Model-driven performance analysis of rule-based domain specific visual models

ContextDomain-Specific Visual Languages (DSVLs) play a crucial role in Model-Driven Engineering (MDE). Most DSVLs already allow the specification of the structure and behavior of systems. However, there is also an increasing need to model, simulate and reason about their non-functional properties. In particular, QoS usage and management constraints (performance, reliability, etc.) are essential characteristics of any non-trivial system.ObjectiveVery few DSVLs currently offer support for modeling these kinds of properties. And those which do, tend to require skilled knowledge of specialized notations, which clashes with the intuitive nature of DSVLs. In this paper we present an alternative approach to specify QoS properties in a high-level and platform-independent manner.MethodWe propose the use of special objects (observers) that can be added to the graphical specification of a system for describing and monitoring some of its non-functional properties.ResultsObservers allow extending the global state of the system with the variables that the designer wants to analyze, being able to capture the performance properties of interest. A performance evaluation tool has also been developed as a proof of concept for the proposal.ConclusionThe results show how non-functional properties can be specified in DSVLs using observers, and how the performance of systems specified in this way can be evaluated in a flexible and effective way.     

Model-driven coordinated management of data centers

Management of computing infrastructure in data centers is an important and challenging problem, that needs to: (i) ensure availability of services conforming to the Service Level Agreements (SLAs); and (ii) reduce the Power Usage Effectiveness (PUE), i.e. the ratio of total power, up to half of which is attributed to data center cooling, over the computing power to service the workloads. The cooling energy consumption can be reduced by allowing higher-than-usual thermostat set temperatures while maintaining the ambient temperature in the data center room within manufacturer-specified server redline temperatures for their reliable operations. This paper proposes: (i) a Coordinated Job, Power, and Cooling Management (JPCM) policy, which performs: (a) job management so as to allow for an increase in the thermostat setting of the cooling unit while meeting the SLA requirements, (b) power management to reduce the produced thermal load, and (c) cooling management to dynamically adjust the thermostat setting; and (ii) a Model-driven coordinated Management Architecture (MMA), which uses a state-based model to dynamically decide the correct management policy to handle events, such as new workload arrival or failure of a cooling unit, that can trigger an increase in the ambient temperature. Each event is associated with a time window, referred to as the window-of-opportunity, after which the temperature at the inlet of one or more servers can go beyond the redline temperature if proper management policies are not enforced.This window-of-opportunity monotonically decreases with increase in the incoming workload. The selection of the management policy depends on their potential energy benefits and the conformance of the delays in their actuation to the window-of-opportunity. Simulations based on actual job traces from the ASU HPC data center show that the JPCM can achieve up to 18% energy-savings over separated power or job management policies. However, high delay to reach a stable ambient temperature (in case of cooling management through dynamic thermostat setting) can violate the server redline temperatures. A management decision chart is developed as part of MMA to autonomically employ the management policy with maximum energy-savings without violating the window-of-opportunity, and hence the redline temperatures. Further, a prototype of the JPCM is developed by configuring the widely used Moab cluster manager to dynamically change the server priorities for job assignment.     

模型驱动的预算管理系统设计与实现

针对财务预算的领域概念和复杂需求,将一种模型驱动的开发方式实践于预算管理系统的设计与实现,使开发人员脱离复杂的数据库设计,更加专注领域概念的消化、模型和数据传输对象的创建。给出了预算管理系统的整体说明和框架的总体结构设计,并以开发顺序依次介绍了各层的设计与实现,最后结合系统快速上线的效果说明了这种模型驱动开发方式的可行性、便利性和快捷性。     

全相联异构数据集成系统的研究和设计

本文介绍了在建设出入境人员健康状况实时监测网络采用的基于Web Services的全相联异构数据集成方案,该方案利用Web Services的跨平台等特点,综合了数据仓库和虚拟数据库在异构数据集成中的优点,通过设计领域字典表及字段、量纲等映射表,有效地解决了异构数据集成中的环境异构和数据异构等问题,并从异构数据转化、全局查询等方面阐述了该方案的实现过程。实践表明,该方案对了分布式异构数据的共享问题解决具有可行性和高效性。     

SemQuery: semantic clustering and querying on heterogeneous features for visual data

The effectiveness of content-based image retrieval can be enhanced using heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrievals on heterogeneous features is a nontrivial task. We present a semantics-based clustering and indexing approach, termed SemQuery, to support visual queries on heterogeneous features of images. Using this approach, the database images are classified based on their heterogeneous features. Each semantic image cluster contains a set of subclusters that are represented by the heterogeneous features that the images contain. An image is included in a semantic cluster if it falls within the scope of all the heterogeneous clusters of the semantic cluster. We also design a neural network model to merge the results of basic queries on individual features. A query processing strategy is then presented to support visual queries on heterogeneous features. An experimental analysis is conducted and presented to demonstrate the effectiveness and efficiency of the proposed approach.     

Model-driven multidimensional modeling of secure data warehouses

Data Warehouses (DW), Multidimensional (MD) databases, and On-Line Analytical Processing (OLAP) applications provide companies with many years of historical information for the decision-making process. Owing to the relevant information managed by these systems, they should provide strong security and confidentiality measures from the early stages of a DW project in the MD modeling and enforce them. In the last years, there have been some proposals to accomplish the MD modeling at the conceptual level. Nevertheless, none of them considers security measures as an important element in their models, and therefore, they do not allow us to specify confidentiality constraints to be enforced by the applications that will use these MD models. In this paper, we present an Access Control and Audit (ACA) model for the conceptual MD modeling. Then, we extend the Unified Modeling Language (UML) with this ACA model, representing the security information (gathered in the ACA model) in the conceptual MD modeling, thereby allowing us to obtain secure MD models. Moreover, we use the OSCL (Object Security Constraint Language) to specify our ACA model constraints, avoiding in this way an arbitrary use of them. Furthermore, we align our approach with the Model-Driven Architecture, the Model-Driven Security and the Model-Driven Data Warehouse, offering a proposal highly compatible with the more recent technologies.     

Improved visual correlation analysis for multidimensional data

With the era of data explosion coming, multidimensional visualization, as one of the most helpful data analysis technologies, is more frequently applied to the tasks of multidimensional data analysis. Correlation analysis is an efficient technique to reveal the complex relationships existing among the dimensions in multidimensional data. However, for the multidimensional data with complex dimension features,traditional correlation analysis methods are inaccurate and limited. In this paper, we introduce the improved Pearson correlation coefficient and mutual information correlation analysis respectively to detect the dimensions’ linear and non-linear correlations. For the linear case,all dimensions are classified into three groups according to their distributions. Then we correspondingly select the appropriate parameters for each group of dimensions to calculate their correlations. For the non-linear case,we cluster the data within each dimension. Then their probability distributions are calculated to analyze the dimensions’ correlations and dependencies based on the mutual information correlation analysis. Finally,we use the relationships between dimensions as the criteria for interactive ordering of axes in parallel coordinate displays.     

Interactive visual analysis of perfusion data

Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimensionreduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.     

Interactive visual analysis of set-typed data

While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item--like one cell in a table--has a list of n > or = 0 elements as its value. We present the set'o'gram as a new visualization approach to represent data of set type and to enable interactive visual exploration and analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have a larger number of dimensions (more than a dozen or more), especially also in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people.     

Joint design of clustering and in-cluster data route for heterogeneous wireless sensor networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes. In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that, we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.     

Model-driven monitoring support for the multi-view performance analysis of parallel embedded applications

This paper describes an approach to carry out performance analysis of parallel embedded applications. The approach is based on measurement, but in addition, the idea of driving the measurement process (application instrumentation and monitoring) by a behavioral model is introduced. Using this model, highly comprehensible performance information can be collected. The whole approach is based on this behavioral model, one instrumentation method and two tools, one for monitoring and the other for visualization and analysis. Each of these is briefly described, and the steps to carry out performance analysis using them are clearly defined. They are explained by means of a case study. Finally, one method to evaluate the intrusiveness of the monitoring approach is proposed, and the intrusiveness results for the case study are presented.     

Brushing dimensions--a dual visual analysis model for high-dimensional data

In many application fields, data analysts have to deal with datasets that contain many expressions per item. The effective analysis of such multivariate datasets is dependent on the user's ability to understand both the intrinsic dimensionality of the dataset as well as the distribution of the dependent values with respect to the dimensions. In this paper, we propose a visualization model that enables the joint interactive visual analysis of multivariate datasets with respect to their dimensions as well as with respect to the actual data values. We describe a dual setting of visualization and interaction in items space and in dimensions space. The visualization of items is linked to the visualization of dimensions with brushing and focus+context visualization. With this approach, the user is able to jointly study the structure of the dimensions space as well as the distribution of data items with respect to the dimensions. Even though the proposed visualization model is general, we demonstrate its application in the context of a DNA microarray data analysis.     

Generic visual analysis for multi- and hyperspectral image data

Multi- and hyperspectral imaging and data analysis has been investigated in the last decades in the context of various fields of application like remote sensing or microscopic spectroscopy. However, recent developments in sensor technology and a growing number of application areas require a more generic view on data analysis, that clearly expands the current, domain-specific approaches. In this context, we address the problem of interactive exploration of multi- and hyperspectral data, consisting of (semi-)automatic data analysis and scientific visualization in a comprehensive fashion. In this paper, we propose an approach that enables a generic interactive exploration and easy segmentation of multi- and hyperspectral data, based on characterizing spectra of an individual dataset, the so-called endmembers. Using the concepts of existing endmember extraction algorithms, we derive a visual analysis system, where the characteristic spectra initially identified serve as input to interactively tailor a problem-specific visual analysis by means of visual exploration. An optional outlier detection improves the robustness of the endmember detection and analysis. An adequate system feedback of the costly unmixing procedure for the spectral data with respect to the current set of endmembers is ensured by a novel technique for progressive unmixing and view update which is applied at user modification. The progressive unmixing is based on an efficient prediction scheme applied to previous unmixing results. We present a detailed evaluation of our system in terms of confocal Raman microscopy, common multispectral imaging and remote sensing.     

Feature-based design for heterogeneous objects

Heterogeneous objects are objects composed of different constituent materials. In these objects, multiple desirable properties from different constituent materials can be synthesized into one part. In order to obtain mass applications of such heterogeneous objects, efficient and effective design methodologies for heterogeneous objects are crucial.In this paper, we present a feature based design methodology to facilitate heterogeneous object design. Under this methodology, designers design heterogeneous objects using high-level design components that have engineering significance. These high level components are form features and material features. In this paper, we first examine the relationships between form features and material features in heterogeneous objects. We then propose three synthesized material features in accordance with our examination of these features. Based on these proposed features, we develop a feature based design methodology for heterogeneous objects. Two enabling methods for this design methodology, material heterogeneity specification within each feature and combination of these material features, are developed. A physics (diffusion) based B-spline method is developed to (1) allow design intent of material variation be explicitly captured by boundary conditions, (2) ensure smooth material variation across the feature volume. A novel method, direct face neighborhood alteration, is developed to increase the efficiency of combining heterogeneous material features.Examples of using this feature based design methodology for heterogeneous object design, such as a prosthesis design, are presented.     

Interactive visual analysis of time-series microarray data

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.     

异构数据库系统数据转换方法设计与实现

针对传统关系数据库处理海量数据效率低下的问题,提出了一种基于键值数据库Level DB与传统关系数据库My SQL协同存储管理海量数据的方案,以及两种数据库之间数据转换方法。首先对两种数据库特点进行了分析,提出了一种异构数据库系统存储策略,并在此基础上介绍了Level DB到My SQL的数据转移流程以及基于实用性的异构数据转换方法,最后给出了数据转换应用示例展示,结果表明,该方法具备良好的可行性和实用价值。     

Model-driven vision for in-door navigation

For navigation in a partially known environment it is possible to provide a model that may be used for guidance in the navigation and as a basis for selective sensing. In this paper a navigation system for an autonomous mobile robot is presented. Both navigation and sensing is built around a graphics model, which enables prediction of the expected scene content. The model is used directly for prediction of line segments which, through matching, allow estimation of position and orientation. In addition, the model is used as a basis for a hierarchical stereo matching that enables dynamic updating of the model with unmodelled objects in the environment. For short-term path planning a set of reactive behaviours is used. The reactive behaviours include use of inverse perspective mapping for generation of occupancy grids, a sonar system and simple gaze holding for monitoring of dynamic obstacles. The full system and its component processes are described and initial experiments with the system are briefly outlined.     

Model-driven analysis and synthesis of textual concrete syntax

Meta-modeling is raising more and more interest in the field of language engineering. While this approach is now well understood for defining abstract syntaxes, formally defining textual concrete syntaxes with meta-models is still a challenge. Textual concrete syntaxes are traditionally expressed with rules, conforming to EBNF-like grammars, which can be processed by compiler compilers to generate parsers. Unfortunately, these generated parsers produce concrete syntax trees, leaving a gap with the abstract syntax defined by meta-models, and further ad hoc hand-coding is required. In this paper we propose a new kind of specification for concrete syntaxes, which takes advantage of meta-models to generate fully operational tools (such as parsers or text generators). The principle is to map abstract syntaxes to textual concrete syntaxes via bidirectional mapping-models with support for both model-to-text, and text-to-model transformations.