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*** This repository is the implementation of the following papers: *** 1) Hien To, Leyla Kazemi, and Cyrus Shahabi, A Server-Assigned Spatial Crowdsourcing Framework, Journal ACM Transactions on Spatial Algorithms and Systems, Volume 1 Issue 1, Article No. 2. , New York, NY, USA, August 2015 2) Hien To, Liyue Fan, Luan Tran, and Cyrus Shahabi, Real-Time Task Assignment in Hyperlocal Spatial Crowdsourcing under Budget Constraints, In Proceeding of IEEE International Conference on Pervasive Computing and Communications (PerCom 2016), Sydney, Australia, March 14-18, 2016 Related studies: https://github.com/ubriela/geocrowd-priv-dynamic https://github.com/ubriela/geocrowd-priv https://github.com/ubriela/geocrowd-priv-demo https://github.com/ubriela/geocrowd-complex ---------------------- Version -------------------------- 1.1 ---------------------- Packages -------------------------- cplex: solve bipartite matching with IBM cplex library org.datasets.gowalla: preprocess gowalla dataset org.datasets.yelp: preprocess yelp dataset org.geocrowd: main package, with GeoCrowd.java org.geocrowd.maxmatching: a package to solve weighted bipartite matching org.geocrowd.maxflow: an opensource to solve max-flow using Ford-Fulkerson algorithm test.geocrowd: test package, run experiments ---------------------- How to run? ---------------------- ----------- Gowalla Step 1: Run PreProcess.java Step 2: Run GeoCrowd.java Step 1: run PreProcessTest.java 1) testFilterInput(): to get data points in California region 2) generateWorkers(): generate workers in folder dataset/real/gowalla/worker. Each file is associated with a day. The format is as shown in WORKER INPUT FILES. 3) computeLocationEntropy(): generate location entropy in file gowalla_loc_entropy (format shown below). Step 2: run GeoCrowd.java 1) testGenerateGowallaTasks(): output file dataset\real\gowalla\task. 2) testGeoCrowd(): ----------- Yelp ----------- SYN ----------------------------------------------- ----------- SYN-UNIFORM Step 1: Run randomly_distributed.m to generate worker set and task set ----------- SYN-SKEWED Step 1: Run gaussian_clusters.m to generate worker set and task set ---------------------- Next: Run PreProcess.java, testGenerateSynWorkersTasks() Step 2: Run GeoCrowd.java testGeoCrowd(). Note that we need to set DATA_SET = 1 for SYN-SKEWED and DATASET = 2 for SYN-UNIFORM ---------------------- gowalla_totalCheckins_CA ---------------------- Extract data from Gowalla dataset. This is an intermediate input file ****** userID datetime lat lng pointID 2 2010-10-21T00:03:50Z 34.0430230998 -118.2671570778 14637 ****** ---------------------- gowalla_entropy ---------------------- Location entropy of each location id ****** loc_id, loc_entropy ****** ---------------------- gowalla_loc_entropy ---------------------- Location entropy of each grid cell (after grid discretization) ****** row,col,loc_entropy ****** ---------------------- Real Data sets ---------------------- WORKER INPUT FILES: ****** worker_id, latitude, longitude, maxT, working_region[min_lat, min_lng, max_lat, max_lng], expertise_ids[expertise_id] EAvzjtPx7kBk83GCWiaSDA,33.5130361,-112.08681055,4,[33.500805,-112.0995348,33.5252672,-112.0740863],[33,11,4,5,6] ****** TASK INPUT FILES: ****** latitude, longitude, time_instance, density, task_type 36.9778628999,-121.96604528228605,10,1.9609640474436814,1 ****** ---------------------- Synthetic Data sets ---------------------- WORKER INPUT FILES latitude, longitude TASK INPUT FILES: latitude, longitude
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