Application areas

Posts focusing on the use of database and analytic technologies in specific application domains. Related subjects include:

January 27, 2015

Soft robots, Part 2 — implications

What will soft, mobile robots be able to do that previous generations cannot? A lot. But I’m particularly intrigued by two large categories:

There are still many things that are hard for humans to keep in good working order, including:

Sometimes the issue is (hopefully minor) repairs. Sometimes it’s cleaning or lubrication. In some cases one might want to upgrade a structure with fixed sensors, and the “repair” is mainly putting those sensors in place. In all these cases, it seems that soft robots could eventually offer a solution. Further examples, I’m sure, could be found in factories, mines, or farms.

Of course, if there’s a maintenance/repair need, inspection is at least part of the challenge; in some cases it’s almost the whole thing. And so this technology will help lead us toward the point that substantially all major objects will be associated with consistent flows of data. Opportunities for data analysis will abound.

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 12, 2014

Notes and links, December 12, 2014

1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.

For starters:

I’ve also heard a couple of ideas about how predictive modeling can support BI. One is via my client Omer Trajman, whose startup ScalingData is still semi-stealthy, but says they’re “working at the intersection of big data and IT operations”. The idea goes something like this:

Makes sense to me.

* The word “cluster” could have been used here in a couple of different ways, so I decided to avoid it altogether.

Finally, I’m hearing a variety of “smart ETL/data preparation” and “we recommend what columns you should join” stories. I don’t know how much machine learning there’s been in those to date, but it’s usually at least on the roadmap to make the systems (yet) smarter in the future. The end benefit is usually to facilitate BI.

2. Discussion of graph DBMS can get confusing. For example: 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

October 26, 2014

Datameer at the time of Datameer 5.0

Datameer checked in, having recently announced general availability of Datameer 5.0. So far as I understood, Datameer is still clearly in the investigative analytics business, in that:

Key aspects include:

Read more

October 10, 2014

Notes on predictive modeling, October 10, 2014

As planned, I’m getting more active in predictive modeling. Anyhow …

1. I still believe most of what I said in a July, 2013 predictive modeling catch-all post. However, I haven’t heard as much subsequently about Ayasdi as I had expected to.

2. The most controversial part of that post was probably the claim:

I think the predictive modeling state of the art has become:

  • Cluster in some way.
  • Model separately on each cluster.

In particular:

3. Nutonian is now a client. I just had my first meeting with them this week. To a first approximation, they’re somewhat like KXEN (sophisticated math, non-linear models, ease of modeling, quasi-automagic feature selection), but with differences that start: Read more

October 5, 2014

Streaming for Hadoop

The genesis of this post is that:

Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.

In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more

September 28, 2014

Some stuff on my mind, September 28, 2014

1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?

2. A few thoughts on cloud, colocation, etc.:

3. As for the analytic DBMS industry: Read more

September 21, 2014

Data as an asset

We all tend to assume that data is a great and glorious asset. How solid is this assumption?

*”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.

This all raises the idea – if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include:  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

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