"errors" entries

Four short links: 1 July 2015

Four short links: 1 July 2015

Recovering from Debacle, Open IRS Data, Time Series Requirements, and Error Messages

  1. Google Dev Apologies After Photos App Tags Black People as Gorillas (Ars Technica) — this is how you recover from a unequivocally horrendous mistake.
  2. IRS Finally Agrees to Release Non-Profit Records (BoingBoing) — Today, the IRS released a statement saying they’re going to do what we’ve been hoping for, saying they are going to release e-file data and this is a “priority for the IRS.” Only took $217,000 in billable lawyer hours (pro bono, thank goodness) to get there.
  3. Time Series Database Requirements — classic paper, laying out why time-series databases are so damn weird. Their access patterns are so unique because of the way data is over-gathered and pushed ASAP to the store. It’s mostly recent, mostly never useful, and mostly needed in order. (via Thoughts on Time-Series Databases)
  4. Compiler Errors for Humans — it’s so important, and generally underbaked in languages. A decade or more ago, I was appalled by Python’s errors after Perl’s very useful messages. Today, appreciating Go’s generally handy errors. How a system handles the operational failures that will inevitably occur is part and parcel of its UX.
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Four short links: 3 February 2015

Four short links: 3 February 2015

Product Trends, Writing Code, Simple Testing, and Quantum Gotchas

  1. Frog Design Predictions (Wired) — designers pick product trends, arrayed from probable to speculative.
  2. Making Wrong Code Look Wrong (Joel Spolsky) — This makes mistakes even more visible. Your eyes will learn to “see” smelly code, and this will help you find obscure security bugs just through the normal process of writing code and reading code.
  3. Simple Testing Can Prevent Most Critical FailuresWe found the majority of catastrophic failures could easily have been prevented by performing simple testing on error handling code – the last line of defense – even without an understanding of the software design. We extracted three simple rules from the bugs that have lead to some of the catastrophic failures, and developed a static [Java] checker, Aspirator, capable of locating these bugs. One of the tests is a FIXME or TODO in an exception handler.
  4. Quantum Machine Learning Algorithms: Read the Fine Print (Scott Aaronson) — In the years since HHL, quantum algorithms achieving “exponential speedups over classical algorithms” have been proposed for other major application areas […]. With each of them, one faces the problem of how to load a large amount of classical data into a quantum computer (or else compute the data “on-the-fly”), in a way that is efficient enough to preserve the quantum speedup.
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New approaches to anomaly detection

A practical example of how anomaly detection makes complex data problems easier to solve.

Dots

As new tools for distributed storage and analysis of big data are becoming more stable and widely known, there is a growing need for discovering best practices for analytics at this scale. One of the areas of widespread interest that crosses many verticals is anomaly detection.

At its best, anomaly detection is used to find unusual, rarely occurring events or data for which little is known in advance. Examples include changes in sensor data reported for a variety of parameters, suspicious behavior on secure websites, or unexpected changes in web traffic. In some cases, the data patterns being examined are simple and regular and, thus, fairly easy to model.

Anomaly detection approaches start with some essential but sometimes overlooked ideas about anomalies:

  • Anomalies are defined not by their own characteristics but in contrast to what is normal.

Thus …

  • Before you can spot an anomaly, you first have to figure out what “normal” actually is.

This need to first discover what is considered “normal” may seem obvious, but it is not always obvious how to do it, especially in situations with complicated patterns of behavior. Best results are achieved when you use statistical methods to build an adaptive model of events in the system you are analyzing as a first step toward discovering anomalous behavior. Read more…

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