Preselection of Electronic Services by Given Business Service Based on Measuring Semantic Heterogeneity within the Application Area of Logistics

According to the service orientation design paradigm there are business (BS) and electronic services (ES). BS encapsulate business concerns. ES encapsulate computing systems, information systems and software applications. In environments with a high number of BS and ES the decision on which ES provides the most suitable support for a certain BS is not a trivial task. The objective of the thesis is to provide models, methods, and techniques for preselection of ES for a given BS. Preselection is about reducing the large amount of ES to a significant smaller amount under the consideration of a particular BS

A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers

Change Management for Distributed Ontologies

Akkermans, J.M. [Promotor]Schreiber, A.T. [Promotor

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Advanced languages and techniques for trust negotiation.

The Web is quickly shifting from a document browsing and delivery system to a hugely complex ecosystem of interconnected online applications. A relevant portion of these applications dramatically increase the number of users required to dynamically authenticate themselves and to, on the other hand, to identify the service they want to use. In order to manage interactions among such users/services is required a flexible but powerful mechanism. Trust management, and in particular trust negotiation techniques, is a reasonable solution. In this work we present the formalization of the well known trust negotiation framework Trust-X, of a rule-based policy definition language, called X-RNL. Moreover, we present the extension of both the framework and of the language to provide advanced trust negotiation architectures, namely negotiations among groups. We also provide protocols to adapt trust negotiations to mobile environments, specifically, we present protocols allowing a trust negotiation to be executed among several, distinct, sessions while still preserving its security properties. Such protocols have also been extended to provides the capability to migrate a ongoing trust negotiation among a set of known, reliable, subjects. Finally, we present the application of the previously introduced trust negotiation techniques into real world scenarios: online social networks, critical infrastructures and cognitive radio networks

Integration of distributed terminology resources to facilitate subject cross-browsing for library portal systems

With the increase in the number of distributed library information resources, users may have to interact with different user interfaces, learn to switch their mental models between these interfaces, and familiarise themselves with controlled vocabularies used by different resources. For this reason, library professionals have developed library portals to integrate these distributed information resources, and assist end-users in cross-accessing distributed resources via a single access point in their own library. There are two important subject-based services that a library portal system might be able to provide. The first is a federated search service, which refers to a process where a user can input a query to cross-search a number of information resources. The second is a subject cross-browsing service, which can offer a knowledge navigation tree to link subject schemes used by distributed resources. However, the development of subject cross-searching and browsing services has been impeded by the heterogeneity of different KOS (Knowledge Organisation System) used by different information resources. Due to the lack of mappings between different KOS, it is impossible to offer a subject cross-browsing service for a library portal system. [Continues.

Data quality issues in electronic health records for large-scale databases

Data Quality (DQ) in Electronic Health Records (EHRs) is one of the core functions that play a decisive role to improve the healthcare service quality. The DQ issues in EHRs are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards in Large-Scale Databases (LSDB) management systems. Therefore, large data communications are challenging in the traditional approaches to satisfy the needs of the consumers, as data is often not capture directly into the Database Management Systems (DBMS) in a seasonably enough fashion to enable their subsequent uses. In addition, large data plays a vital role in containing plenty of treasures for all the fields in the DBMS. EHRs technology provides portfolio management systems that allow HealthCare Organisations (HCOs) to deliver a higher quality of care to their patients than that which is possible with paper-based records. EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. Efficient EHR systems reduce the data redundancy as well as the system application failure and increase the possibility to draw all necessary reports. However, one of the main challenges in developing efficient EHR systems is the inherent difficulty to coherently manage data from diverse heterogeneous sources. It is practically challenging to integrate diverse data into a global schema, which satisfies the need of users. The efficient management of EHR systems using an existing DBMS present challenges because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every data integration problem. The challenges of the DQ issue raised the need to find an efficient way to integrate large EHRs from diverse heterogeneous sources. To handle and align a large dataset efficiently, the hybrid algorithm method with the logical combination of Fuzzy-Ontology along with a large-scale EHRs analysis platform has shown the results in term of improved accuracy. This study investigated and addressed the raised DQ issues to interventions to overcome these barriers and challenges, including the provision of EHRs as they pertain to DQ and has combined features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets. The study researched the design of a hybrid method based on Fuzzy-Ontology with performed mathematical simulations based on the Markov Chain Probability Model. The similarity measurement based on dynamic Hungarian algorithm was followed by the Design Science Research (DSR) methodology, which will increase the quality of service over HCOs in adaptive frameworks

Structural Graph-based Metamodel Matching

Data integration has been, and still is, a challenge for applications processing multiple heterogeneous data sources. Across the domains of schemas, ontologies, and metamodels, this imposes the need for mapping specifications, i.e. the task of discovering semantic correspondences between elements. Support for the development of such mappings has been researched, producing matching systems that automatically propose mapping suggestions. However, especially in the context of metamodel matching the result quality of state of the art matching techniques leaves room for improvement. Although the traditional approach of pair-wise element comparison works on smaller data sets, its quadratic complexity leads to poor runtime and memory performance and eventually to the inability to match, when applied on real-world data. The work presented in this thesis seeks to address these shortcomings. Thereby, we take advantage of the graph structure of metamodels. Consequently, we derive a planar graph edit distance as metamodel similarity metric and mining-based matching to make use of redundant information. We also propose a planar graph-based partitioning to cope with large-scale matching. These techniques are then evaluated using real-world mappings from SAP business integration scenarios and the MDA community. The results demonstrate improvement in quality and managed runtime and memory consumption for large-scale metamodel matching

Discourse and Digital Practices

Discourse and Digital Practices shows how tools from discourse analysis can be used to help us understand new communication practices associated with digital media, from video gaming and social networking to apps and photo sharing. This cutting-edge book: draws together fourteen eminent scholars in the field including James Paul Gee, David Barton, Ilana Snyder, Phil Benson, Victoria Carrington, Guy Merchant, Camilla Vasquez, Neil Selwyn and Rodney Jones answers the central question: "How does discourse analysis enable us to understand digital practices?" addresses a different type of digital media in each chapter demonstrates how digital practices and the associated new technologies challenge discourse analysts to adapt traditional analytic tools and formulate new theories and methodologies examines digital practices from a wide variety of approaches including textual analysis, conversation analysis, interactional sociolinguistics, multimodal discourse analysis, object ethnography, geosemiotics, and critical discourse analysis. Discourse and Digital Practices will be of interest to advanced students studying courses on digital literacies or language and digital practices