Agent-Based Service-Oriented Architecture for Heterogeneous Data Sources Management in Ubiquitous Enterprise

2013 ◽  
pp. 367-378
Author(s):  
L. Y. Pang ◽  
Ray Y. Zhong ◽  
George Q. Huang
Keyword(s):  
Service Oriented Architecture ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Agent Based ◽  
Heterogeneous Data Sources ◽  
Service Oriented

A Middleware Architecture for Secure Service Discovery using Ontologies with Multiagent Approach

2012 ◽  
Vol 3 (1) ◽  
pp. 47-55
Author(s):  
Arup Sarkar ◽  
Ujjal Marjit ◽  
Utpal Biswas
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Service Oriented Architecture ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Security Measures ◽  
Learning Capability ◽  
Heterogeneous Data Sources ◽  
Service Oriented ◽  
The Rich

Web is a place for information sharing as well as service providing. With the addition of Service Oriented Architecture ensures better reusability, maintainability and flexibility among the heterogeneous data sources. Possibility of a better interoperability within such a heterogeneous data sources is less without further assistance. For better service discovery, these issues must be cleared first. Besides this, security measures also play a key role. By developing a Multiagent based middleware system can resolve all these issues. Further it will add up better communication among the different modules of the system as well as the self learning capability. This paper’s approach is aimed to the development of Multiagent system based middleware architecture for better service discovery, selection and invocation through a secure way without replacing the existing services based on Web Service and Semantic Web Service technologies. The architecture will use ontologies heavily to introduce the rich semantics to the services to provide better meaning understandable by machines.

Research on Knowledge Service Oriented Expert Management System

2014 ◽  
Vol 519-520 ◽  
pp. 1568-1571
Author(s):  
Su Yan Wu ◽  
Wen Bo Li
Keyword(s):  
Management System ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Access To Information ◽  
Retrieval Method ◽  
Knowledge Service ◽  
On Demand ◽  
Heterogeneous Data Sources ◽  
Service Oriented ◽  
Demand Service

The distinctive characteristics of knowledge service oriented expert management system needs to have instant, on-demand service, accurate service, comprehensive services, and personalized service. In order to meet the requirements of the establishment of this system, this paper studied the construction of expert resources and expert retrieval services , and The crowdsourcing technology-based open expert body build, experts of different heterogeneous data sources access to information based expert body interaction semantics expert retrieval method

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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