Cost-efficient processing of MIN/MAX queries over distributed sensors with uncertainty

Liu, Zhenyu; Sia, Ka Cheung; Cho, Junghoo

doi:10.1145/1066677.1066823

Cited by 22 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are three differences between our study and [19,28,42]. First, the uncertain data models adopted in the above work are different from the uncertain stream model discussed in this paper, due to different application requirements.…”

Section: Continuous Sensor Stream Monitoringmentioning

confidence: 91%

“…Only approximate answers with certain confidence bounds are provided, but the cost of data maintenance and query answering is significantly reduced. More specifically, Liu et al [42] study Min/Max query monitoring over distributed sensors. In their scenario, queries are submitted to a central server, and the major cost in query answering is the communication cost between the central server and distributed sensors.…”

Section: Continuous Sensor Stream Monitoringmentioning

confidence: 99%

“…First, the uncertain data models adopted in the above work are different from the uncertain stream model discussed in this paper, due to different application requirements. Second, the monitored queries are different: [19,28,42] deal with general SQL queries, Min/Max queries, and probabilistic queries, respectively, but our study focuses on top-k queries on uncertain streams specifically. Last, while the above work only provides approximate answers, our study can provide a spectrum of methods including an exact algorithm, a random method and their space efficient versions.…”

Section: Continuous Sensor Stream Monitoringmentioning

confidence: 99%

See 2 more Smart Citations

Continuously monitoring top-k uncertain data streams: a probabilistic threshold method

Hua

Pei

2009

Distrib Parallel Databases

View full text Add to dashboard Cite

Recently, uncertain data processing has become more and more important. Although a significant amount of previous research explores various continuous queries on data streams, continuous queries on uncertain data streams have seldom been investigated. In this paper, we formulate a novel and challenging problem of continuously monitoring top-k uncertain data streams, and propose a probabilistic threshold method. We develop four algorithms systematically: a deterministic exact algorithm, a randomized method, and their space-efficient versions using quantile summaries. An extensive empirical study using real data sets and synthetic data sets is reported to verify the effectiveness and the efficiency of our methods.

show abstract

Section: Continuous Sensor Stream Monitoringmentioning

confidence: 91%

Section: Continuous Sensor Stream Monitoringmentioning

confidence: 99%

Section: Continuous Sensor Stream Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Continuously monitoring top-k uncertain data streams: a probabilistic threshold method

Hua

Pei

2009

Distrib Parallel Databases

View full text Add to dashboard Cite

show abstract

“…In [6,14,21,25], efficient methods for probing fresh data from stream sources and sensor networks have been considered. In [18], integrity constraints are used to clean dirty data.…”

Section: Querying Uncertain Databasesmentioning

confidence: 99%

Cleaning uncertain data with quality guarantees

2008

View full text Add to dashboard Cite

Uncertain or imprecise data are pervasive in applications like location-based services, sensor monitoring, and data collection and integration. For these applications, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with statistical confidence. Given that a limited amount of resources is available to "clean" the database (e.g., by probing some sensor data values to get their latest values), we address the problem of choosing the set of uncertain objects to be cleaned, in order to achieve the best improvement in the quality of query answers. For this purpose, we present the PWS-quality metric, which is a universal measure that quantifies the ambiguity of query answers under the possible world semantics. We study how PWS-quality can be efficiently evaluated for two major query classes: (1) queries that examine the satisfiability of tuples independent of other tuples (e.g., range queries); and (2) queries that require the knowledge of the relative ranking of the tuples (e.g., MAX queries). We then propose a polynomial-time solution to achieve an optimal improvement in PWS-quality. Other fast heuristics are presented as well. Experiments, performed on both real and synthetic datasets, show that the PWS-quality metric can be evaluated quickly, and that our cleaning algorithm provides an optimal solution with high efficiency. To our best knowledge, this is the first work that develops a quality metric for a probabilistic database, and investigates how such a metric can be used for data cleaning purposes.

show abstract

“…Recently, real-time data processing or data stream are progressive rapidly, and many applications in Cloud or IoT (Internet of Things), such as sensor network, finance, medical, traffic and military affairs etc, are characterized as continuous data queries, in which aggregation is the most expensive query [1]. In this paper, we focus on dedicate technology for extreme aggregation (MAX& MIN query) over timebased sliding window.…”

Section: Introductionmentioning

confidence: 99%

Space Reduction for Extreme Aggregation of Data Stream over Time-Based Sliding Window

Ding

Han

Wang

et al. 2012

2012 IEEE Fifth International Conference on Cloud Computing

View full text Add to dashboard Cite

Cost-efficient processing of MIN/MAX queries over distributed sensors with uncertainty

Cited by 22 publications

References 15 publications

Continuously monitoring top-k uncertain data streams: a probabilistic threshold method

Continuously monitoring top-k uncertain data streams: a probabilistic threshold method

Cleaning uncertain data with quality guarantees

Space Reduction for Extreme Aggregation of Data Stream over Time-Based Sliding Window

Contact Info

Product

Resources

About