QoS Management of Real-Time Data Stream Queries in Distributed Environments

Wei, Yuan; Prasad, Vibha; Son, Sang H.

doi:10.1109/isorc.2007.49

Cited by 5 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The techniques for estimating selectivity using sampling, histogram, and wavelet in the conventional database environment were investigated [39–42]. Those for estimating selectivity in data stream management systems were also investigated [43–47]. However, direct and in-network employment of these techniques in WSNs is infeasible due to communication overhead and resource constraints of the sensor nodes.…”

Section: Open Issuesmentioning

confidence: 99%

In-Network Processing of Joins in Wireless Sensor Networks

Kang

2013

Sensors

View full text Add to dashboard Cite

The join or correlated filtering of sensor readings is one of the fundamental query operations in wireless sensor networks (WSNs). Although the join in centralized or distributed databases is a well-researched problem, join processing in WSNs has quite different characteristics and is much more difficult to perform due to the lack of statistics on sensor readings and the resource constraints of sensor nodes. Since data transmission is orders of magnitude more costly than processing at a sensor node, in-network processing of joins is essential. In this paper, the state-of-the-art techniques for join implementation in WSNs are surveyed. The requirements and challenges, join types, and components of join implementation are described. The open issues for further research are identified.

show abstract

Section: Open Issuesmentioning

confidence: 99%

In-Network Processing of Joins in Wireless Sensor Networks

Kang

2013

Sensors

View full text Add to dashboard Cite

show abstract

“…Tu et al [2006] use system identification and rigorous controller analysis to improve the benefits of input load shedding. Wei et al [2007] apply just-in-time sampling to estimate the output size of query operators. The estimation results are used to control the intermediate query result propagation strategy.…”

Section: Query Optimizationmentioning

confidence: 99%

“…This is especially important in the context of data streams as the input rate of the streams is constantly changing. Wei et al were the first to consider a sampling-based approach to evaluate the data stream query workloads and use the results to manage the query QoS [Wei et al 2007]. In this paper we employed this sampling technique and used it as part of our prediction-based QoS management mechanism.…”

Section: Selectivity Estimationmentioning

confidence: 99%

Applying Formal Methods to Modeling and Analysis of Real-time Data Streams

Kapitanova

Wei²,

Kang

et al. 2011

Journal of Computing Science and Engineering

Self Cite

View full text Add to dashboard Cite

Achieving situation awareness is especially challenging for real-time data stream applications because they i) operate on continuous unbounded streams of data, and ii) have inherent realtime requirements. In this paper we showed how formal data stream modeling and analysis can be used to better understand stream behavior, evaluate query costs, and improve application performance. We used MEDAL, a formal specification language based on Petri nets, to model the data stream queries and the quality-of-service management mechanisms of RT-STREAM, a prototype system for data stream management. MEDAL's ability to combine query logic and data admission control in one model allows us to design a single comprehensive model of the system. This model can be used to perform a large set of analyses to help improve the application's performance and quality of service.

show abstract