TY - GEN
T1 - TurboReg
T2 - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
AU - Sabek, Ibrahim
AU - Musleh, Mashaal
AU - Mokbel, Mohamed F
PY - 2018/11/6
Y1 - 2018/11/6
N2 - Predicting the presence or absence of spatial phenomena has been of great interest to scientists pursuing research in several applications including epidemic diseases detection, species occurrence prediction and earth observation. In this operation, a geographical space is divided by a two-dimensional grid, where the prediction (i.e, either 0 or 1) is performed at each cell in the grid. A common approach to solve this problem is to build spatial logistic regression models (a.k.a autologistic models) that estimate the prediction at any location based on a set of predictors (i.e., features) at this location and predictions from neighboring locations. Unfortunately, existing methods to build autologistic models are computationally expensive and do not scale up for large-scale grid data (e.g., fine-grained satellite images). This paper introduces TurboReg, a scalable framework to build autologistic models for predicting large-scale spatial phenomena. TurboReg considers both the accuracy and efficiency aspects when learning the regression model parameters. TurboReg is built on top of Markov Logic Network (MLN), a scalable statistical learning framework, where its internals and data structures are optimized to process spatial data. A set of experiments using large real and synthetic data show that TurboReg achieves at least three orders of magnitude performance gain over existing methods while preserving the model accuracy.
AB - Predicting the presence or absence of spatial phenomena has been of great interest to scientists pursuing research in several applications including epidemic diseases detection, species occurrence prediction and earth observation. In this operation, a geographical space is divided by a two-dimensional grid, where the prediction (i.e, either 0 or 1) is performed at each cell in the grid. A common approach to solve this problem is to build spatial logistic regression models (a.k.a autologistic models) that estimate the prediction at any location based on a set of predictors (i.e., features) at this location and predictions from neighboring locations. Unfortunately, existing methods to build autologistic models are computationally expensive and do not scale up for large-scale grid data (e.g., fine-grained satellite images). This paper introduces TurboReg, a scalable framework to build autologistic models for predicting large-scale spatial phenomena. TurboReg considers both the accuracy and efficiency aspects when learning the regression model parameters. TurboReg is built on top of Markov Logic Network (MLN), a scalable statistical learning framework, where its internals and data structures are optimized to process spatial data. A set of experiments using large real and synthetic data show that TurboReg achieves at least three orders of magnitude performance gain over existing methods while preserving the model accuracy.
KW - Autologistic models
KW - Factor graph
KW - First-order logic
KW - Markov logic networks
KW - Spatial regression
UR - http://www.scopus.com/inward/record.url?scp=85058653614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058653614&partnerID=8YFLogxK
U2 - 10.1145/3274895.3274987
DO - 10.1145/3274895.3274987
M3 - Conference contribution
AN - SCOPUS:85058653614
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 129
EP - 138
BT - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
A2 - Xiong, Li
A2 - Tamassia, Roberto
A2 - Banaei, Kashani Farnoush
A2 - Guting, Ralf Hartmut
A2 - Hoel, Erik
PB - Association for Computing Machinery
Y2 - 6 November 2018 through 9 November 2018
ER -