In the case of the TPI, each tuple toward (i.e., Inhibitors,Modulators,Libraries sampled record) is attached only to a time-point and tuple insertion is only performed when its value changes. These methods can help to store the incoming stream without any loss of information, while maintaining the history of the data in memory as long as possible, because only one tuple needs to be kept in memory when it duplicates the values acquired over some interval.Additionally, the spatial information about the sensors is kept resident in memory with the assistance of a fixed grid that identifies the sensors’ locations. This method is capable of reducing the cost of a join operation, due to the filtering out of the list of irrelevant sensors from the query range before making the join operation. Besides, the historical data stream evicted from memory is stored to disk.
Although the cost of disk access is high, this operation rarely occurs, because the stored data stream is already reduced Inhibitors,Modulators,Libraries in size while Inhibitors,Modulators,Libraries in memory.In our experiments, we compare the TSI and TPI methods with the Non-Temporal Insertion (NTI) method as a na?ve method which is used in most data stream systems and has no consideration of the time representation. Moreover, we evaluate the performance of these methods with the use of factors such Inhibitors,Modulators,Libraries as the number of tuples, average insert and query execution time, along with the number of sensor readings obtained from the incoming data stream. The results show that the proposed methods are better than the NTI method in terms of the data storage as well as the query execution time.
The remainder of this paper is organized as follows: AV-951 in Section 2, we briefly review related work. Section 3 introduces how the data stream pertaining to the spatial and temporal attributes is managed to tackle spatiotemporal queries. The design of the system architecture for the implementation of the proposed approach is introduced in Section 4. In Section 5, we present the system implementation and a running example illustrating a weather monitoring. selleckbio The performance evaluation and analysis are presented in Section 6. Finally, we conclude this paper and describe the directions of our future work in Section 7.2.?Related WorkBefore any system is designed and installed, a detailed understanding of its physical environment and deployment is required. Design of environmental sensor networks has been approached by many researchers. Research areas including sensing, communication and computing have been examined extensively [8]. Work in [7] designed a WSN for habitat monitoring. The requirements of environmental monitoring in the context of two wildlife habitats: Great Duck Island and James Reserve were examined first.