Monitoring and troubleshooting distributed systems is notoriously difficult; potential problems
are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used …

The collection of time series data increases as more monitoring and automation are being
deployed. These deployments range in scale from an Internet of things (IoT) device located …

Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …

We present JetStream, a system that allows real-time analysis of large, widely-distributed
changing data sets. Traditional approaches to distributed analytics require users to specify …

We consider extending decision support facilities toward large sophisticated networks, upon
which multidimensional attributes are associated with network entities, thereby forming the …

Motivated by the recent active research on online analytical processing (OLAP), we develop
a computation and storage efficient algorithm for estimating equation (EE) estimation in …

Quantile regression, which analyzes the conditional distribution of outcomes given a set of
covariates, has been widely used in many fields. However, the volume and velocity of big …

We address the problem of capturing and tracking local correlations among time evolving
time series. Our approach is based on comparing the local auto-covariance matrices (via …

As OLAP engines are widely used to support multidimensional data analysis, it is desirable
to support in data cubes advanced statistical measures, such as regression and filtering, in …

Many modern applications, including online financial feeds, tag-based mass transit systems
and RFID-based supply chain management systems transmit real-time data streams. There …