence among the decisions of individual services. Based on the approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach
612178
A learning-based approach to estimate statistics of operators in continuous queries: a case study
Statistic estimation such as output size estimation of operators is a well-studied subject in the database research community, mainly for the purpose of query optimization. The assumption, however, is that queries are ad-hoc and therefore the emphasis has been on capturing the data distribution. When long standing continuous queries on a changing database are concerned, a more direct approach, namely building an estimation model for each operator, is possible. In this paper, we propose a novel learning-based method. Our method consists of two steps. The first step is to design a dedicated feature extraction algorithm that can be used incrementally to obtain feature values from the underlying data. The second step is to use a data mining algorithm to generate an estimation model based on the feature values extracted from the historical data. To illustrate the approach, this paper studies the case of similarity-based searches over streaming time series. Experimental results show this approach provides accurate statistic estimates with a low overhead.
19020482
A cost-effective three-in-one personal digital assistant input control
Abstract We attach an inexpensive pressure sensor to the side of a personal digital assistant and use it as three input devices at once. Users can squeeze the device to provide near-continuous input to applications. At the same time the drivers interpret a sudden full squeeze as the push of a virtual button. A user's sudden pressure release while squeezing is detected as the push of a second virtual button. We briefly describe our hardware and signal processing techniques. The remainder of the writing describes an experiment that explores whether users can cope cognitively with the 3-in-1 control. We compare against a three-control setup consisting of a jog wheel and two physical buttons. We show that the three-in-one control enables a 13% faster reaction time over the three-control one, but that the three-in-one control suffers a 4% penalty in the accuracy of users choosing between the two buttons in response to cues from an application. We show that a good choice of application cue is more important for assuring accuracy in the 3-in-1 than in the more traditional set of separate controls.
2554333
1-dimensional splines as building blocks for improving accuracy of risk outcomes models
Transformation of both the response variable and the predictors is commonly used in fitting regression models. However, these transformation methods do not always provide the maximum linear correlation between the response variable and the predictors, especially when there are non-linear relationships between predictors and the response such as the medical data set used in this study. A spline based transformation method is proposed that is second order smooth, continuous, and minimizes the mean squared error between the response and each predictor. Since the computation time for generating this spline is O(n), the processing time is reasonable with massive data sets. In contrast to cubic smoothing splines, the resulting transformation equations also display a high level of efficiency for scoring. Data used for predicting health outcomes contains an abundance of non-linear relationships between predictors and the outcomes requiring an algorithm for modeling them accurately. Thus, a transformation that fits an adaptive cubic spline to each of a set of variables is proposed. These curves are used as a set of transformation functions on the predictors. A case study of how the transformed variables can be fed into a simple linear regression model to predict risk outcomes is presented. The results show significant improvement over the performance of the original variables in both linear and non-linear models.
12605479
E-Service/Process Composition Through Multi-agent Constraint Management
E-service/process composition requires allocating suitable resources to a set of services that constitute a composite service/process. The problem is complicated due to undetermined constraints of each component service and unpredictable solutions contributed by service providers. It needs the ability to rapidly identify the suitable solutions as well as effectively coordinate them under various constraints. In this paper, an agent-mediated coordination framework for e-service/process composition is proposed. Each agent works as a broker for each service type, posting service constraints, searching suitable solutions and refining the constraints for achieving coherence among the decisions of each service. Based on the framework, a prototype of multi-agent supported e-supply chain composition is implemented. The experimental results indicate the significant effectiveness of the approach.