Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Solving Vehicle Routing Problems Using Constraint Programming and Metaheuristics
Journal of Heuristics
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
ACM Computing Surveys (CSUR)
Discovering Association Patterns in Large Spatio-temporal Databases
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Diversity Maximization Approach for Multiobjective Optimization
Operations Research
Predicting electricity distribution feeder failures using machine learning susceptibility analysis
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
A process for predicting manhole events in Manhattan
Machine Learning
Analysis of advanced meter infrastructure data of water consumption in apartment buildings
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Asset-intensive businesses across industries rely on physical assets to deliver services to their customers, and effective asset management is critical to the businesses. Today, businesses may make use of enterprise asset-management (EAM) solutions for many asset-related processes, ranging from the core asset-management functions to maintenance, inventory, contracts, warranties, procurement, and customer-service management. While EAM solutions have transformed the operational aspects of asset management through data capture and process automation, the decision-making process with respect to assets still heavily relies on institutional knowledge and anecdotal insights. Analytics-driven asset management is an approach that makes use of advanced analytics and optimization technologies to transform the vast amounts of data from asset management, metering, and sensor systems into actionable insight, foresight, and prescriptions that can guide decisions involving strategic and tactical assets, as well as customer and business models.