Scalable histograms on large probabilistic data

Tang, Mingwang; Li, Feifei

doi:10.1145/2623330.2623640

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…The focus of this work was to adjust the balance between trees ("Tributaries") and multipath ("Deltas"), in response to varying network conditions, expressed in terms of the packets drop rate, for the purpose of robust and efficient in-network computation of aggregates. In particular, algorithms are presented in [8], [9], [10], [11], [12], [13], [14], [15] for computing frequent item sets, quantiles, histograms, and spatio-temporal data along with the criteria for changing the role of a particular node. In this paper, the problem of constructing the number of data aggregation trees with minimum energy cost will be studied.…”

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confidence: 99%

On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor Networks

2016

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Abstract-Data aggregation protocols are generally utilized to extend the lifetime of sensor networks by reducing the communication cost. Traditionally, tree-based structured approaches that is a basic operation for the sink to periodically collect reports from all sensors were concerned about many applications. Since the data aggregation process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs when maintaining the routing tables of sensors. In our previous work, we have demonstrated that multiple trees, as well as split trees, can provide additional lifetime extensions when certain nodes are deployed in a wireless sensor network. In this paper, we explore how the number of the family-set of trees influences the lifetime gain, and we work on the problem of constructing data aggregation trees that minimizes the total energy cost of data transmission under diverse set of scenarios and network query region. Through dividing query area, the sensory and aggregation data has been returned through a number of different forwarding trees within each sub query area, which reduces the network "hot spots". To evaluate the performance of the proposed approach, we have compared and analyzed an angular division routing algorithm and query region division routing with LEACH. Theoretical and experimental results illustrate that the query region division algorithm based on angle leads to lower energy cost in comparison with the models reported in the literatures.Index Terms-Energy efficiency, query region division, LEACH, tree structure, wireless sensor networks.

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confidence: 99%

On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor Networks

2016

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Scalable histograms on large probabilistic data

Tang

2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Histogram construction is a fundamental problem in data management, and a good histogram supports numerous mining operations. Recent work has extended histograms to probabilistic data [5][6][7]. However, constructing histograms for probabilistic data can be extremely expensive, and existing studies suffer from limited scalability [5][6][7]. This work designs novel approximation methods to construct scalable histograms on probabilistic data. We show that our methods provide constant approximations compared to the optimal histograms produced by the state-of-the-art in the worst case. We also extend our methods to parallel and distributed settings so that they can run gracefully in a cluster of commodity machines. We introduced novel synopses to reduce communication cost when running our methods in such settings. Extensive experiments on large real data sets have demonstrated the superb scalability and efficiency achieved by our methods, when compared to the state-ofthe-art methods. They also achieved excellent approximation quality in practice.

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QuickSel: Quick Selectivity Learning with Mixture Models

Park

Zhong

Mozafari

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice-i.e., require an exponential number of buckets-or quickly lose their advantage as they observe more queries. For example, the state-of-the-art technique requires 318,936 buckets (and over 8 seconds of refinement overhead per query) after observing only 300 queries.In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, Quick-Sel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is on average 254.6× faster than stateof-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is on average 57.3% and 91.1% more accurate than periodicallyupdated histograms and samples, respectively.

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Scalable histograms on large probabilistic data

Cited by 4 publications

References 30 publications

On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor Networks

On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor Networks

Scalable histograms on large probabilistic data

QuickSel: Quick Selectivity Learning with Mixture Models

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