Tracking of multiple quantiles in dynamically varying data streams

Quantiles on Streams

The need to summarize data has been around since the earliest days of data processing. Large volumes of raw, unstructured data easily overwhelm human ability to comprehend or digest, and tools that help identify the major underlying trends or patterns in data have enormous value. Quantiles characterize distributions of real world data sets in ways that are less sensitive to outliers than simple...

Quantiles and Equi-depth Histograms over Streams

Dynamically Varying Environments

The vast majority of work to date concerned with obstacle avoidance for manipulators has dealt with task descriptions in the form ofpick-and-place movements. The added flexibility in motion control for manipulators possessing redundant degrees offreedom permits the consideration of obstacle avoidance in the context of a specified end-effector trajectory as the task description. Such a task defi...

Monitoring Multiple Data Streams in Real Time

Online monitoring of data streams poses a challenge in many data-centric applications, such as telecommunications networks, traffic management, trend-related analysis, webclick streams, intrusion detection, and sensor networks. Mining techniques employed in these applications have to be efficient in terms of space usage and per-item processing time while providing a high quality of answers to (...

Estimating Dominance Norms of Multiple Data Streams

There is much focus in the algorithms and database communities on designing tools to manage and mine data streams. Typically, data streams consist of multiple signals. Formally, a stream of multiple signals is (i, ai,j) where i’s correspond to the domain, j’s index the different signals and ai,j ≥ 0 give the value of the jth signal at point i. We study the problem of finding norms that are cumu...