Towards a requirements-driven framework for detecting malicious behavior against software systems
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
Requirements-Driven root cause analysis using markov logic networks
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
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Machine learning and data mining systems have achieved many impressive successes, but to become truly widespread they must be able to work with less help from people. This requires automating the data cleaning and integration process, handling multiple types of objects and relations at once, and easily incorporating domain knowledge. In this talk, I describe how we are pursuing these aims using Markov logic networks, a representation that combines first-order logic and probabilistic graphical models. Data from multiple sources is integrated by automatically learning mappings between the objects and terms in them. Rich relational structure is learned using a combination of ILP and statistical techniques. Knowledge is incorporated by viewing logic statements as soft constraints on the models to be learned. Application to a real-world university domain shows our approach to be accurate, efficient, and less labor-intensive than traditional ones.