UML for the Semantic Web: Transformation-Based Approaches

The perspective role of UML as a conceptual modelling language for the Semantic Web has become an important research topic. We argue that UML could be a key technology for overcoming the ontology development bottleneck thanks to its wide acceptance and sophisticated tool support. Transformational approaches are a promising way of establishing a connection between UML and web-based ontology languages. We compare some proposals for defining transformations between UML and web ontology languages and discuss the different ways they handle the conceptual differences between these languages. We identify commonalities and differences of the approaches and point out open questions that have not or not satisfyingly been addressed by existing approaches

Context aware guidance for multimedia authoring: harmonizing domain and discourse knowledge

This paper presents an approach to assist authors during the authoring of multimedia presentations. We extend existing authoring support by integrating processes of topic identification, content collection and discourse structure building in a single environment. This integration allows identification of the context of the authoring process. Our approach combines this process context awareness with explicit domain and discourse knowledge to steer system suggestions. We evaluate our approach with an experimental system prototyp

INS Information Systems

CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics. CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below are the names of the clusters and in parentheses their acronyms

INformation Systems Composing discourse based on genre semantics

CWI is the National Research Institute for Mathematics and Computer Science. It is sponsored by th

Distributed Processing System with Shared Pool and its Application in Genetic Algorithm

[[abstract]]由於分散式網路運算的興起，大幅減少對生產分析與規劃所需時間。尤其對於流程龐雜致評估費時，且需同時滿足多重目標下，透過龐大網路資源，可於有限時效內獲得所需結果。其中，若能進一步善用企業現有資源，改善諸如演化演算法於分散運算上的效益，將可擴大在生產分析問題上的應用層面。本研究提出分享池與負載平衡方法，以改善MapReduce為核心的Hadoop架構的分散運算效益。在節點運算上，分享池可作為加快演化運算的共享來源，再結合田口方法可獲得包含分享比例、時機、與機率等之最佳分享參數組合。在負載平衡上，藉由各實體資源的動態分析，如CPU、Disk存取、與Network等即時負載的彙整，可動態決定最適節點數的啟用與管理，而達到有限計算資源的最適利用。在研究結果上，當應用分散式演化計算以獲得最佳訂單配適的生產分析為例，應用Hadoop架構，在3個資源數下，可較單一資源縮短約63.9%時間。若加上利用所提分享池與動態配置法，則在相同資源數下，可進一步縮短工作時間56.2%。此外，在相同資源數下，適當增加4個節點數，更可減少67.2%的工作時間。[[abstract]]With rising of distributed computing, time for production analysis and planning has been significantly reduced. By using enormous network resources, evaluation results for complicated processes with multiple objectives can be derived in time limits. If the existing resource can be advanced utilized to improve distributed computing performances of evolution algorithm, the applications of production analysis can be expanded.This study proposes a shared pool and load balancing scheme to improve computing performance of a distributed architecture, whose kernel adopts a MapReduce-based Hadoop framework. For nodes computation, the shared pool serves as a sharing source for accelerating evolution computing; moreover, optimal sharing parameters, i.e., sharing ratio, timing, and probability, are derived by using the Taguchi method. For load balancing, invocation and management of the optimal nodes are dynamically decided by integrating of real-time loadings of physical resources such as CPU, disk I/O, and network, such that the limited computing resources can be optimally utilized.In results, when applying a distributed evolution-based algorithm to find the optimal purchase order in Hadoop framework, the processing time by using three nodes is reduced 63.9% than that of one node; moreover, the processing time can be reduced 56.2% when adopting the proposed shared pool and load balancing scheme in same resources. Meanwhile, the processing time can be decreased 67.2% by adding four nodes in the same resources