FireStream: Sensor Stream Processing for Monitoring Fire Spread

Raghavan, Venkatesh; Rundensteiner, Elke A.; Woycheese, John P.; Mukherji, Abhishek

doi:10.1109/icde.2007.369056

Cited by 10 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the characteristics of source data fundamentally determine the performances of the filtering. We summarise results for three other data sets: a cow's orientation changes (Schwager et al, 2007), seismic readings for the volcano (Werner-Allen et al, 2006) and the HRR(Q) reading changes in a fire experiment (Raghavan et al, 2007) respectively. The data sets' key values behave differently, and their plots are distinctive in the shape.…”

Section: Summary Of Resultsmentioning

confidence: 99%

Towards collaborative data reduction in stream-processing systems

Kotz

2009

IJCNDS

View full text Add to dashboard Cite

We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. 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 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. We can thus choose the 'best alternative' subset for each application to maximise the data overlap within the group to best benefit from multicasting. We provide a general framework that treats the group-aware stream filtering problem completely; we prove the problem NP-hard and thus provide a suite of heuristic algorithms that ensure data quality (specifically, granularity and timeliness) while collaboratively reducing data. The framework is extensible and supports a diverse range of filters. Our prototype-based evaluation shows that group-aware stream filtering is effective in trading CPU time for data reduction, compared with self-interested filtering.

show abstract

Section: Summary Of Resultsmentioning

confidence: 99%

Towards collaborative data reduction in stream-processing systems

Kotz

2009

IJCNDS

View full text Add to dashboard Cite

show abstract

“…The FireStream system [Raghavan et al, 2007], for instance, designed to monitor the spread of fire, maintains three data libraries: a Spatial Store, to record structural elements and sensor locations required for spatial analysis, a Sensor Store, a collection of metadata pertinent to sensors such as thresholds and calibrations, and a Phenomenon Repository, a set of event patterns representing different classes of fire extracted from analysis of real fire datasets. The FireStream system [Raghavan et al, 2007], for instance, designed to monitor the spread of fire, maintains three data libraries: a Spatial Store, to record structural elements and sensor locations required for spatial analysis, a Sensor Store, a collection of metadata pertinent to sensors such as thresholds and calibrations, and a Phenomenon Repository, a set of event patterns representing different classes of fire extracted from analysis of real fire datasets.…”

Section: Events and Multimedia Information Systemsmentioning

confidence: 99%

Managing Event Information: Modeling, Retrieval, and Applications

Gupta¹,

Jain²

2011

Synthesis Lectures on Data Management

View full text Add to dashboard Cite

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means-electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Managing Event Information: Modeling, Retrieval, and Applications ABSTRACTWith the proliferation of citizen reporting, smart mobile devices, and social media, an increasing number of people are beginning generate information about events they observe and participate in. A significant fraction of this information contain multimedia data to share the experience with their audience. A systematic information modeling and management framework is necessary to capture this widely heterogeneous, schemaless, potentially humongous information produced by many different people. This book is an attempt to examine the modeling, storage, querying, and applications of such an event management system in a holistic manner. It uses a semantic-web style graph-based view of events, and shows how this event model, together with its query facility, can be used toward emerging applications like semi-automated storytelling.

show abstract

“…To copy otherwise, or to republish, to post on servers or to redistribute to lists, requires a fee and/or special permissions from the publisher, ACM. multiple stream tuples in scientific and engineering domains [3,10,13,15]. For example, environmental monitoring systems use streams from sensors for possibly complex pattern matching methodologies [3,15].…”

Section: Introductionmentioning

confidence: 99%

“…multiple stream tuples in scientific and engineering domains [3,10,13,15]. For example, environmental monitoring systems use streams from sensors for possibly complex pattern matching methodologies [3,15]. Network monitoring systems use deep packet inspection queries to evaluate network traffic flows with content-based analysis methods [13].…”

Section: Introductionmentioning

confidence: 99%

Scalable stream join processing with expensive predicates

Wang

Rundensteiner

2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

Self Cite

View full text Add to dashboard Cite

Multi-way stream joins with expensive join predicates lead to great challenge for real-time (or close to real-time) stream processing. Given the memory-and CPU-intensive nature of such stream join queries, scalable processing on a cluster must be employed. This paper proposes a novel scheme for distributed processing of generic multi-way joins with window constraints, called Pipelined State Partitioning (PSP). We target generic joins with arbitrarily join conditions, which are used in non-trivial stream applications such as image matching and biometric recognizing. The PSP scheme partitions the states into disjoint slices in the time domain, and then distributes the fine-grained states in the cluster, forming a virtual computation ring. Compared to replicationbased distribution of non-equi-joins, PSP scheme is superior since: (1) zero state duplication and thus no repeated computations, (2) pipelined processing of every input tuple on multiple nodes to achieve low response time, and (3) cost-based adaptive workload distribution. We have implemented the proposed PSP schemes within the CAPE DSMS. Our experimental study demonstrates the significant performance improvements compared to the state-of-the-art generic distributed stream join algorithms.

show abstract

FireStream: Sensor Stream Processing for Monitoring Fire Spread

Abstract: Abstract

Cited by 10 publications

References 3 publications

Towards collaborative data reduction in stream-processing systems

Towards collaborative data reduction in stream-processing systems

Managing Event Information: Modeling, Retrieval, and Applications

Scalable stream join processing with expensive predicates

Contact Info

Product

Resources

About