Chien-Sing Lee

Data storage practices and query processing in XML databases: a survey

With the rapid emergence of XML as a data exchange standard over the Web, storing and querying XML data have become critical issues. The two main approaches to storing XML data are (1) to employ traditional storage such as relational database, object-oriented database and so on, and (2) to create an XML-specific native storage. The storage representation affects the efficiency of query processing. In this paper, firstly, we review the two approaches for storing XML data. Secondly, we review various query optimization techniques such as indexing, labeling and join algorithms to enhance query processing in both approaches. Next, we suggest an indexing classification scheme and discuss some of the current trends in indexing methods, which indicate a clear shift towards hybrid indexing.

Hybrid genetic algorithm and association rules for mining workflow best practices

Business workflow analysis has become crucial in strategizing how to create competitive edge. Consequently, deriving a series of positively correlated association rules from workflows is essential to identify strong relationships among key business activities. These rules can subsequently, serve as best practices. We have addressed this problem by hybridizing genetic algorithm with association rules. First, we used correlation to replace support-confidence in genetic algorithm to enable dynamic data-driven determination of support and confidence, i.e., use correlation to optimize the derivation of positively correlated association rules. Second, we used correlation as fitness function to support upward closure in association rules (hitherto, association rules support only downward closure). The ability to support upward closure allows derivation of the most specific association rules (business model) from less specific association rules (business meta-model) and generic association rules (reference meta-model). Downward closure allows the opposite. Upward-downward closures allow the manager to drill-down and analyze based on the degree of dependency among business activities. Subsequently, association rules can be used to describe best practices at the model, meta-model and reference meta-model levels with the most general positively dependent association rules as reference meta-model. Experiments are based on an online hotel reservation system.

Discovering fuzzy association rule patterns and increasing sensitivity analysis of XML-related attacks

Most active research in Host and Network-based Intrusion Detection (ID) and Intrusion Prevention (IP) systems are only able to detect and prevent attacks of the computer systems and attacks at the Network Layer. They are not adequate to countermeasure XML-related attacks. Furthermore, although research have been conducted to countermeasure Web application attacks, they are still not adequate in countering SOAP or XML-based attacks. In this paper, a predictive fuzzy association rule model aimed at segregating known attack patterns (such as SQL injection, buffer overflow and SOAP oversized payload) and anomalies is developed. First, inputs are validated using business policies. The validated input is then fed into our fuzzy association rule model (FARM). Consequently, 20 fuzzy association rule patterns matching input attributes with 3 decision outcomes are discovered with at least 99% confidence. These fuzzy association rule patterns will enable the identification of frequently occurring features, useful to the security administrator in prioritizing which feature to focus on in the future, hence addressing the features selection problem. Data simulated using a Web service e-commerce application are collected and tested on our model. Our models detection or prediction rate is close to 100% and false alarm rate is less than 1%. Compared to other classifiers, our models classification accuracy using random forests achieves the best results with RMSE close to 0.02 and time to build the model within 0.02s for each data set with sample size of more than 600 instances. Thus, our novel fuzzy association rule model significantly provides a viable added layer of security protection for Web service and Business Intelligence-based applications.

Policy-enhanced ANFIS model to counter SOAP-related attacks

Business Intelligence or e-commerce applications are increasingly built on the Web Service platform. Thus, SOAP-related attacks have a higher chance of occurring at the Application Layer. Although active research has been on-going in Host and Network-based intrusion detection and intrusion prevention areas, they are not adequate to countermeasure the attacks occurring at the Application Layer. This is detrimental, especially for e-commerce where sensitive and huge amount of business-related information are being exposed over the Internet. Consequently, in this paper, a policy-enhanced fuzzy model with adaptive neuro-fuzzy inference system features is introduced. Transactions generated by simulation reveal that SOAP-related attacks at the Application Layer can be detected and prevented by validating input values, input field lengths, and SOAP size using our model to classify the possibilities of granting or denying access to the backend application or database. Restricting the inputs using business policies further strengthens the model to be able to achieve detection accuracy of 99% and false positive rate of only 1%. Thus, our model has significantly contributed to an added layer of security protection for Web Service-based e-commerce applications.

Change Detection in Ontology Versioning: A Bottom-Up Approach by Incorporating Ontology Metadata Vocabulary

In ontology versioning, change detection should be related to dependent database or metadata, and vice-versa. Therefore, there is the need to detect change based on a bottom-up approach. This paper discusses bottom-up change detection by incorporating Ontology Metadata Vocabulary (OMV) as the interface to detect change from a database or metadata perspective. With respect to ontology versioning tasks, the approach emphasizes the use of change detection as an effort to preserve the compatibility serialization between ontology and knowledge-based systems (KBS). Our proposed framework and algorithm are presented.

Layered and weighted methodology to Workflow evaluation

Software quality improvement for product or service must begin from its root, i.e., Workflow (WF) processes and consequently extend its benefits to knowledge management. However, most research on Workflow Management Systems (WFMS) focus on prediction, tracking and monitoring, not on evaluation of these processes. We propose a weighted layered methodology to assess diverse WF to form best WF practices, consequently enhancing the value of managed knowledge and to enable update, reuse and refinement of codified knowledge in meta-models and reference meta-models, which subsequently can function as implementation roadmaps. Findings from surveys on two airline systems are presented.

Software Reuse: MDA-Based Ontology Development to Support Data Access over Legacy Applications

Unified Modeling Language (UML) and ontology share common properties such as classes, properties and instances. We propose using Model-Driven Architecture (MDA) enriched with ontological approach to provide ontology development method. The method leverages the UML model in the initial phase of ontology development, and then the produced ontology is aligned with specific domain ontology. The steps involved consist of: (1) generating the UML model from the legacy application, (2) generating OWL ontology from the UML model, (3) enriching the generated ontology with domain ontology, and (4) incorporating the ontology in ontology-based query answering. For simulation, the query is implemented using SPARQL over the OpenBiblio database.