A Multiagent-based Framework for Integrating Biological Data

Biological data has been rapidly increasing in volume in different Web data sources. To query multiple data sources manually on the internet is time consuming for biologists. Therefore, systems and tools that facilitate searching multiple biological data sources are needed. Traditional approaches to build distributed or federated systems do not scale well to the large, diverse, and the growing number of biological data sources. Internet search engines allow users to search through large numbers of data sources, but provide very limited capabilities for locating, combining, processing, and organizing information. A promising approach to this problem is to provide access to the large number of biological data sources through a multiagent-based framework where a set of agents can cooperate with each other to retrieve relevant information from different biological Web databases. The proposed system uses a mediator-based integration approach with domain ontology, which uses as a global schema. In this paper we propose a multiagent-based framework that responds to biological queries according to its biological domain ontology.

[1]  Ravi Bhushan Mishra,et al.  Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods , 2010, Int. J. Intell. Inf. Technol..

[2]  Wil M. P. van der Aalst,et al.  Workflow Patterns , 2004, Distributed and Parallel Databases.

[3]  Julian R. Ullmann,et al.  An Algorithm for Subgraph Isomorphism , 1976, J. ACM.

[4]  Mike Bennett,et al.  Meaning Makers: User Generated Ambient Presence , 2009, Int. J. Ambient Comput. Intell..

[5]  Yolanda Gil,et al.  From data to knowledge to discoveries: Artificial intelligence and scientific workflows , 2009, Sci. Program..

[6]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[7]  Amit P. Sheth,et al.  Exception Handling for Conflict Resolution in Cross-Organizational Workflows , 2003, Distributed and Parallel Databases.

[8]  V. Sugumaran The Inaugural Issue of the International Journal of Intelligent Information Technologies , 2005 .

[9]  Mathias Weske,et al.  Case handling: a new paradigm for business process support , 2005, Data Knowl. Eng..

[10]  J. Leon Zhao,et al.  A case-based reasoning framework for workflow model management , 2004, Data Knowl. Eng..

[11]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[12]  Manuel Kolp,et al.  Engineering Software Systems with Social-Driven Templates , 2010 .

[13]  Alejandro Pazos Sierra,et al.  Encyclopedia of Artificial Intelligence , 2008 .

[14]  Vijayan Sugumaran,et al.  Methodological Advancements in Intelligent Information Technologies: Evolutionary Trends , 2009 .

[15]  Alexander Tartakovski,et al.  Agile Workflow Technology and Case-Based Change Reuse for Long-Term Processes , 2008, Int. J. Intell. Inf. Technol..

[16]  Paul Erdös,et al.  Random Graph Isomorphism , 1980, SIAM J. Comput..

[17]  Jian Tang,et al.  Consulting past exceptions to facilitate workflow exception handling , 2004, Decis. Support Syst..

[18]  Stefanie Rinderle-Ma,et al.  Change patterns and change support features - Enhancing flexibility in process-aware information systems , 2008, Data Knowl. Eng..

[19]  Boudewijn F. van Dongen,et al.  Workflow mining: A survey of issues and approaches , 2003, Data Knowl. Eng..

[20]  Mikael Wiberg,et al.  Interaction Per Se: Understanding "The Ambience of Interaction" as Manifested and Situated in Everyday & Ubiquitous IT-Use , 2010, Int. J. Ambient Comput. Intell..

[21]  Chris D. Nugent,et al.  Smart Home Research: Projects and Issues , 2009, Int. J. Ambient Comput. Intell..

[22]  Fabio Casati,et al.  Workflow Evolution , 1996, ER.

[23]  Marius Mikalsen,et al.  Context: Representation and Reasoning. Representing and Reasoning about Context in a Mobile Environment , 2005, Rev. d'Intelligence Artif..