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.