Limn Matrix: A Tool for Visualizing Large, Distributed, High-Dimension Data Sets

title: Business Process-Oriented Software Architecture for Supporting Business Process Change
abstract: As a business process is essential in running an enterprise, this paper aims toward improving software architecture by extracting business-relevant semantics from business logic and transforming them as important elements that need to be molded and represented in software architecture. This allows for the development of a business process-oriented software architecture (BPOSA) that can support smoother business process change. The paper introduces the structure of BPOSA and its work mode. Then it describes the steps involved in designing a process-oriented software system, and provides analysis and design methods of extending process-relevant attributes for application in business operations.

title: A Composition-Oriented Approach for Business Process Formal Design
abstract: Traditional approaches to designing business processes are often inadequate for complicated, distributed, and collaborated settings. However since network technologies have become widespread, an increasing number of business processes exhibit these properties. Therefore a new approach is needed. To this end, we describe the motivation, conceptualization, and analysis of a composition-oriented business process design method. The key advance of our method is that it uses process algebra pi-calculus as its formal basis and builds a complex process by composing simple structured process control modules to reduce the complexity of business process design as well as let several designers collaborate the design of a complex process. To make business process more clear, we also provide way of separating exception processing logic from normal process control logic and provide scooping mechanisms to minimize the detrimental effects of unplanned contingencies by ways of composition

title: Automating change evolution in model-driven engineering
abstract: The escalating complexity of software and system models is making it difficult to rapidly explore the effects of a design decision. Automating such exploration with model transformation and aspect-oriented techniques can improve both productivity and model quality. The combination of model transformation and aspect weaving provides a powerful technology for rapidly transforming legacy systems from the high-level properties that models describe. Further, by applying aspect-oriented techniques and program transformation, small changes at the modeling level can trigger very large transformations at the source code level. Thus, model engineers can explore alternative configurations using an aspect weaver targeted for modeling tools and then use the models to generate program transformation rules for adapting legacy source code on a wide scale.

title: Aspect mining from a modelling perspective
abstract: Aspect mining aims at identifying, analysing, and refactoring crosscutting concerns throughout a legacy system for the purpose of improving software modularisation. This paper presents our investigation into raising the benefits of aspect mining to high level domain-specific models. A key contribution is the capability to identify crosscutting concerns early in development, which will assist in the modularisation of a design through aspects, before proceeding to the implementation level. Furthermore, our experience has led us to believe that aspects are easier to identify at the modelling level, because much of the accidental complexities of implementation concerns are not present in the corresponding modelling abstractions.

title: Model replication: transformations to address model scalability
abstract: 2 SUMMARY In Model-Driven Engineering, it is often desirable to evaluate different design alternatives as they relate to scalability issues of the modeled system. A typical approach to address scalability is model replication, which starts by creating base models that capture the key entities as model elements and their relationships as model connections. A collection of base models can be adorned with necessary information to characterize a specific scalability concern as it relates to how the base modeling elements are replicated and connected together. In current modeling practice, such model replication is usually accomplished by scaling the base model manually. This is a time-consuming process that represents a source of error, especially when there are deep interactions between model components. As an alternative to the manual process, this paper presents the idea of automated model replication through a model transformation process that expands the number of elements from the base model and makes the correct connections among the generated modeling elements. The paper motivates the need for model replication through case studies taken from models supporting different domains.

title: Recommendation over a Heterogeneous Social Network
abstract: With the Web content having been changed from homogeneity to heterogeneity, the recommendation becomes a more challenging issue. In this paper, we have investigated the recommendation problem on a general heterogeneous Web social network. We categorize the recommendation needs on it into two main scenarios: recommendation when a person is doing a search and recommendation when the person is browsing the information. We formalize the recommendation as a ranking problem over the heterogeneous network. Moreover, we propose using a random walk model to simultaneously ranking different types of objects and propose a pair-wise learning algorithm to learn the weight of each type of relationship in the model. Experimental results on two real-world data sets show that improvements can be obtained by comparing with the baseline methods.

title: Comparison between Voxel-based Morphometry and Volumetric Analysis in Schizophrenia
abstract: This study compares the voxel-based morphometry (VBM) and region-of-interest (ROI)-based volumetric analysis in schizophrenia. Magnetic resonance imaging (MRI) data of 50 unmedicated schizophrenia patients and 82 healthy controls were examined. ROI- based volumetric analysis was performed by tracing caudates on anterior commissure and posterior commissure (ACPC)-positioned MRI. The comparison of 20 subjects in the lower and upper 25% for traced caudate size and the comparison between patients and controls revealed significantly reduced gray matter concentration in the caudate through VBM. Caudate volume (absolute and relative) revealed that patients with schizophrenia had smaller caudate nuclei than controls. The ROC results demonstrated that the performance of VBM (area under the curve (AUC): 61%) was similar to that of ROI-based volumetric analysis (AUC: 61% for relative ROI volume) and their correlation was 0.51 (p<0.05). These results suggest that to be an alternative to ROI-based volumetry in schizophrenia, cautious interpretation of VBM may be needed.

title: An Effective and Efficient Data Cleaning Technique in Large Databases

title: Minitrack: E-Government Information and Knowledge Management
abstract: The public sector realizes the strategic importance of methods, tools and concepts applied to the efficient and effective Information and Knowledge resource Management (IKM). Because governments and public administrations deal with vast amounts of information and knowledge, it is particularly important to leverage research agendas that advance tenets related to the management of these resources. For example, much of the work of public authorities and governments is related to the collection, processing, interpretation, and use of data, information and knowledge on citizens, businesses, governments themselves, the society, the market, the environment, laws, politics, etc.

title: Hyperspectral images lossless compression by a novel three-dimensional wavelet coding
abstract: In this paper, we present a 3D hyperspectral images lossless compression algorithm, which is based on the asymmetric 3D quincunx structured wavelet transform (A3D-QWT) and the adaptive classification coding. A3D-QWT is a simpler 3D transform compared to the classical asymmetric 3D pyramidal wavelet structure (A3D-DWT) with nearly equal entropy; furthermore the spectral correlation after this quincunx structured transform is higher, which is useful to the adaptive classification coding. The adaptive classification coding can make full use of not only the spatial correlation but also the spectral correlation characteristics of hyperspectral images. Experiments show that our method is capable of providing a higher compression performance for hyperspectral images.