5/7/2023 0 Comments FirestreamWe turn data-compressing filters into group-aware filters by exploiting two overlooked, yet important, properties of monitoring applications: 1 many of them can tolerate some degree of 'slack' in their data quality requirements 2 there may exist multiple subsets of the source data satisfying the quality needs of an application. For bandwidth efficiency, we propose a collaborative data-reduction mechanism, 'group-aware stream filtering', used together with multicast, to select a small set of necessary data that satisfy the needs of a group of subscribers simultaneously. We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. We also classify the intelligent techniques outlined in the researches for each category. We could classify researches to four categories: fire detectors, reduce false alarms systems, fire data analysis and fire predictors. In this paper the automatic fire detection researches using intelligent techniques from 2000 to 2010 is reviewed. In recent years researches have been studying technical developments in this field aimed at exploiting wireless communications networks, detection systems and fire prediction systems design. There are many studies that have considered appropriate techniques for early fire detection. Automatic fire detection has attracted increased attention due to its importance in decreasing fire damage. There are two perspectives in fire detection: fire detection in forests or jungles and fire detection in occupied or residential areas. Our experimental study demonstrates the signiflcant performance improvements compared to the state-of-the-art generic distributed stream join algorithms.Īutomatic fire detection system is a system that is capable of assessing environmental factors and their effects on the environment as well as predicting the occurrence of fire in the early stages and even before the outbreak. We have implemented the proposed PSP schemes within the CAPE DSMS. Compared to replication- based distribution of non-equi-joins, PSP scheme is supe- rior since: (1) zero state duplication and thus no repeated computations, (2) pipelined processing of every input tu- ple on multiple nodes to achieve low response time, and (3) cost-based adaptive workload distribution. The PSP scheme par- titions the states into disjoint slices in the time domain, and then distributes the flne-grained states in the cluster, form- ing a virtual computation ring. We target generic joins with arbitrarily join conditions, which are used in non-trivial stream applications such as image matching and biometric recognizing. This paper proposes a novel scheme for distributed processing of generic multi-way joins with win- dow constraints, called Pipelined State Partitioning (PSP). Given the memory- and CPU-intensive nature of such stream join queries, scalable processing on a cluster must be employed. Multi-way stream joins with expensive join predicates lead to great challenge for real-time (or close to real-time) stream processing. Our experimental results demonstrate a linear throughput scaling with respect to the numbers of streams and processing cores. Finally, we present a parallelized version of our multiway stream join by integrating our proposed pipelines into a parallel unidirectional flow-based architecture ( If an overwrite is detected in the stash, our design automatically resorts to recomputing intermediate results. ) that uses a best-effort buffering technique (referred to as stash) to maintain intermediate results. We also present a novel two-stage pipeline stream join ( In this circular design, each new tuple (given its origin stream) starts its processing from a specific join core and passes through all respective join cores in a pipeline sequence to produce the final results. ) in hardware to orchestrate a multiway join while minimizing data flow disruption. We propose a scalable circular pipeline design ( In this paper, we focus primarily on accelerating stream joins, which are arguably one of the most commonly used and resource-intensive operators in stream processing. Efficient real-time analytics are an integral part of an increasing number of data management applications, such as computational targeted advertising, algorithmic trading, and Internet of Things.
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