Techniques for mining information from distributed data sources accessible over the Internet are a growing area of research.The mobile Agent paradigm opens a new door for distributed data mining and knowledge discover...Techniques for mining information from distributed data sources accessible over the Internet are a growing area of research.The mobile Agent paradigm opens a new door for distributed data mining and knowledge discovery applications.In this paper we present the design of a mobile agent system which couples service discovery,using a logical language based application programming interface,and database access.Combining mobility with database access provides a means to create more efficient data mining applications.The processing of data is moved to network wide data locations instead of the traditional approach of bringing huge amount of data to the processing location.Our proposal aims at implementing system tools that will enable intelligent mobile Agents to roam the Internet searching for distributed data services.Agents access the data,discover patterns,extract useful information from facts recorded in the databases,then communicate local results back to the user.The user then generates a global data model through the aggregation of results provided by all Agents.This overcomes barriers posed by network congestion,poor security,and unreliability.展开更多
With the explosion of digital data,the need for advanced visual analytics,including coordinated multiple views(CMV),is rapidly increasing.CMV enable users to discover patterns and examine relationships across multiple...With the explosion of digital data,the need for advanced visual analytics,including coordinated multiple views(CMV),is rapidly increasing.CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets.CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network(AURIN)project.AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia.Visual exploration of these datasets is essential to support research endeavors.This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV.We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations.Using the concept of data cubes and hierarchical dimensions,we present strategies to automatically build render groups.This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion.The CMV approach is demonstrated using aggregate-level data,which is provided through federated data services.The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.展开更多
文摘Techniques for mining information from distributed data sources accessible over the Internet are a growing area of research.The mobile Agent paradigm opens a new door for distributed data mining and knowledge discovery applications.In this paper we present the design of a mobile agent system which couples service discovery,using a logical language based application programming interface,and database access.Combining mobility with database access provides a means to create more efficient data mining applications.The processing of data is moved to network wide data locations instead of the traditional approach of bringing huge amount of data to the processing location.Our proposal aims at implementing system tools that will enable intelligent mobile Agents to roam the Internet searching for distributed data services.Agents access the data,discover patterns,extract useful information from facts recorded in the databases,then communicate local results back to the user.The user then generates a global data model through the aggregation of results provided by all Agents.This overcomes barriers posed by network congestion,poor security,and unreliability.
文摘With the explosion of digital data,the need for advanced visual analytics,including coordinated multiple views(CMV),is rapidly increasing.CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets.CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network(AURIN)project.AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia.Visual exploration of these datasets is essential to support research endeavors.This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV.We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations.Using the concept of data cubes and hierarchical dimensions,we present strategies to automatically build render groups.This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion.The CMV approach is demonstrated using aggregate-level data,which is provided through federated data services.The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.
