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SNMP-based system for monitoring network is equipped with network management, monitoring system and statistic analysis of the network in it. It not only contains the function like network information capture that normal network devices have, but also has the ability to extend such applications as CPU usage rate, traffic flow information of interfaces and memory usage rate through plug-in mechanism. Simplifying the basic network management tasks by centering on auto-topology mechanism and making a tradeoff between usability and extensibility, the auto-topology control which is implemented and designed independently could support almost networking devices with better flexibility. Completely supporting to capture TrapV1, TrapV2 and partial TrapV3 (limited by protocol) with embedded database to facilitate backend storage, the software has become a green-software except depending on .NET Framework 4.
SNMP-based system for monitoring network is equipped with network management, monitoring system and statistic analysis of the network in it. It not only contains the function like network information capture that normal network devices have, but also has the ability to extend such applications as CPU usage rate, traffic flow information of interfaces and memory usage rate through plug-in mechanism. Simplifying the basic network management tasks by centering on auto-topology mechanism and making a tradeoff between usability and extensibility, the auto-topology control which is implemented and designed independently could support almost networking devices with better flexibility. Completely supporting to capture TrapV1, TrapV2 and partial TrapV3 (limited by protocol) with embedded database to facilitate backend storage, the software has become a green-software except depending on .NET Framework 4.
Ontologies have long been considered the core of semantics as they offer shareable and reusable knowledge about a particular domain. Improving and sharing domain specific knowledge residing in a database is one of the key challenges faced while developing any application. Due to the ever growing amount of data on the Web, it is almost impossible to extract meaningful data and the amount of manual work during creation of ontology poses series of challenges. And researchers are facing challenges such as the unavailability of well-formed databases, domain expert's help for extracting cardinality restrictions and generation of un-resolvable URIs. Therefore, we have focused on domain specific relational databases for constructing ontologies as a solution. Our aim is to analyze the various Ontology construction approaches from relational databases and identify the advantages and disadvantages of these techniques, so that an enhanced and efficient approach can be proposed. We have performed detailed analysis of various ontology construction techniques from relational database (RDB) based on database schema analysis (meta-data, cardinality restrictions and datatype information), stored data (through data mining) and also performed a comparative analysis of these techniques.
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