A Declarative Approach for Designing Web Portals

2011 ◽  
pp. 197-203
Author(s):  
William Gardner ◽  
R. Rajugan
Keyword(s):  
Data Exchange ◽  
Data Representation ◽  
Content Management ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Distributed Model ◽  
Heterogeneous Data Sources ◽  
Extensible Markup ◽  
Management Techniques ◽  
Exchange Medium

As many enterprise and industrial content management techniques are moving towards a distributed model, the need to exchange data between heterogeneous data sources in a seamless fashion is constantly increasing. These heterogeneous data sources could arise from server groups from different manufacturers or databases at different sites with their own schemas. Since its introduction in 1996, eXtensible Markup Language (XML) (W3C-XML, 2004) has established itself as the open, presentation independent data representation and exchange medium. XML provides a mechanism for seamless data exchange in many industrial informatics settings. In addition, XML is also emerging as the dominant standard for storing, describing, representing, and interchanging data among various enterprises systems and databases in the context of complex Web enterprises information systems (EIS).

Web Retrieval of XML Documents

2011 ◽  
pp. 170-199
Author(s):  
Barbara Catania ◽  
Elena Ferrari
Keyword(s):  
Expressive Power ◽  
Data Representation ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Xml Data ◽  
Web Documents ◽  
Web Retrieval ◽  
Heterogeneous Data Sources ◽  
The Web

Web is characterized by a huge amount of very heterogeneous data sources, that differ both in media support and format representation. In this scenario, there is the need of an integrating approach for querying heterogeneous Web documents. To this purpose, XML can play an important role since it is becoming a standard for data representation and exchange over the Web. Due to its flexibility, XML is currently being used as an interface language over the Web, by which (part of) document sources are represented and exported. Under this assumption, the problem of querying heterogeneous sources can be reduced to the problem of querying XML data sources. In this chapter, we first survey the most relevant query languages for XML data proposed both by the scientific community and by standardization committees, e.g., W3C, mainly focusing on their expressive power. Then, we investigate how typical Information Retrieval concepts, such as ranking, similarity-based search, and profile-based search, can be applied to XML query languages. Commercial products based on the considered approaches are then briefly surveyed. Finally, we conclude the chapter by providing an overview of the most promising research trends in the fields.

Conceptual and Systematic Design Approach for XML Document Warehouses

2008 ◽  
pp. 485-508
Author(s):  
Vicky Nassis ◽  
R. Rajagopalapillai ◽  
Tharam S. Dillon ◽  
Wenny Rahayu
Keyword(s):  
Conceptual Design ◽  
Complex Structure ◽  
Heterogeneous Data ◽  
Xml Schema ◽  
Data Sources ◽  
Systematic Design ◽  
Simple Fact ◽  
Heterogeneous Data Sources ◽  
Xml Document ◽  
Extensible Markup

EXtensible Markup Language (XML) has emerged as the dominant standard in describing and exchanging data among heterogeneous data sources. The increasing presence of large volumes of data appearing creates the need to investigate XML Document Warehouses as a means of handling the data. In this paper our focus is twofold. First we utilise Object Oriented (OO) concepts to develop and propose a conceptual design formalism to build meaningful XML Document Warehouses (XDW). This includes: (1) XML (warehouse) repository (xFACT) using OO concepts followed by the transformation of XML Schema constructs and (2) Conceptual Virtual Dimensions (VDims) using Conceptual views (Rajugan, Chang, Dillon, & Feng, 2003, 2004). Secondly we address several important outstanding issues related to our proposed design of an XML Document Warehouse. Specifically we note that the xFACT portion is now a complex structure, involving several entities and relationships as opposed to being a simple FACT table as was the case in relational data warehouses, and the notion of Virtual Dimensions (VDims) has considerably greater complexity.

scholarly journals A multi-agent conversational system with heterogeneous data sources access

2016 ◽  
Vol 53 ◽  
pp. 172-191 ◽  
Author(s):  
Eduardo M. Eisman ◽  
María Navarro ◽  
Juan Luis Castro
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Multi Agent

scholarly journals Prediction of RNA subcellular localization: learning from heterogeneous data sources

iScience ◽  
2021 ◽  
pp. 103298
Author(s):  
Anca Flavia Savulescu ◽  
Emmanuel Bouilhol ◽  
Nicolas Beaume ◽  
Macha Nikolski
Keyword(s):  
Subcellular Localization ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals Learning from heterogeneous data sources: an application in spatial proteomics

2015 ◽  
Author(s):  
Lisa M. Breckels ◽  
Sean Holden ◽  
David Wojnar ◽  
Claire M. Mulvey ◽  
Andy Christoforou ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Support Vector Machine ◽  
Transfer Learning ◽  
High Throughput ◽  
Cell Biology ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Support Vector ◽  
Proteomics Data ◽  
Heterogeneous Data Sources

AbstractSub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.AbbreviationsLOPITLocalisation of Organelle Proteins by Isotope TaggingPCPProtein Correlation ProfilingMLMachine learningTLTransfer learningSVMSupport vector machinePCAPrincipal component analysisGOGene OntologyCCCellular compartmentiTRAQIsobaric tags for relative and absolute quantitationTMTTandem mass tagsMSMass spectrometry

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