Levels of Independence in Aspect-Oriented Modeling
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: 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: 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: 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: 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: ArnetMiner: extraction and mining of academic social networks
abstract: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.

title: A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
abstract: In this paper, we propose a unified topic modeling approach and its integration into the random walk framework for academic search. Specifically, we present a topic model for simultaneously modeling papers, authors, and publication venues. We combine the proposed topic model into the random walk framework. Experimental results show that our proposed approach for academic search significantly outperforms the baseline methods of using BM25 and language model, and those of using the existing topic models (including pLSI, LDA, and the AT model).

title: Do You Know Where Your Data's Been? - Tamper-Evident Database Provenance
abstract: Database provenance chronicles the history of updates and modifications to data, and has received much attention due to its central role in scientific data management. However, the use of provenance information still requires a leap of faith. Without additional protections, provenance records are vulnerable to accidental corruption, and even malicious forgery, a problem that is most pronounced in the loosely-coupled multi-user environments often found in scientific research. 
 
This paper investigates the problem of providing integrity and tamper-detection for database provenance. We propose a checksum-based approach, which is well-suited to the unique characteristics of database provenance, including non-linear provenance objects and provenance associated with multiple fine granularities of data. We demonstrate that the proposed solution satisfies a set of desirable security properties, and that the additional time and space overhead incurred by the checksum approach is manageable, making the solution feasible in practice.

title: Detection of potential positive regulatory motifs of transcription in yeast introns by comparative analysis of oligonucleotide frequencies
abstract: We conducted a comparative statistical analysis of tetra- through hexanucleotide frequencies in two sets of introns of yeast genes. The first set consisted of introns of genes that have transcription rates higher than 30 mRNAs/h while the second set contained introns of genes whose transcription rates were lower than or equal to 10 mRNAs/h. Some oligonucleotides whose occurrence frequencies in the first set of introns are significantly higher than those in the second set of introns were detected. The frequencies of occurrence of most of these detected oligonucleotides are also significantly higher than those in the exons flanking the introns of the first set. Interestingly some of these detected oligonucleotides are the same as well known "signature" sequences of transcriptional regulatory elements. This could imply the existence of potential positive regulatory motifs of transcription in yeast introns.

title: Semantic Information Extraction of Video Based on Ontology and Inference
abstract: In this paper, a new ontology-based composite concept detection method is proposed, which adopts Bayesian network to construct the ontology and uses the inference rules to perform the composite concept detection providing the concrete concepts in a phrase of video. Furthermore, the probability instead of binary value is gained through the inference pattern of Bayesian network, which can obtain more precise results. The main contribution of this paper is that semantic concept ontology is constructed using Bayesian network and the constructed ontology represents the hierarchical relationship between the concepts which can be used for inference. The method narrows the influence of "Semantic Gap" in some extent and achieves good performance in composite concept detection.