Web analytics
Discussion of how data warehousing and analytic technologies are applied to clickstream analysis and other web analytics challenges. Related subjects include:
- The use of analytic technologies for logfile analysis
- (in Text Technologies) Online marketing
Eight kinds of analytic database (Part 2)
In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear. Read more
Eight kinds of analytic database (Part 1)
Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.
Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning. Read more
Citrusleaf RTA
Citrusleaf has released an add-on product called Citrusleaf RTA (Real-Time Attribution). It’s to be used when:
- You want to update dashboards within a minute.
- You want to update predictive models fairly quickly (within the hour?), although it’s not clear to me how much the models are being updated or changed with that latency.
The metrics envisioned are:
- 100 or so ad impressions per person …
- … for 1 billion or so people …
- … stored for 30-90 days …
- … where each ad impression is a fairly short record …
- … stored on disk …
- … but indexed in a way so that the index can fit into RAM.
- 50-100,000 writes per second. (I didn’t ask on what amount of hardware.)
- Several hundred reads per second.
A consistent relational schema is NOT assumed.
Citrusleaf’s solution is:
- Have one index entry for each of the 1 billion people.
- Bang each new object/record to disk. Include in it a pointer to the previous object/record for the same person.
- Each time a new object/record is added, update the index in place so that it now points to the new once. Hence, the index is sized according to the number of people, not according to the total number of objects/records.
- Eventually let objects/records age off in the obvious way.
The downside is that when you do read 100 objects/records per person, you might need to do 100 seeks.
Columnar DBMS vendor customer metrics
Last April, I asked some columnar DBMS vendors to share customer metrics. They answered, but it took until now to iron out a couple of details. Overall, the answers are pretty impressive. Read more
Infobright 4.0
Infobright is announcing its 4.0 release, with imminent availability. In marketing and product alike, Infobright is betting the farm on machine-generated data. This hasn’t been Infobright’s strategy from the getgo, but it is these days, with pretty good focus and commitment. While some fraction of Infobright’s customer base is in the Sybase-IQ-like data mart market — and indeed Infobright put out a customer-win press release in that market a few days ago — Infobright’s current customer targets seem to be mainly:
- Web companies, many of which are already MySQL users.
- Telecommunication and similar log data, especially in OEM relationships.
- Trading/financial services, especially at mid-tier companies.
Key aspects of Infobright 4.0 include: Read more
| Categories: Data warehousing, Database compression, Infobright, Investment research and trading, Log analysis, Open source, Telecommunications, Web analytics | 8 Comments |
The essence of an application
Once upon a time, information technology was strictly about — well, information. And by “information” what was meant was “data”.* An application boiled down to a database design, plus a straightforward user interface, in whatever the best UI technology of the day happened to be. Things rarely worked quite as smoothly as the design-database/press-button/generate-UI propaganda would have one believe, but database design was clearly at the center of application invention.
*Not coincidentally, two of the oldest names for “IT” were data processing and management information systems.
Eventually, there came to be three views of the essence of IT:
- Data — i.e., the traditional view, still exemplified by IBM and Oracle.
- People empowerment — i.e., Microsoft-style emphasis on UI friendliness and efficiency.
- Operational workflow — i.e., SAP-style emphasis on actual business processes.
Graphical user interfaces were a major enabling technology for that evolution. Equally important, relational databases made some difficult problems easy(ier), freeing application designers to pursue more advanced functionality.
Based on further technical evolution, specifically in analytic and consumer technologies, I think we should now take that list up to five. The new members I propose are:
- Investigative analytics.
- Emotional response.
| Categories: Data warehousing, Facebook, Predictive modeling and advanced analytics, Theory and architecture, Web analytics | 1 Comment |
Notes on short-request scale-out MySQL
A press person recently asked about:
… start-ups that are building technologies to enable MySQL and other SQL databases to get over some of the problems they have in scaling past a certain size. … I’d like to get a sense as to whether or not the problems are as severe and wide spread as these companies are telling me? If so, why wouldn’t a customer just move to a new database?
While that sounds as if he was asking about scale-out relational DBMS in general, MySQL or otherwise, short-request or analytic, it turned out that he was asking just about short-request scale-out MySQL. My thoughts and comments on that narrower subject include(d) but are not limited to: Read more
The technology of privacy threats
This post is the second of a series. The first one was an overview of privacy dangers, replete with specific examples of kinds of data that are stored for good reasons, but can also be repurposed for more questionable uses. More on this subject may be found in my August, 2010 post Big Data is Watching You!
There are two technology trends driving electronic privacy threats. Taken together, these trends raise scenarios such as the following:
- Your web surfing behavior indicates you’re a sports car buff, and you further like to look at pictures of scantily-clad young women. A number of your Facebook friends are single women. As a result, you’re deemed a risk to have a mid-life crisis and divorce your wife, thus increasing the interest rate you have to pay when refinancing your house.
- Your cell phone GPS indicates that you drive everywhere, instead of walking. There is no evidence of you pursuing fitness activities, but forum posting activity suggests you’re highly interested in several TV series. Your credit card bills show that your taste in restaurant food tends to the fatty. Your online photos make you look fairly obese, and a couple have ashtrays in them. As a result, you’re judged a high risk of heart attack, and your medical insurance rates are jacked up accordingly.
- You did actually have that mid-life crisis and get divorced. At the child-custody hearing, your ex-spouse’s lawyer quotes a study showing that football-loving upper income Republicans are 27% more likely to beat their children than yoga-class-attending moderate Democrats, and the probability goes up another 8% if they ever bought a jersey featuring a defensive lineman. What’s more, several of the more influential people in your network of friends also fit angry-male patterns, taking the probability of abuse up another 13%. Because of the sound statistics behind such analyses, the judge listens.
Not all these stories are quite possible today, but they aren’t far off either.
| Categories: Facebook, Predictive modeling and advanced analytics, Surveillance and privacy, Telecommunications, Web analytics | 4 Comments |
Privacy dangers — an overview
This post is the first of a series. The second one delves into the technology behind the most serious electronic privacy threats.
The privacy discussion has gotten more active, and more complicated as well. A year ago, I still struggled to get people to pay attention to privacy concerns at all, at least in the United States, with my first public breakthrough coming at the end of January. But much has changed since then.
On the commercial side, Facebook modified its privacy policies, garnering great press attention and an intense user backlash, leading to a quick partial retreat. The Wall Street Journal then launched a long series of articles — 13 so far — recounting multiple kinds of privacy threats. Other media joined in, from Forbes to CNet. Various forms of US government rule-making to inhibit advertising-related tracking have been proposed as an apparent result.
In the US, the government had a lively year as well. The Transportation Security Administration (TSA) rolled out what have been dubbed “porn scanners,” and backed them up with “enhanced patdowns.” For somebody who is, for example, female, young, a sex abuse survivor, and/or a follower of certain religions, those can be highly unpleasant, if not traumatic. Meanwhile, the Wikileaks/Cablegate events have spawned a government reaction whose scope is only beginning to be seen. A couple of “highlights” so far are some very nasty laptop seizures, and the recent demand for information on over 600,000 Twitter accounts. (Christopher Soghoian provided a detailed, nuanced legal analysis of same.)
At this point, it’s fair to say there are at least six different kinds of legitimate privacy fear. Read more
| Categories: Analytic technologies, Facebook, GIS and geospatial, Health care, Surveillance and privacy, Telecommunications, Web analytics | 6 Comments |
The privacy discussion is heating up
Internet privacy issues are getting more and more attention. Frankly, I think we’re getting past the point where the only big risk is loss of liberty. More and more, the risk of an excessive backlash is upon us as well. (In the medical area, I’d say it’s already more than a risk — it’s a life-wrecking reality. But now the problem is poised to become wider-spread.) Read more
| Categories: Health care, Surveillance and privacy, Web analytics | 2 Comments |