Bolstering pattern mining by taking into account relationships and uncertainty in data

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By and large, data stored in databases are considered facts and observations that took place. These records are also typically considered mutually independent. For instance, a hospital database contains information about patients that are independent from each other and a record could state a visit at a given date for a particular diagnostic for which a given treatment is given. These are all observed facts. Data mining and analysis tasks assume this independence between records and consider the recorded facts to be certain. However, in many real applications some relationships exist between observations and are known and thus could be recorded. The fact that a person is a friend of another person, or a family member, or the fact that a phone number calls another telephone number creates a relationship between records. These are information networks. Ignoring these relationships in data analysis is a missed opportunity to get better insights about the data. Also, uncertainties in data exist and can come from the measurement instruments such as sensors or any confidence level attached to the source of information. For instance, the temperature measured by a sensor may not be certain but assumed in a range between a minimum and a maximum; the identity of a car observed at an intersection may only be ascertained with some level of confidence. These are probabilistic data because the value of an attribute could be affixed with a probability level. Uncertainty can also be ascribed to relationships in data to form probabilistic information networks. Unfortunately, most existing data mining approaches assume independence and certainty of data. Very little work has been done on effectively analyzing probabilistic databases or probabilistic information networks to discover useful new knowledge or patterns in such data collections that takes into account the uncertainties and the relationships. The purpose of this proposal is to work on devising effective and efficient techniques to mine and learn from probabilistic databases and probabilistic information networks, and show the use and relevance of these techniques in real domains applications where uncertainty is germane to the data collection, such as in automated information extraction from textual data.