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                      Telecommunications

                      Posts about database and analytic technologies applied to the telecommunications industry, especially in call detail record (CDR) applications. Related subjects include:

                      March 5, 2015

                      Cask and CDAP

                      For starters:

                      Also:

                      So far as I can tell:

                      Read more

                      December 31, 2014

                      Notes on machine-generated data, year-end 2014

                      Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.

                      1. There are many kinds of machine-generated data. Important categories include:

                      That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.

                      2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: Read more

                      December 10, 2014

                      A few numbers from MapR

                      MapR put out a press release aggregating some customer information; unfortunately, the release is a monument to vagueness. Let me start by saying:

                      Anyhow, the key statement in the MapR release is:

                      … the number of companies that have a paid subscription for MapR now exceeds 700.

                      Unfortunately, that includes OEM customers as well as direct ones; I imagine MapR’s direct customer count is much lower.

                      In one gesture to numerical conservatism, MapR did indicate by email that it counts by overall customer organization, not by department/cluster/contract (i.e., not the way Hortonworks does). Read more

                      September 15, 2014

                      Misconceptions about privacy and surveillance

                      Everybody is confused about privacy and surveillance. So I’m renewing my efforts to consciousness-raise within the tech community. For if we don’t figure out and explain the issues clearly enough, there isn’t a snowball’s chance in Hades our lawmakers will get it right without us.

                      How bad is the confusion? Well, even Edward Snowden is getting it wrong. A Wired interview with Snowden says:

                      “If somebody’s really watching me, they’ve got a team of guys whose job is just to hack me,” he says. “I don’t think they’ve geolocated me, but they almost certainly monitor who I’m talking to online. Even if they don’t know what you’re saying, because it’s encrypted, they can still get a lot from who you’re talking to and when you’re talking to them.”

                      That is surely correct. But the same article also says:

                      “We have the means and we have the technology to end mass surveillance without any legislative action at all, without any policy changes.” The answer, he says, is robust encryption. “By basically adopting changes like making encryption a universal standard—where all communications are encrypted by default—we can end mass surveillance not just in the United States but around the world.”

                      That is false, for a myriad of reasons, and indeed is contradicted by the first excerpt I cited.

                      What privacy/surveillance commentators evidently keep forgetting is:

                      So closing down a few vectors of privacy attack doesn’t solve the underlying problem at all.

                      Worst of all, commentators forget that the correct metric for danger is not just harmful information use, but chilling effects on the exercise of ordinary liberties. But in the interest of space, I won’t reiterate that argument in this post.

                      Perhaps I can refresh your memory why each of those bulleted claims is correct. Major categories of privacy-destroying information (raw or derived) include:

                      Read more

                      February 23, 2014

                      Confusion about metadata

                      A couple of points that arise frequently in conversation, but that I don’t seem to have made clearly online.

                      “Metadata” is generally defined as “data about data”. That’s basically correct, but it’s easy to forget how many different kinds of metadata there are. My list of metadata kinds starts with:

                      What’s worse, the past year’s most famous example of “metadata”, telephone call metadata, is misnamed. This so-called metadata, much loved by the NSA (National Security Agency), is just data, e.g. in the format of a CDR (Call Detail Record). Calling it metadata implies that it describes other data — the actual contents of the phone calls — that the NSA strenuously asserts don’t actually exist.

                      And finally, the first bullet point above has a counter-intuitive consequence — all common terminology notwithstanding, relational data is less structured than document data. Reasons include:

                      Related links

                      February 2, 2014

                      Some stuff I’m thinking about (early 2014)

                      From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:

                      Other stuff on my mind includes but is not limited to:

                      1. Certain categories of buying organizations are inherently leading-edge.

                      Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.

                      Read more

                      September 20, 2013

                      Trends in predictive modeling

                      I talked with Teradata about a bunch of stuff yesterday, including this week’s announcements in in-database predictive modeling. The specific news was about partnerships with Fuzzy Logix and Revolution Analytics. But what I found more interesting was the surrounding discussion. In a nutshell:

                      This is the strongest statement of perceived demand for in-database modeling I’ve heard. (Compare Point #3 of my July predictive modeling post.) And fits with what I’ve been hearing about R.

                      Read more

                      September 3, 2013

                      The Hemisphere program

                      Another surveillance slide deck has emerged, as reported by the New York Times and other media outlets. This one is for the Hemisphere program, which apparently:

                      Other notes include:

                      I’ve never gotten a single consistent figure, but typical CDR size seems to be in the 100s of bytes range. So I conjecture that Project Hemisphere spawned one of the first petabyte-scale databases ever.

                      Hemisphere Project unknowns start:? Read more

                      August 24, 2013

                      Hortonworks business notes

                      Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes? from my call — for which Rob Bearden didn’t bother showing up — include, in no particular order:

                      In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social.? Read more

                      July 20, 2013

                      The refactoring of everything

                      I’ll start with three observations:

                      As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more

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