• Calendar

May 8, Tuesday
12:00 – 13:00

Monitoring, interpretation, and analysis of large amounts of time-stamped data are tasks that are at the core of tasks such as the management of chronic patients, the detection of malware, or the integration of Intelligence data from multiple sources, and the related task of learning new knowledge from the accumulating data. In the case of the medical domain, these tasks are crucial for chronic-patient care, retrospective quality assessment, and clinical research. I will briefly describe several conceptual and computational architectures developed over the past 20 years, mostly by my research teams at Stanford and Ben Gurion universities, for knowledge-based performance of these tasks, and will highlight the complex and interesting relationships amongst them. Examples of such architectures include the IDAN and Momentum goal-directed and data-driven temporal-abstraction architectures, the KNAVE-II and VISITORS interactive-exploration frameworks for single and multiple longitudinal records, and the KarmaLego temporal data mining methodology. I will also point out the progression from individual-subject monitoring and therapy, to multiple-patient aggregate analysis and research, and finally to the learning of new knowledge. This progression, however, can also be viewed as a positive-feedback loop, in which new knowledge is brought back to bear upon both individual-patient management as well as on the learning of new and meaningful (temporal) associations.