Tree structure data change detection method

The new method, increasing efficiency and reliability of change detection in three structures in the Internet data under indetermination of data structure (DTD, XML-Schema) is proposed in this paper. The Boolean linear programming problem was solved in two exact methods – modified Balazs with filter and modified DP method and A modified method for selecting a neural network architecture was proposed. There is also considered publish/subscribe system description enhanced with core module, which provides notifications of changes to subscribers only in case they occurred

BGrep and BDiff: UNIX Tools for High-Level Languages

The rise in high-level languages for system administrators requires us to rethink traditional UNIX tools designed for these older data formats. We propose new block-oriented tools, bgrep and bdiff, operating on syntactic blocks of code rather than the line, the traditional information container of UNIX. Transcending the line number allows us to introduce longitudinal diff, a mode of bdiff that lets us track changes across arbitrary blocks of code. We present a detailed implementation roadmap and evaluation framework for the full version of this paper. In addition we demonstrate how the design of our tools already addresses several real-wold problems faced by network administrators to maintain security policy

A first exploration of an inductive analysis approach for detecting learning design patterns

Please cite as: Francis Brouns, Rob Koper, Jocelyn Manderveld, Jan van Bruggen, Peter Sloep, Peter van Rosmalen, Colin Tattersall and Hubert Vogten (2005). A first exploration of an inductive analysis approach for detecting learning design patterns. Journal of Interactive Media in Education (Advances in Learning Design. Special Issue, eds. Colin Tattersall, Rob Koper), 2005/03. ISSN:1365-893X [http://jime.open.ac.uk/2005/03]One way to develop effective online courses is the use of learning design patterns, since patterns capture successful solutions. Pedagogical patterns are commonly created by human cognitive processing in "writer's workshops". We explore two ideas; first whether IMS Learning Design is suitable for detecting patterns in existing courses and secondly whether the use of inductive analyses is a suitable approach. We expect patterns to occur in the method section of a learning design, because here the process of teaching and learning is defined. We provide some suggestions for inductive techniques that could be applied to existing learning designs in order to detect patterns and discuss how the patterns could be used to create new learning designs. None of the suggested approaches are validated yet, but are intended as input for the ongoing discussion on patterns

Tree mining application to matching of hetereogeneous knowledge

Matching of heterogeneous knowledge sources is of increasing importance in areas such as scientific knowledge management, e-commerce, enterprise application integration, and many emerging Semantic Web applications. With the desire of knowledge sharing and reuse in these fields, it is common that the knowledge coming from different organizations from the same domain is to be matched. We propose a knowledge matching method based on our previously developed tree mining algorithms for extracting frequently occurring subtrees from a tree structured database such as XML. Using the method the common structure among the different representations can be automatically extracted. Our focus is on knowledge matching at the structural level and we use a set of example XML schema documents from the same domain to evaluate the method. We discuss some important issues that arise when applying tree mining algorithms for detection of common document structures. The experiments demonstrate the usefulness of the approach

Learning Design Patterns: Exploring an inductive analysis approach

Preprint of article submitted to the joint Unfold/Prolearn Workshop, September 2005; to be published in a Special Issue on Learning Design of the IEEE journal Educational Technology & Society.Learning design patterns assist the development of effective courses, because patterns capture successful solutions. Pedagogical patterns are commonly created by human cognitive processing in "writer's workshops". Inductive techniques could be used to detect or determine patterns in existing data, or learning designs. This assumes that the learning designs are available in a format that is machine interpretable. The IMS Learning Design specification enables the formal coding of learning designs. We explain that we expect patterns to occur in the method section of a learning design and in particular in acts. We explore several inductive techniques that could be applied to existing learning designs in order to detect and determine patterns and discuss how these could be applied to create new learning designs

Systematic adaptation of dynamically generated source code via domain-specific examples

In modern web-based applications, an increasing amount of source code is generated dynamically at runtime. Web applications commonly execute dynamically generated code (DGC) emitted by third-party, black-box generators, run at remote sites. Web developers often need to adapt DGC before it can be executed: embedded HTML can be vulnerable to cross-site scripting attacks; an API may be incompatible with some browsers; and the program\u27s state created by DGC may not be persisting. Lacking any systematic approaches for adapting DGC, web developers resort to ad-hoc techniques that are unsafe and error-prone. This study presents an approach for adapting DGC systematically that follows the program-transformation-byexample paradigm. The proposed approach provides predefined, domain-specific before/after examples that capture the variability of commonly used adaptations. By approving or rejecting these examples, web developers determine the required adaptation transformations, which are encoded in an adaptation script operating on the generated code\u27s abstract syntax tree. The proposed approach is a suite of practical JavaScript program adaptations and their corresponding before/after examples. The authors have successfully applied the approach to real web applications to adapt third-party generated JavaScript code for security, browser compatibility, and persistence