XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

A knowledge based approach to integration of products, processes and reconfigurable automation resources

The success of next generation automotive companies will depend upon their ability to adapt to ever changing market trends thus becoming highly responsive. In the automotive sector, the assembly line design and reconfiguration is an especially critical and extremely complex job. The current research addresses some of the aspects of this activity under the umbrella of a larger ongoing research project called Business Driven Automation (BDA) project. The BDA project aims to carry out complete virtual 3D modeling-based verifications of the assembly line for new or revised products in contrast to the prevalent practice of manual evaluation of effects of product change on physical resources. [Continues.

Matching methods for semantic interoperability in Product Lifecycle Management.

Product lifecycle management (PLM) is a business strategy that enables seamless information flow in today's collaborative, but distributed product development environment. In such environment, geographically and functionally distributed teams are involved in the development process, and the teams use different software systems with different ways of representing product data. As the product development process gets bigger and complicated, product semantics also needs to be translated in addition to the syntactic information, but ISO 10303, the current industry standard, cannot successfully translate the semantics; this has led to a new approach toward semantics-based product data integration. Semantics-based integration first requires participating domains to use semantic representation of product data. Given the semantic representations, it further requires techniques to determine semantic maps across product representations that will enable semantically correct interoperability of product data, and we propose the enabling techniques in this research. In order to determine semantic maps, we propose a method - Instance-Based Concept Matching (IBCM) that detects 1-to-n maps by exploiting implicit semantics captured in the instances of product models. The use of implicit semantics adds a new dimension in the area of product development, where most of the previous research has focused on using schema or data definition that are explicitly defined. Any single matching method is not enough to determine the semantic maps across the different systems, since each method presents only one view. We propose a method - FEedback Matching Framework with Implicit Training (FEMFIT) to combine the different matching approaches using ranking Support Vector Machine. The method overcomes the need to explicitly train the algorithm before it is used, and minimizes the decision-making load on the domain expert. Finally, we propose a framework to automatically determine the translation rules to enable translation of concepts from one system to another. Even after the semantic maps are obtained, the syntax in the sending system should properly transform to the syntax in the receiving system. We use a graph search method that obtains the overall translation rule as a combination of multiple basic functions. Using such rules, data from one system can be easily translated to another system.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64796/1/yeoil_1.pd

A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases

PhDData stored in individual autonomous databases often needs to be combined and interrelated. For example, in the Inland Water (IW) environment monitoring domain, the spatial and temporal variation of measurements of different water quality indicators stored in different databases are of interest. Data from multiple data sources is more complex to combine when there is a lack of metadata in a computation forin and when the syntax and semantics of the stored data models are heterogeneous. The main types of information retrieval (IR) requirements are query transparency and data harmonisation for data interoperability and support for multiple user views. A combined Semantic Web based and Agent based distributed system framework has been developed to support the above IR requirements. It has been implemented using the Jena ontology and JADE agent toolkits. The semantic part supports the interoperability of autonomous data sources by merging their intensional data, using a Global-As-View or GAV approach, into a global semantic model, represented in DAML+OIL and in OWL. This is used to mediate between different local database views. The agent part provides the semantic services to import, align and parse semantic metadata instances, to support data mediation and to reason about data mappings during alignment. The framework has applied to support information retrieval, interoperability and multi-lateral viewpoints for four European environmental agency databases. An extended GAV approach has been developed and applied to handle queries that can be reformulated over multiple user views of the stored data. This allows users to retrieve data in a conceptualisation that is better suited to them rather than to have to understand the entire detailed global view conceptualisation. User viewpoints are derived from the global ontology or existing viewpoints of it. This has the advantage that it reduces the number of potential conceptualisations and their associated mappings to be more computationally manageable. Whereas an ad hoc framework based upon conventional distributed programming language and a rule framework could be used to support user views and adaptation to user views, a more formal framework has the benefit in that it can support reasoning about the consistency, equivalence, containment and conflict resolution when traversing data models. A preliminary formulation of the formal model has been undertaken and is based upon extending a Datalog type algebra with hierarchical, attribute and instance value operators. These operators can be applied to support compositional mapping and consistency checking of data views. The multiple viewpoint system was implemented as a Java-based application consisting of two sub-systems, one for viewpoint adaptation and management, the other for query processing and query result adjustment

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Third international workshop on Authoring of adaptive and adaptable educational hypermedia (A3EH), Amsterdam, 18-22 July, 2005

The A3EH follows a successful series of workshops on Adaptive and Adaptable Educational Hypermedia. This workshop focuses on models, design and authoring of AEH, on assessment of AEH, conversion between AEH and evaluation of AEH. The workshop has paper presentations, poster session and panel discussions