Answering enumeration queries with the crowd

Trushkowsky, Beth; Kraska, Tim; Franklin, Michael J.; Sarkar, Purnamrita

doi:10.1145/2845644

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…Thus, this process incurs the monetary cost and causes time latency. Thus, the main reason for using the monetary cost and time latency metrics in our research work to estimate the missing values of skylines is due to the fact that these metrics are the most critical factors when dealing with crowdsourced databases [4], [5], [8], [10], [41], [45], [46], [47], [48]. These six metrics are measured by varying the dataset size, missing rate, the user-given threshold value for the acceptable relative error rate between the real missing and the estimated values of the skylines, and the number of the batch for crowd-sourced estimation.…”

Section: Experiments Evaluationmentioning

confidence: 99%

Skyline Queries Computation on Crowdsourced- Enabled Incomplete Database

et al. 2020

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Data incompleteness becomes a frequent phenomenon in a large number of contemporary database applications such as web autonomous databases, big data, and crowd-sourced databases. Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. Most importantly, the skylines derived from incomplete databases are also incomplete in which some values are missing. Retrieving skylines with missing values is undesirable, particularly, for recommendation and decision-making systems. Furthermore, running skyline queries on a database with incomplete data raises a number of issues influence processing skyline queries such as losing the transitivity property of the skyline technique and cyclic dominance between the tuples. The issue of estimating the missing values of skylines has been discussed and examined in the database literature. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible values using the crowd. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result that meets the user's preferences. This paper proposes an approach for estimating the missing values of the skylines by first exploiting the available data and utilizes the implicit relationships between the attributes in order to impute the missing values of the skylines. This process aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches.

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Section: Experiments Evaluationmentioning

confidence: 99%

Skyline Queries Computation on Crowdsourced- Enabled Incomplete Database

et al. 2020

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“…There are some works that apply crowd-sourcing techniques [TKFS13,TKFS16] and develop statistical tools to find a trade-off between cost/time and completeness of results. However, while the query result is constructed incrementally (the query is performed on an initial small dataset and the result is refined as more source data arrives to the system), in our proposal an initial large dataset is approximated at once.…”

Section: Related Workmentioning

confidence: 99%

Trading Accuracy for Performance in Data Processing Applications.

Barquero¹,

Troya²,

Vallecillo³

2019

JOT

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Applications that need to process large volumes of data to make informed decisions, such as stream processing systems or those dealing with social networks, usually impose strong requirements on resources like memory, processing time, disk or network latency. One approach to address this problem is to reduce the amount of data to be processed, at the cost of decreasing the confidence on the results. Estimating the errors of such approximate solutions becomes a critical issue. In this paper, we explore different approximation possibilities depending on how the data is organized, and what information needs to be obtained from them. We propose an approach to estimate the accuracy of these approximate solutions in the context of data processing systems, in terms of the precision and recall of the results obtained. A case study is used to validate the proposal and to evaluate the performance of different types of approximations.

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“…We therefore started to develop techniques that estimate not only the amount of missing data based on techniques from [100,101,102] but also the impact those items might have on query results [19,20]. We assume a simple data integration scenario in which (semi-)independent data sources are integrated into a single database.…”

Section: Uncertainty As Unknown Unknownsmentioning

confidence: 99%

Northstar

Kraska

2018

Proc. VLDB Endow.

Self Cite

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In order to democratize data science, we need to fundamentally rethink the current analytics stack, from the user interface to the "guts." Most importantly, enabling a broader range of users to unfold the potential of (their) data requires a change in the interface and the "protection" we offer them. On the one hand, visual interfaces for data science have to be intuitive, easy, and interactive to reach users without a strong background in computer science or statistics. On the other hand, we need to protect users from making false discoveries. Furthermore, it requires that technically involved (and often boring) tasks have to be automatically done by the system so that the user can focus on contributing their domain expertise to the problem. In this paper, we present Northstar, the Interactive Data Science System, which we have developed over the last 4 years to explore designs that make advanced analytics and model building more accessible.

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Answering enumeration queries with the crowd

Cited by 12 publications

References 22 publications

Skyline Queries Computation on Crowdsourced- Enabled Incomplete Database

Skyline Queries Computation on Crowdsourced- Enabled Incomplete Database

Trading Accuracy for Performance in Data Processing Applications.

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