Posts about database and analytic technologies applied to the telecommunications industry, especially in call detail record (CDR) applications. Related subjects include:
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:
- There are many kinds of privacy-destroying information. I think people frequently overlook just how many kinds there are.
- Many kinds of organization capture that information, can share it with each other, and gain benefits from eroding or destroying privacy. Similarly, I think people overlook just how pervasive the incentive is to snoop.
- Privacy is invaded through a variety of analytic techniques applied to that information.
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:
- The actual content of your communications – phone calls, email, social media posts and more.
- The metadata of your communications — who you communicate with, when, how long, etc.
- What you read, watch, surf to or otherwise pay attention to.
- Your purchases, sales and other transactions.
- Video images, via stationary cameras, license plate readers in police cars, drones or just ordinary consumer photography.
- Monitoring via the devices you carry, such as phones or medical monitors.
- Your health and physical state, via those devices, but also inferred from, for example, your transactions or search engine entries.
- Your state of mind, which can be inferred to various extents from almost any of the other information areas.
- Your location and movements, ditto. Insurance companies also want to put monitors in cars to track your driving behavior in detail.
|Categories: Health care, Predictive modeling and advanced analytics, Surveillance and privacy, Telecommunications||2 Comments|
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:
- Data about data structure. This is the classical sense of the term. But please note:
- In a relational database, structural metadata is rather separate from the data itself.
- In a document database, each document might carry structure information with it.
- Other inputs to core data management functions. Two major examples are:
- Column statistics that inform RDBMS optimizers.
- Value ranges that inform partition pruning or, more generally, data skipping.
- Inputs to ancillary data management functions — for example, security privileges.
- Support for human decisions about data — for example, information about authorship or lineage.
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:
- Relational databases usually just hold strings — or maybe numbers — with structural information being held elsewhere.
- Some document databases store structural metadata right with the document data itself.
- Some document databases store data in the form of (name, value) pairs. In some cases additional structure is imposed by naming conventions.
- Actual text documents carry the structure imposed by grammar and syntax.
- A lengthy survey of metadata kinds, biased to Hadoop (August, 2012)
- Metadata as derived data (May, 2011)
- Dataset management (May, 2013)
- Structured/unstructured … multi-structured/poly-structured (May, 2011)
|Categories: Data models and architecture, Hadoop, Structured documents, Surveillance and privacy, Telecommunications||5 Comments|
From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:
- Hadoop (always, and please see below).
- Analytic RDBMS (ditto).
- NoSQL and NewSQL.
- Specifically, SQL-on-Hadoop
- Spark and other memory-centric technology, including streaming.
- Public policy, mainly but not only in the area of surveillance/privacy.
- General strategic advice for all sizes of tech company.
Other stuff on my mind includes but is not limited to:
1. Certain categories of buying organizations are inherently leading-edge.
- Internet companies have adopted Hadoop, NoSQL, NewSQL and all that en masse. Often, they won’t even look at things that are conventional or expensive.
- US telecom companies have been buying 1 each of every DBMS on the market since pre-relational days.
- Financial services firms — specifically algorithmic traders and broker-dealers — have been in their own technical world for decades …
- … as have national-security agencies …
- … as have pharmaceutical research departments.
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.
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:
- Teradata is finally seeing substantial interest in in-database modeling, rather than just in-database scoring (which has been important for years) and in-database data preparation (which is a lot like ELT — Extract/Load/transform).
- Teradata is seeing substantial interest in R.
- It seems as if similar groups of customers are interested in both parts of that, such as:
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.
|Categories: EAI, EII, ETL, ELT, ETLT, Parallelization, Predictive modeling and advanced analytics, Revolution Analytics, SAS Institute, Telecommunications, Teradata||1 Comment|
- Stores CDRs (Call Detail Records), many or all of which are collected via …
- … some kind of back door into the AT&T switches that many carriers use. (See Slide 2.)
- Has also included “subscriber information” for AT&T phones since July, 2012.
- Contains “long distance and international” CDRs back to 1987.
- Currently adds 4 billion CDRs per day.
- Is administered by a Federal drug-related law enforcement agency but …
- … is used to combat many non-drug-related crimes as well. (See Slides 21-26.)
Other notes include:
- The agencies specifically mentioned on Slide 16 as making numerous Hemisphere requests are the DEA (Drug Enforcement Agency) and DHS (Department of Homeland Security).
- “Roaming” data giving city/state is mentioned in the deck, but more precise geo-targeting is not.
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
|Categories: Data warehousing, GIS and geospatial, Petabyte-scale data management, Specific users, Surveillance and privacy, Telecommunications||Leave a Comment|
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:
- Hortonworks denies advanced acquisition discussions with either Microsoft and Intel. Of course, that doesn’t exactly contradict the widespread story of Intel having made an acquisition offer.
- As vendors usually do, Hortonworks denies the extreme forms of Cloudera’s suggestion that Hortonworks competitive wins relate to price slashing. But Hortonworks does believe that its license fees often wind up being lower than Cloudera’s, due especially to Hortonworks offering few extra-charge items than Cloudera.
- Hortonworks used a figure of ~75 subscription customers. This does not include OEM sales through, for example, Teradata, Microsoft Azure, or Rackspace. However, that does include …
- … a small number of installations hosted in the cloud — e.g. ~2 on Amazon Web Services — or otherwise remotely. Also, testing in the cloud seems to be fairly frequent, and the cloud can also be a source of data ingested into Hadoop.
- Since Hortonworks a couple of times made it seem that Rackspace was an important partner, behind only Teradata and Microsoft, I finally asked why. Answers boiled down to a Rackspace Hadoop-as-a-service offering, plus joint work to improve Hadoop-on-OpenStack.
- Other Hortonworks reseller partners seem more important in terms of helping customers consumer HDP (Hortonworks Data Platform), rather than for actually doing Hortonworks’ selling for it. (This is unsurprising — channel sales rarely are a path to success for a product that is also appropriately sold by a direct force.)
- Hortonworks listed its major industry sectors as:
- Web and retailing, which it identifies as one thing.
- Health care (various subsectors).
- Financial services, which it called “competitive” in the kind of tone that usually signifies “we lose a lot more than we win, and would love to change that”.
*Speaking of CEO Bearden, an interesting note from Derrick’s piece is that Bearden is quoted as saying “I started this company from day one …”, notwithstanding that the now-departed Eric Baldeschwieler was founding CEO.
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
I’ll start with three observations:
- Computer systems can’t be entirely tightly coupled — nothing would ever get developed or tested.
- Computer systems can’t be entirely loosely coupled — nothing would ever get optimized, in performance and functionality alike.
- In an ongoing trend, there is and will be dramatic refactoring as to which connections wind up being loose or tight.
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
Edit: Please see the comment thread below for updates. Please also see a follow-on post about how the surveillance data is actually used.
US government surveillance has exploded into public consciousness since last Thursday. With one major exception, the news has just confirmed what was already thought or known. So where do we stand?
My views about domestic data collection start:
- I’ve long believed that the Feds — specifically the NSA (National Security Agency) — are storing metadata/traffic data on every telephone call and email in the US. The recent news, for example Senator Feinstein’s responses to the Verizon disclosure, just confirms it. That the Feds sometimes claim this has to be “foreign” data or they won’t look at it hardly undermines my opinion.
- Even private enterprises can more or less straightforwardly buy information about every credit card purchase we make. So of course the Feds can get that as well, as the Wall Street Journal seems to have noticed. More generally, I’d assume the Feds have all the financial data they want, via the IRS if nothing else.
- Similarly, many kinds of social media postings are aggregated for anybody to purchase, or can be scraped by anybody who invests in the equipment and bandwidth. Attensity’s service is just one example.
- I’m guessing that web use data (http requests, search terms, etc.) is not yet routinely harvested by the US government.* Ditto deanonymization of same. I guess that way basically because I’ve heard few rumblings to the contrary. Further, the consumer psychographic profiles that are so valuable to online retailers might be of little help to national security analysts anyway.
- Video surveillance seems likely to grow, from fixed cameras perhaps to drones; note for example the various officials who called for more public cameras after that Boston Marathon bombing. But for the present discussion, that’s of lesser concern to me, simply because it’s done less secretively than other kinds of surveillance. If there’s a camera that can see us, often we can see it too.
*Recall that these comments are US-specific. Data retention legislation has been proposed or passed in multiple countries to require recording of, among other things, all URL requests, with the stated goal of fighting either digital piracy or child pornography.
As for foreign data: Read more
|Categories: Hadoop, HP and Neoview, Petabyte-scale data management, Pricing, Surveillance and privacy, Telecommunications, Text, Vertica Systems, Web analytics||10 Comments|
I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.
1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.
Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.
Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:
- There are vast numbers of companies offering data-management-related technology. They need ways to differentiate.
- Doing analytics at short-request speeds is an obvious data-management-related challenge, and not yet comprehensively addressed.
2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:
- Customer interaction
- Network and sensor monitoring
- Game and mobile application back-ends
Also arising fairly frequently are:
- Algorithmic trading
- Risk measurement
- Law enforcement/national security
- Stakeholder-facing analytics
I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.
Time for another catch-all post. First and saddest — one of the earliest great commenters on this blog, and a beloved figure in the Boston-area database community, was Dan Weinreb, whom I had known since some Symbolics briefings in the early 1980s. He passed away recently, much much much too young. Looking back for a couple of examples — even if you’ve never heard of him before, I see that Dan ‘s 2009 comment on Tokutek is still interesting today, and so is a post on his own blog disagreeing with some of my choices in terminology.
Otherwise, in no particular order:
1. Chris Bird is learning MongoDB. As is common for Chris, his comments are both amusing and enlightening.
2. When I relayed Cloudera’s comments on Hadoop adoption, I left out a couple of categories. One Cloudera called “mobile”; when I probed, that was about HBase, with an example being messaging apps.
The other was “phone home” — i.e., the ingest of machine-generated data from a lot of different devices. This is something that’s obviously been coming for several years — but I’m increasingly getting the sense that it’s actually arrived.
|Categories: Cloudera, Data integration and middleware, Hadoop, HBase, Informatica, Metamarkets and Druid, MongoDB and 10gen, NoSQL, Open source, Telecommunications||2 Comments|