Analyzing huge amounts of time interval data is a task arising more and more frequently in different domains like resource utilization and scheduling, real time disposition, as well as health care. Analyzing this type of data using established, reliable, and proven technologies is desirable and required. However, utilizing commonly used tools and multidimensional models is not sufficient, because of modeling, querying, and processing limitations. In this talk, I present a tool (TIDAIS) helpful to analyze large amounts of time interval data. I present a query language and demonstrate some API examples.
With time interval data, I will also share how time interval data plays a role in generating real-time user behavior predictions around interests and intent.