37451209
Storage Device Performance Prediction with CART Models
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.
15001397
An integrated computational proteomics method to extract protein targets for Fanconi Anemia studies
Fanconi Anemia (FA) is a rare autosomal genetic disease with multiple birth defects and severe childhood complications for its patients. The lack of sequence homology of the entire FA Complementation Group proteins in such as FANCC, FANCG, FANCA makes them extremely difficult to characterize using conventional bioinformatics methods. In this work, we describe how to use computational methods to extract protein targets for FA, using protein interaction data set collected for FANC group C protein (FANCC). We first generated an initial set of 130 FA-interacting proteins as "FANCC seed proteins" by merging an in-house experimental set of FANCC Tandem Affinity Purification (TAP) Pulldown Proteomics data identified from Mass Spectrometry methods with publicly available human FANCC-interacting proteins. Next, we expanded the FANCC seed proteins using a nearest-neighbor method to generate a FANCC protein interaction subnetwork of 948 proteins in 903 protein interactions. We show that this network is statistically significant, with high indices of aggregation and separations. We also show a visualization of the network, support the evidence that many well-connected proteins exists in the network. Further, we developed and applied an interaction network protein scoring algorithm, which allows us to calculate a ranked list of significant FA proteins. Our result has been supporting further biological investigations of disease biologists on our team. We believe our method can be generalized to other disease biology studies with similar problems.
28766981
An Agent-Based Collaborative Framework for Mobile P2P Applications
The design of ad-hoc, wireless, peer-to-peer applications for small mobile devices raises a number of challenges for the developer, with object synchronisation, network failure, and device limitations being the most significant. In this paper, we introduce the FRAGme2004 framework for mobile P2P application development. To address data availability and stability problems, we have devised an agent-based fostering mechanism to protect applications against data losses in cases of peers dropping out. In contrast to most current literature, we focus on small scale P2P applications, especially gaming applications.
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.
15805455
SciPort: An Adaptable Scientific Data Integration Platform for Collaborative Scientific Research
Scientific data are posing new challenges to data management due to the large volume, complexity and heterogeneity of the data. Meanwhile, scientific collaboration becomes increasingly important, which relies on integrating and sharing data from distributed institutions. In this demo, we present SciPort, a Web-based platform on supporting scientific data management and integration based on peer-to-peer architectures, where researchers can easily collect, publish, and share their complex scientific data across multi-institutions. SciPort provides a general metadata based data model to capture the context description of experiments and link experiment data into comprehensive metadata documents, and supports a hierarchical organization of the overall data space for data browsing. SciPort takes two alternative "peer"-to-"peer" (or peer-database-to-peer-database) based approaches to integrate scientific data: pure peer-to-peer architecture and central server based peer-to-peer architecture. The later provides a virtual view of all published data from multiple local sites and supports complex queries with XQuery. The system provides a unified framework for adaptable architectures and customizable schemas, and supplies comprehensive tool set to manage and share scientific data. SciPort was first prototyped in Siemens Corporate Research, and now becomes a mature product and has been successfully used in both biomedical research and clinical trials for scientific research communities.
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.