Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Independence is good: dependency-based histogram synopses for high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient inference on sequence segmentation models
ICML '06 Proceedings of the 23rd international conference on Machine learning
Creating probabilistic databases from information extraction models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Information Sciences: an International Journal
Large-scale uncertainty management systems: learning and exploiting your data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods
Journal of Intelligent Information Systems
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Probabilistic graphical models provide a framework for compact representation and efficient reasoning about the joint probability distribution of several interdependent variables. This is a classical topic with roots in statistical physics. In recent years, spurred by several applications in unstructured data integration, sensor networks, image processing, bio-informatics, and code design, the topic has received renewed interest in the machine learning, data mining, and database communities. Techniques from graphical models have also been applied to many topics directly of interest to the database community including information extraction, sensor data analysis, imprecise data representation and querying, selectivity estimation for query optimization, and data privacy. As database research continues to expand beyond the confines of traditional enterprise domains, we expect both the need and applicability of probabilistic graphical models to increase dramatically over the next few years. With this tutorial, we are aiming to provide a foundational overview of probabilistic graphical models to the database community, accompanied by a brief overview of some of the recent research literature on the role of graphical models in databases.