Mediaq Upload Metadata First
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We propose some interesting ideas, e.g., small amount of metadata is transmitted to a server in real-time while video content will remain on the device; the server will ping the device when it wants the user to upload the video.
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Link: http://dl.acm.org/citation.cfm?id=2732966Entropy-based Histograms for Selectivity Estimation
Histograms have been extensively used for selectivity estimation by academics and have successfully been adopted by database industry. However, the estimation error is usually large for skewed distributions and biased attributes, which are typical in real-world data. Therefore, we propose effective models to quantitatively measure bias and selectivity based on information entropy. These models together with the principles of maximum entropy are then used to develop a class of entropy-based histograms. Moreover, since entropy can be computed incrementally, we present the incremental variations of our algorithms that reduce the complexities of the histogram construction from quadratic to linear. We conducted an extensive set of experiments with both synthetic and real-world datasets to compare the accuracy and efficiency of our proposed techniques with many other histogram-based techniques, showing the superiority of the entropy-based approaches for both equality and range queries.
Links:
http://dl.acm.org/citation.cfm?id=2505756
http://infolab.usc.edu/DocsDemos/to-CIKM13.pdf
A Server-Assigned Crowdsourcing Framework
With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.
Links:
http://dl.acm.org/citation.cfm?id=2729713
http://infolab.usc.edu/DocsDemos/to_TSAS15.pdf
http://dl.acm.org/citation.cfm?doid=2729713
Maximum Complex Task Assignment in Spatial Crowdsourcing
Spatial crowdsourcing has gained emerging interest from both research communities and industries. Most of current spatial crowdsourcing frameworks assume independent and atomic tasks. However, there could be some cases that one needs to crowdsource a spatial complex task which consists of some spatial sub-tasks (i.e., tasks related to a specific location). The spatial complex task's assignment requires assignments of all of its sub-tasks. The currently available frameworks are inapplicable to such kind of tasks. In this paper, we introduce a novel approach to crowdsource spatial complex tasks. We first formally define the Maximum Complex Task Assignment (MCTA) problem and propose alternative solutions. Subsequently, we perform various experiments using both real and synthetic datasets to investigate and verify the usability of our proposed approach.
Links:
http://dl.acm.org/citation.cfm?id=2539243
http://dl.acm.org/citation.cfm?id=2539243
http://dl.acm.org/citation.cfm?doid=2539150.2539243
Spatial Crowdsourcing Research @ Infolab, USC Fall 2013
This document discusses research on spatial crowdsourcing and differential privacy. It introduces the PriGeoCrowd framework which aims to preserve worker location privacy in spatial crowdsourcing using differential privacy. The framework uses a WorkerPSD technique to guide efficient task assignment and differentially private spatial decompositions. It also proposes a geocast query algorithm that expands the query using the WorkerPSD to navigate worker search and assignment.
A Benchmark to Evaluate Mobile Video Upload to Cloud Infrastructures
A Benchmark to Evaluate Mobile Video Upload to Cloud InfrastructuresUniversity of Southern California
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.Harvesting Crowdsourced Mobile Videos under Bandwidth Constraint
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Crowdsourcing the Acquisition and Analysis of Mobile Videos for Disaster Resp...
Crowdsourcing the Acquisition and Analysis of Mobile Videos for Disaster Resp...University of Southern California
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814iCampus Mobile 2012
Motivation: the popularity of smart phones, and availability of data allow us to create an application that provide students with useful services, and allow them to share their experience
Integrate various types of data, including public data (Google Map, Facebook, Twitter), user data (picture, location), USC's private data (event, alert)
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Crowdsourcing the Acquisition and Analysis of Mobile Videos for Disaster Resp...
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Spatial Crowdsourcing Research @ Infolab, USC Fall 2013
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