11,948 research outputs found
Interoperability of Information Systems and Heterogenous Databases Using XML
Interoperabilily of information systerrrs is the most critical issue facing businesse!
that need to access information from multiple idormution systems on
tlifferent environments ancl diverse platforms. Interoperability has been a basic
requirement for the modern information systems in a competitive and volatile
business environment, particularly with the advent of distributed network system
and the growing relevance of inter-network communications. Our objective
in tltis paper is to develop a comprehensiveframework tofacilitate interoperability
smong distributed and heterogeneous information systems and to develop prototype
software to validate tlte application of XML in interoperability of infurmation
systems and databases
Cross-concordances: terminology mapping and its effectiveness for information retrieval
The German Federal Ministry for Education and Research funded a major
terminology mapping initiative, which found its conclusion in 2007. The task of
this terminology mapping initiative was to organize, create and manage
'cross-concordances' between controlled vocabularies (thesauri, classification
systems, subject heading lists) centred around the social sciences but quickly
extending to other subject areas. 64 crosswalks with more than 500,000
relations were established. In the final phase of the project, a major
evaluation effort to test and measure the effectiveness of the vocabulary
mappings in an information system environment was conducted. The paper reports
on the cross-concordance work and evaluation results.Comment: 19 pages, 4 figures, 11 tables, IFLA conference 200
Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.
Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu
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
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