Index Support for Visualizing Large, Distributed, High-Dimension Data Sets

title: Levels of Independence in Aspect-Oriented Modeling
abstract: We are investigating the application of aspect-oriented principles as an aid toward improving the capabilities of domain-specific modeling. The approach, in general, provides transformations by weaving modeling aspects into a base model. The modeling aspects provide variation points within the model and can be used to drive the synthesis of the model into different artifacts. In this brief position paper, we describe how our current and future work improves the capabilities offered in a model-based tool. We highlight several levels of independence that make the approach applicable to numerous modeling situations.

title: A Pi-calculus-Based Business Process Formal Design Method
abstract: The pi-calculus is a model of concurrent computation, which can express communication between correlation processes. It provides a way of constructing systems in a bottom-up manner by composing subsystems, and it also has strict formal semantics. Therefore, pi-calculus is very suitable for designing business processes which are presently becoming more and more complicated. The paper proposes a composition-oriented approach to business process design based on pi-calculus theory and gives the formal definition of business process. It also illustrates the design method by simulating an example of order processing. By this method, a complicated business process can be divided into several independent simple sub-modules, which can greatly reduce the complexity of business process design.

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: 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: Factor-analysis based anomaly detection and clustering
abstract: This paper presents a novel anomaly detection and clustering algorithm for the network intrusion detection based on factor analysis and Mahalanobis distance. Factor analysis is used to uncover the latent structure of a set of variables. The Mahalanobis distance is used to determine the "similarity" of a set of values from an "unknown" sample to a set of values measured from a collection of "known" samples. By utilizing factor analysis and Mahalanobis distance, we developed an algorithm 1) to identify outliers based on a trained model, and 2) to cluster attacks by abnormal features.

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: Computing exact P-values for DNA motifs
abstract: Many heuristic algorithms have been designed to approximate P-values of DNA motifs described by position weight matrices, for evaluating their statistical significance. They often significantly deviate from the true P-value by orders of magnitude. Exact P-value computation is needed for ranking the motifs. Furthermore, surprisingly, the complexity of the problem is unknown.



We show the problem to be NP-hard, and present MotifRank, software based on dynamic programming, to calculate exact P-values of motifs. We define the exact P-value on a general and more precise model. Asymptotically, MotifRank is faster than the best exact P-value computing algorithm, and is in fact practical. Our experiments clearly demonstrate that MotifRank significantly improves the accuracy of existing approximation algorithms.


AVAILABILITY
MotifRank is available from http://bio.dlg.cn.



Supplementary data are available at Bioinformatics online.

title: A Web Service Adapter with Contract-Oriented Methodology
abstract: Loose coupling is a cornerstone for Service-Oriented Architecture (SOA). Service contracts enable loose coupling, because they conceal internal details of services from the outside world behind the facade. However, current works on service oriented analysis and design (SOAD) largely focus on defining processes for developing business processes and services without considering variability in details. In this paper, a Web Service adapter of service-contract oriented is presented in order to enhance the applicability and reusability of such services. We also introduce some critical issues about service contract analysis engine, skeleton code generate engine and web-based WSDL editor in the Web Service adapter. Additionally, the Web Service adapter designed in this paper is compared with related tools, and the results indicate that our methods demonstrate a more desirable interactivity and stability.

title: Efficient Video Authentication for H.264/AVC
abstract: This paper proposed a novel and efficient scheme to authenticate the H.264/AVC video. The scheme makes an accurate usage of the tree-structured motion compensation, motion estimation and Lagrangian optimization of the standard. Authentication information is embedded strictly based on the best mode decision strategy in the sense that if undergone any spatial and temporal attacks, the scheme can detect the tampering by the sensitive mode change. And the experimental results prove the effectiveness the algorithm against many transcoding and signal processing attacks

title: XAR-miner: efficient association rules mining for XML data
abstract: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.