Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structur...Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structured data are difficult to transform due to theirirregular structures. We design an efficient algorithm and data structure for ensuring losslesstransformation. We bring forward an approach of schema extraction through data mining, in whichdifferent kinds of elements are transformed respectively and lossless mapping from semi-structureddata to structured data can be achieved.展开更多
The Wireless Sensor network is distributed event based systems that differ from conventional communica-tion network. Sensor network has severe energy constraints, redundant low data rate, and many-to-one flows. Aggreg...The Wireless Sensor network is distributed event based systems that differ from conventional communica-tion network. Sensor network has severe energy constraints, redundant low data rate, and many-to-one flows. Aggregation is a technique to avoid redundant information to save energy and other resources. There are two types of aggregations. In one of the aggregation many sensor data are embedded into single packet, thus avoiding the unnecessary packet headers, this is called lossless aggregation. In the second case the sensor data goes under statistical process (average, maximum, minimum) and results are communicated to the base station, this is called lossy aggregation, because we cannot recover the original sensor data from the received aggregated packet. The number of sensor data to be aggregated in a single packet is known as degree of ag-gregation. The main contribution of this paper is to propose an algorithm which is adaptive to choose one of the aggregations based on scenarios and degree of aggregation based on traffic. We are also suggesting a suitable buffer management to offer best Quality of Service. Our initial experiment with NS-2 implementa-tion shows significant energy savings by reducing the number of packets optimally at any given moment of time.展开更多
文摘Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structured data are difficult to transform due to theirirregular structures. We design an efficient algorithm and data structure for ensuring losslesstransformation. We bring forward an approach of schema extraction through data mining, in whichdifferent kinds of elements are transformed respectively and lossless mapping from semi-structureddata to structured data can be achieved.
文摘The Wireless Sensor network is distributed event based systems that differ from conventional communica-tion network. Sensor network has severe energy constraints, redundant low data rate, and many-to-one flows. Aggregation is a technique to avoid redundant information to save energy and other resources. There are two types of aggregations. In one of the aggregation many sensor data are embedded into single packet, thus avoiding the unnecessary packet headers, this is called lossless aggregation. In the second case the sensor data goes under statistical process (average, maximum, minimum) and results are communicated to the base station, this is called lossy aggregation, because we cannot recover the original sensor data from the received aggregated packet. The number of sensor data to be aggregated in a single packet is known as degree of ag-gregation. The main contribution of this paper is to propose an algorithm which is adaptive to choose one of the aggregations based on scenarios and degree of aggregation based on traffic. We are also suggesting a suitable buffer management to offer best Quality of Service. Our initial experiment with NS-2 implementa-tion shows significant energy savings by reducing the number of packets optimally at any given moment of time.
