David Kensche

GeRoMe : a generic role based metamodel for model management

The goal of Model Management is the development of new technologies and mechanisms to support the integration, evolution and matching of models. Such tasks are to be performed by means of a set of model management operators which work on models and their elements, without being restricted to a particular metamodel (e.g. the relational or UML metamodel). We propose that generic model management should employ a generic metamodel (GMM) which serves as an abstraction of the features of particular metamodels while preserving the semantics of its different elements. A naive generalization of the elements of concrete metamodels in generic metaclasses would loose some of the specific features of the metamodels, or yield a prohibitive number of metaclasses in the GMM. To avoid these problems, we propose the Generic Role Based MetamodelGeRoMe in which each model element is decorated with a set of role objects that represent specific properties of the model element. Roles may be added to or removed from elements at any time, which enables a very flexible and dynamic yet accurate definition of models. Roles constitute to operators different views on the same model element. Thus, operators concentrate on features which affect their functionality but may remain agnostic about other features. Consequently, these operators can use polymorphism and have to be implemented only once using GeRoMe, and not for each specific metamodel. We verified our results by implementing GeRoMe and a selection of model management operators using our metadata system ConceptBase.

Generic schema merging

Schema merging is the process of integrating several schemas into a common, unified schema. There have been various approaches to schema merging, focusing on particular modeling languages, or using a lightweight, abstract metamodel. Having a semantically rich representation of models and mappings is particularly important for merging as semantic information is required to resolve the conflicts encountered. Therefore, our approach to schema merging is based on the generic role-based metamodel GeRoMe and intensional mappings based on the real world states of model elements. We give a formal definition of the merged schema and present an algorithm implementing these formalizations.

Mobile Web Services for Collaborative Learning

Since learning nowadays is conceptualized as a social system within communities of practice, the best way to learn is with others, in groups. In the past few years, there has been an increasing focus on social software applications as a result of the rapid development of new web technologies. Furthermore, mobile and ubiquitous technologies have provided capabilities for more sophisticated open social systems, where mobile knowledge sharing is the norm. In this paper, we explore the use of these concepts for learning and present a smart phone driven mobile Web Services architecture for collaborative learning.

Generic schema mappings for composition and query answering

In this article, we present extensional mappings, that are based on second-order tuple generating dependencies between models in our Generic Role-based Metamodel GeRoMe. Our mappings support data translation between heterogeneous models, such as XML schemas, relational schemas, or OWL ontologies. The mapping language provides grouping functionalities that allow for complete restructuring of data, which is necessary for handling object oriented models and nested data structures such as XML. Furthermore, we present algorithms for mapping composition and optimization of the composition result. To verify the genericness, correctness, and composability of our approach we implemented a data translation tool and mapping export for several data manipulation languages. Furthermore, we address the question how generic schema mappings can be harnessed for answering queries against an integrated global schema.

Influence of contact pressure and moisture on the signal quality of a newly developed textile ECG sensor shirt

A newly developed textile integrated sensor shirt, called ldquoITcaresrdquo (Intelligent Textile for CArdio REspiratory Sensing), is presented. Textile integrated ECG sensors are known to be highly depended on the electrode-skin-impedance. Two main influence factors on the skin-electrode impedance are: 1. contact pressure and 2. moisture. Systematic measurements were performed with additional sensors to evaluate the ECG signal quality. Furthermore, signal-to-noise ratios were calculated as a quantitative measure.

Matching of ontologies with XML schemas using a generic metamodel

Schema matching is the task of automatically computing correspondences between schema elements. A multitude of schema matching approaches exists for various scenarios using syntactic, semantic, or instance information. The schema matching problem is aggravated by the fact that models to be matched are often represented in different modeling languages, e.g. OWL, XML Schema, or SQL DDL. Consequently, besides being able to match models in the same metamodel, a schema matching tool must be able to compute reasonable results when matching models in heterogeneous modeling languages. Therefore, we developed a matching component as a part of our model management system GeRoMeSuite which is based on our generic metamodel GeRoMe. As GeRoMe provides a unified representation of models, the matcher is able to match models represented in different languages with each other. In this paper, we will show in particular the results for matching XML Schemas with OWL ontologies as it is often required for the semantic annotation of existing XML data sources. GeRoMeSuite allows for flexible configuration of the matching system; various matching algorithms for element and structure level matching are provided and can be combined freely using different ways of aggregation and filtering in order to define new matching strategies. This makes the matcher highly configurable and extensible. We evaluated our system with several pairs of XML Schemas and OWL ontologies and compared the performance with results from other systems. The results are considerably better which shows that a matching system based on a generic metamodel is favorable for heterogeneous matching tasks.

Automatic selection of background knowledge for ontology matching

Background knowledge in form of ontologies is an important source of information for many tasks in the semantic web, e.g., ontology matching, ontology construction and editing, natural language processing. In particular, ontology matching and integration can benefit from background ontologies as semantic relationships may be discovered which cannot be identified otherwise. In existing approaches, the background ontology has to be provided often by the user. Therefore, we present an approach that uses background knowledge for matching; but in contrast to other approaches, our approach is able to identify appropriate background ontologies automatically. We implemented this approach in our matching frame-work GeRoMeSuite and tested it with several data sets from the Ontology Alignment Evaluation Initiative (OAEI) campaign. The evaluation shows that the use of background knowledge improves the result in most cases and that our ontology discovery process is able to find appropriate background knowledge to bridge the gap between the two input ontologies.

Generic schema mappings

Schema mappings come in different flavors: simple correspondences are produced by schema matchers, intensional mappings are used for schema integration. However, the execution of mappings requires a formalization based on the extensional semantics of models. This problem is aggravated if multiple metamodels are involved. In this paper we present extensional mappings, that are based on second order tuple generating dependencies, between models in our Generic Role-based Metamodel GeRoMe. By using a generic metamodel, our mappings support data translation between heterogeneous metamodels. Our mapping representation provides grouping functionalities that allow for complete restructuring of data, which is necessary for handling nested data structures such as XML and object oriented models. Furthermore, we present an algorithm for mapping composition and optimization of the composition result. To verify the genericness, correctness, and composability of our approach we implemented a data translation tool and mapping export for several data manipulation languages.

LM-DTM: an environment for XML-based, LIP/PAPI-compliant deployment, transformation and matching of learner models

Our shared belief is that learning, like other human activities, cannot and is not confined within rigidly defined course systems or learning repositories, inclosing learning resources which cannot be tailored to the different learners needs, skills, interests, preferences, goals, etc. Therefore, a learning environment, besides supporting communication between knowledge providers and consumers, has to be organized in a flexible manner based on different learner profiles. Learner modeling has become a highly challenging task to provide personalized, adaptive and context-based learning. The work presented in this paper addresses this issue by providing a meta-level solution for the description, transformation and matching of learner models, based on standards such as IMS LIP, IEEE PAPI, XML to foster the reuse and exchange of learner models between learning platforms, both by universities and corporations.

Mobile Mining and Information Management in HealthNet Scenarios

Health and mobility of elderly people is gaining importance in aging societies. New communication-based methods to provide health services with personal health care devices are considered promising elements of first-class medical care services for everybody. To achieve this vision, several technological issues have to be solved: (i) body sensors to monitor vital functions have to be developed; (ii) these sensors should be integrated into textile structures to guarantee ease of use and patient acceptance; (iii) the collected sensor data has to be analyzed to detect emergency situations and to reduce the data volume; (iv) relevant data has to be integrated with other information systems in the work environment of medical experts. These challenges are addressed within the HealthNet project at RWTH Aachen University. The goal of the project is to develop a framework in which health professionals can remotely monitor and diagnose mobile patients. The described demonstration presents our results of the first three issues mentioned above while focussing on the employed data mining and management techniques.