Research Track Session RT15: Mining in Emerging Applications II – Summit 4, Ground Level, Egan Center
Chair: Petko Bogdanov
Optimizing Impression Counts for Outdoor Advertising
Yipeng Zhang (RMIT University); Yuchen Li (Singapore Management University ); Zhifeng Bao (RMIT University); Songsong Mo
(Wuhan University & RMIT University); Ping Zhang (Huawei)
How a billboard impress an audience?
Influence Measurement
The logistic function (Advertising market and Customer behavior)
The effectiveness of advertisement repetition varies from one person to another.
Influence meautrement is not submodular
- no approximation ratio for a greedy-based algorithm
NP-hard to approximate within any constant factor
Upper-bound estimation
Branch-and-Branch Framework
optimization
BBS: branch-and-Bound Framework
PBBS: Branch-and-Bound Framework with Progressive Bound-Estimation
Data: trajectory dataset: NYC, LA
experiment: algorithm
greedy,top-k, BBS, PBBS, LazyProbe
Varying the budget B
Varying the number of Trajectories
Scalability test in NYC
Comparison with LazyProbe
Conclusion
- real problem
- meet more than one billboard in each travel
- not uniform cost od billboards
- budget
- real solution
- while having the approximation guarantee
- real-world trajectory dataset and billboard
Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms
Boyang Li (Northeastern University); Yurong Cheng (Beijing Institute of Technology); Ye Yuan (Northeastern University); Guoren
Wang (Beijing Institute of Technology); Lei Chen (The Hong Kong University of Science and Technology)
No talk
Hidden POI Ranking with Spatial Crowdsourcing
Yue Cui (University of Electronic Science and Technology of China); Liwei Deng (University of Electronic Science and Technology
of China); Yan Zhao (Soochow University); Bin Yao (Shanghai Jiao Tong University); Vincent W. Zheng (WeBank); Kai Zheng
(University of Electronic Science and Technology of China)
Inequity of business market
how to generate candidate tasks and distribute proper tasks to proper workers with a goal of aggregating an accurate ranking got comparable H-POI
H-POI ranking consideration
- Maintain effectiveness
- worker quality
- task generation
- Improve efficiency
- Budget
- Time
Hidden POI ranking with spatial crowdsourcing
Preview: key Definitions
- spatial task
- comparable H-POI
- Valid Task Set(VTS)
Minimum VTS (MinVTS) Greedy Search Algorithm
Find MinVTS
Worker reliability
category reliability
Adapted from metapath2vec
Three meta paths are designed on check-in graph
area reliability
adapted from X-means: AIC BIC based X-means clustering
- adaptive number of clusters can be selected
- simplify reliability calculation in an internal based approach
Ranking aggregation
main purpose: Predict a gold-standard ranking that hinges on combining pairwise comparisons via crowdsourcing
closely related work: crowdBT(Chen et al.)
Tree-constrained Skip (TCS) Search
construct a minimum spanning trees(MSTs)
with H-POI ranking supervised(TCSS)
information gain
data: yelp-dataset
Effect of sampling number
Effect of k(kappa)
Effect of H-POI number
Conclusion
- Analyzed the necessities of H-PUI exploration
- Proposed That can aggregate H-POI ranking from pairwise comparison of the crowd
- The proposed approach can greatly reduce text pair searching time cost
Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications
Zhe Jiang (University of Alabama); Arpan Man Sainju (University of Alabama)
Spatial structured prediction
- structured prediction
- syntax tree in linguistics, music note sequence
- spatial structured prediction
- terrain map in hydrology
- potential energy landscape in material science
Data-Driven Approach: The Fourth Paradigm of Scientific Discovery
The pitfalls of Data-driven approach
- Generalizability
- Interpretability
- Reproducibility
- scalability
- Limited ground truth
flood mapping in Disaster Response
flood mapping in Hydrology
National water forecasting
In: spatial raster framework
Out: Aspacial classification model
Objective: Minimize classification errors
Constraints: explanatory feature layers contain noise
challenges
- noise obstacles in imagery > cloud, shadows, tree canopies
- special structure on 3D surface
- large data volumes
related works
- spatial proximity
- MRF, CRF, SAR
- Gaussian process(Kriging)
- CNN, Embedding spartial network
- network kringing
- geometric DL
- Graph DL
Contour Tree on 3D surface
Contour tree (poly-tree structure)
Collapsed contour tree
Hidden Markov contour tree
Hidden Markov Contour Tree(HMCT)
- probabilistic graphical model
- Assumptions:
- feature is Gaussian
- class transitional probability
- HMCT: Parameter Learning
- Approach: EM algorithm
experimental setup
- data: Hurricane Mathew flood, NC, 2016
- two study areas: Greenville NC, Grimesland NC
conclusion
- HCMT model
- contour tree construction, parameter learning, class inference algorithm
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta-Learning
Zheyi Pan (Shanghai Jiaotong University); Yuxuan Liang (Xidian University); Weifeng Wang (Shanghai Jiaotong University); Yong
Yu (Shanghai Jiaotong University); Yu Zheng (JD Intelligent Cities Research); Junbo Zhang (JD Intelligent Cities Research)
Intro
Important for
trafic management
public risk assesment
public safety
challenge
- spartial correlations
- temporal correlations
challenge diversity of Spatio-temporal correlations
- characteristic of locations and their mutual relationship are diverse
- conventional method: ARIMA, GBRTm linear model
- deep model
- spatial network temporal network to model ST correlations
- use a single model to predict traffic on all location
Insights
build a geo graph to describe spatial structure
Node: locations
Edges relation between locations
geographical features reveal characteristics of nodes and edges & impact different types of ST correlations
Overview od ST-MetaNet
- Encoder-Decoder
- meta graph Attention network
- meta recurrent network
- meta knowledge learner
- meta learner
meta graph attention
- calculate attention scores
- get meta knowledge about edge
- weight generation
- softmax&linear combination
Meta gated recurrent unit
evaluation
- data: TaxiBJ, METR-LA
- Metrics: MAE & RMSE
- use much less number of parameters
- evaluation on meta networks
- evaluation on meta knowledge
validate that meta-knowledge can reveal the similarity of ST corrections on nodes
conclusion
- deep meta learning based framework for Spatio-temporal data
- meta graph attention for modeling diverse spatial correlations
- meta gated recurrent Unit for modeling diverse temporal correlations
- achieve significant improvement on two real-world traffic prediction task
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network
Junchen Ye (Beihang University); Leilei Sun (Beihang University); Bowen Du (Beihang University); Yanjie Fu (University of Central
Florida); Xinran Tong (Beihang University); Hui Xiong (Rutgers University)
background
- sharing transportation plays important roles
- pickup demand
related-work
- hand-designed features
- LSTM/ConvLSTM + CNN
summary
- employ the model spatio + temporal
- USE CNN directly
- Single transport to predict single transport
from a Micro View
- Bases Decomposing
- wavelet Tranform
Base decomposition
-
decomposing methods
- clustering
- linear decomposing
proposed: autoencoder(applied)
Heterogeneous Information Fusion
- In fact, there are some deep correlations between different transportation
extract the hidden correlation between different
transport to improve the prediction accuracy
Our method: Cost-net
- transportation pattern decomposing
- heterogeneous information fusions
- spatial autoencoder & heterogenious Information Fusion
Experiment
- Data: NYC Citi Bike, NYC taxi
- Timeshift: small size of a dataset will hurt the high-level features learning for autoencoder.
- cut original data with, all-time shifts expanse the dataset to train an autoencoder
- not only expanse the dataset but, help to learn the traffic feature
How to evaluate methods?
- best/mean result? No
- In the box plot, our method has the best mean perform and smallest variance
a channel single to single, two-channel double to double and our method is four-channel
the more channels we employ, the better results we get