This paper is a presentation of the FETA framework and new work with Naomi Arnold on time varying models.
This paper updates previous work on fitting traffic profiles. We use more modern statistical techniques to question (and refute) previous assumptions about heavy tails in statistics. In this case we believe that the best fit for traffic volume per unit time is the log-normal distribution. Tail distributions an have big impacts for capacity planning and for prediction of pricing (say 95th percentile).
This paper looks the problem of releasing time-series data when privacy is a concern. It uses information theory to look at what extra information could "leak" if our device sends motion data. For example, can users be reidentified or can features such as height and weight be determined. A machine learning framework is given that can produce a tradeoff between allowing useful data to pass through while distorting the signal minimally to disguise information we wish to be private.
This paper describes the Raphtory system which is used to analysis large-scale time-varying graph systems. It can ingest streaming graph information and store the complete graph history. It enables queries to be made over the graphs at different points in that graph's history.
This work in progress was accepted as a Demo and at the Doctoral workshop for DEBS (Distributed and Event-Based Systems). It shows the early development of a system that ingests events and can create (and eventually query) a dynamic graph.
This paper describes a C# library that can be used to build networked programs which can compile to several target hardware and software platforms. This greatly eases development and debugging. The system is tested using NetFPGA as a target and performs almost as well as hand tuned code.
This demo shows how Apple's iBeacon technology can be used to track groups of people who are moving together in a crowd.
This paper is a simulation based study of cloud assisted multi-user video streaming. It is based upon two use cases (one related to video poker the other related to MOOCs). The paper looks at strategies for placing cloud locations to facilitate streaming using Amazon EC2 cloud locations. The paper compares a strategy that dynamically picks new locations for cloud hosts as time goes on. Interestingly this seems to provide little benefit compared with simply having a good initial choice of sites even when users may drop into and out of a cloud chat session over the course of many hours.