4,185 research outputs found
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 Framework for XML-based Integration of Data, Visualization and Analysis in a Biomedical Domain
Biomedical data are becoming increasingly complex and heterogeneous in nature. The data are stored in distributed information systems, using a variety of data models, and are processed by increasingly more complex tools that analyze and visualize them. We present in this paper our framework for integrating biomedical research data and tools into a unique Web front end. Our framework is applied to the University of Washington’s Human Brain Project. Specifically, we present solutions to four integration tasks: definition of complex mappings from relational sources to XML, distributed XQuery processing, generation of heterogeneous output formats, and the integration of heterogeneous data visualization and analysis tools
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Data integration through service-based mediation for web-enabled information systems
The Web and its underlying platform technologies have often been used to integrate existing software and information systems. Traditional techniques for data representation and transformations between documents are not sufficient to support a flexible and maintainable data integration solution that meets the requirements of modern complex Web-enabled software and information systems. The difficulty
arises from the high degree of complexity of data structures, for example in business and technology applications, and from the constant change of data and its
representation. In the Web context, where the Web platform is used to integrate different organisations or software systems, additionally the problem of heterogeneity
arises. We introduce a specific data integration solution for Web applications such as Web-enabled information systems. Our contribution is an integration technology
framework for Web-enabled information systems comprising, firstly, a data integration technique based on the declarative specification of transformation rules and the construction of connectors that handle the integration and, secondly, a mediator architecture based on information services and the constructed connectors to handle the integration process
Mediated data integration and transformation for web service-based software architectures
Service-oriented architecture using XML-based web services has been widely accepted by many organisations as the standard infrastructure to integrate heterogeneous and autonomous data sources. As a result, many Web service providers are built up on top of the data sources to share the data by supporting provided and required interfaces and methods of data access in a unified manner. In the context of data integration, problems arise when Web services are assembled to deliver an integrated view of data, adaptable to the specific needs of individual clients and providers. Traditional approaches of data integration and transformation are not suitable to automate the construction of connectors dedicated to connect selected Web services to render integrated and tailored views of data. We propose a declarative approach that addresses the oftenneglected data integration and adaptivity aspects of serviceoriented
architecture
R2O, an extensible and semantically based database-to-ontology mapping language
We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models
BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)
We describe a binding schema markup language (BSML) for describing data
interchange between scientific codes. Such a facility is an important
constituent of scientific problem solving environments (PSEs). BSML is designed
to integrate with a PSE or application composition system that views model
specification and execution as a problem of managing semistructured data. The
data interchange problem is addressed by three techniques for processing
semistructured data: validation, binding, and conversion. We present BSML and
describe its application to a PSE for wireless communications system design
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