I have time-series data which consists of response time histograms. What is a good way to visualize them?

Each histogram consists of usually 5-13 buckets (max 20) on a logarithmic scale, from 1 microsecond through 1 second. (2^0 microseconds through 2^20 microseconds.) What I'm particularly interested in is highlighting cases where there are more entries in the long-latency buckets than usual, even though they are usually a small components of the overall distribution. But I am also interested in seeing what the modal response time is and any changes to the overall shape of the distribution, over time.

Example:

"7": 14, "8": 10834, "9": 6344, "10": 1997, "11": 5016, "12": 6665, "13": 13858, "14": 80563, "15": 202353, "16": 19600, "17": 341, "18": 1118, "19": 1320, "20": 726

Ideas so far:

* Stacked bar per sample. Color-code each bucket to emphasize the "bad" buckets. But, for example, 726 is a very small fraction of 202353 so the height of each bar would need to be logarithmically scaled. Alignment is also difficult to get right--- might want to allow the user to select which bucket "lines up" along the time series.

* Cumulative distribution per sample in a 3-D graph

* Show 'low/medium/high' as three separate graphs or charts stacked together on a common timeline so that they can be scaled independently.

* Show deviations from the overall average, somehow--- maybe a delta per bucket.

While you're at it, you could also pop over and answer David Eppstein's question on 3-D illustration tools. I would probably end up using POV-ray too.

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