A survey of queries over uncertain data

Wang, Yijie; Li, Xiaoyong; Wang, Yuan

doi:10.1007/s10115-013-0638-6

Cited by 105 publications

(35 citation statements)

References 150 publications

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“…Wang et al [18] presents a survey about data uncertainty and the types of uncertain data queries. Uncertain top-k query returns most probable top-k answers [16].…”

Section: Related Workmentioning

confidence: 99%

Predicting Optimal Facility Location without Customer Locations

Yılmaz

Elbaşı

Ferhatosmanoğlu

2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Deriving meaningful insights from location data helps businesses make beer decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem seing for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. e predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can nd the optimal location when sucient information is provided.

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“…Wang et al [18] presents a survey about data uncertainty and the types of uncertain data queries. Uncertain top-k query returns most probable top-k answers [16].…”

Section: Related Workmentioning

confidence: 99%

Predicting Optimal Facility Location without Customer Locations

Yılmaz

Elbaşı

Ferhatosmanoğlu

2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Therefore, our tasking server is required to deal with the location uncertainty of the participants to estimate distances. Queries over uncertain spatio-temporal data have been extensively studied with many algorithms to handle queries such as nearest neighbors, top-k, and range queries [33]. However, most of them aim at ranking the query answers and cannot be directly adopted in our work which requires actual distances to optimize the sensing cost.…”

Section: ) Distance Estimationmentioning

confidence: 99%

Spatial Task Assignment for Crowd Sensing with Cloaked Locations

Pournajaf

Xiong

Sunderam

et al. 2014

2014 IEEE 15th International Conference on Mobile Data Management

175

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Abstract-Distributed mobile crowd sensing is becoming a valuable paradigm, enabling a variety of novel applications built on mobile networks and smart devices. However, this trend brings several challenges, including the need for crowdsourcing platforms to manage interactions between applications and the crowd (participants or workers). One of the key functions of such platforms is spatial task assignment which assigns sensing tasks to participants based on their locations. Task assignment becomes critical when participants are hesitant to share their locations due to privacy concerns. In this paper, we examine the problem of spatial task assignment in crowd sensing when participants utilize spatial cloaking to obfuscate their locations. We investigate methods for assigning sensing tasks to participants, efficiently managing location uncertainty and resource constraints. We propose a novel two-stage optimization approach which consists of global optimization using cloaked locations followed by a local optimization using participants' precise locations without breaching privacy. Experimental results using both synthetic and real data show that our methods achieve high sensing coverage with low cost using cloaked locations.

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“…1 Assume the randomly selected newcomers are .Y 1 ; : : : ; Y r / 2 for Each Y i in newcomers do 3 for Each provider candidate providerCan j in providerList do 4 Assume the response time between Y i and providerCan j is rt i;j ; 1 6 i 6 r; 1 6 j 6 n r. 5 Sort rt i;j in a ascending order, and get the top k corresponding providers. 6 We represent the top k providers as G i D ¹X i;1 ; X i;2 ; : : : ; X i;k º as the providers for Y i . 7 The intersection provider set is G inter = T r iD1 G i D .X inter,1 ; X i nter;2 ; : : : ; X i nter;c /.…”

Section: Selection Of Newcomers and Providersmentioning

confidence: 99%

Repairing multiple failures adaptively with erasure codes in distributed storage systems

Peng

Wang

et al. 2015

Concurrency and Computation

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Summary Repairs of multiple failures in distributed storage systems have posed the challenges for erasure coding: how to minimize the repair time with the least extra repair network traffic cost. However, existing repair schemes designed for single failure suffer from the high network traffic cost due to the serial repairs for multiple failures. Repair schemes designed for multiple failures suffer from long repair time due to the centralized repair structure. In this paper, we propose a decentralized adaptive repair scheme, called DARS, to minimize the repair time with the least extra network traffic cost. Specially, we propose a three‐layer repair model to support the repairs for both the single and multiple failures. For low repair time, a bandwidth‐aware node selection technique is proposed to guide the selection of nodes, and a line‐structured data transmission technique is proposed to organize the data transmission between the providers and the newcomer. For the least extra network traffic cost, a core‐based data distribution technique is proposed to organize the data transmission between the coordinator and other newcomers, and an intersection provider adjustment technique is proposed to adaptively adjust the number of intersection providers. Moreover, we adopt the ‘lazy repair’ within a stripe to further reduce the repair network traffic cost. We implement and evaluate DARS on our raid distributed storage system under various parameter settings with 30 physical machines and 200 virtual machines. Experimental results confirm that DARS reduces the repair time by 29% and 55% on average compared with tree‐structured repair and CORE, respectively. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

A survey of queries over uncertain data

Cited by 105 publications

References 150 publications

Predicting Optimal Facility Location without Customer Locations

Predicting Optimal Facility Location without Customer Locations

Spatial Task Assignment for Crowd Sensing with Cloaked Locations

Repairing multiple failures adaptively with erasure codes in distributed storage systems

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