Telecommunications
Posts about database and analytic technologies applied to the telecommunications industry, especially in call detail record (CDR) applications. Related subjects include:
Where things stand in US government surveillance
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 |
Analytic application themes
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
- Anti-fraud
- Risk measurement
- Law enforcement/national security
- Healthcare
- 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.
Notes and comments — October 31, 2012
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, NoSQL, Open source, Telecommunications | 2 Comments |
Notes on Hadoop adoption and trends
With Strata/Hadoop World being next week, there is much Hadoop discussion. One theme of the season is BI over Hadoop. I have at least 5 clients claiming they’re uniquely positioned to support that (most of whom partner with a 6th client, Tableau); the first 2 whose offerings I’ve actually written about are Teradata Aster and Hadapt. More generally, I’m hearing “Using Hadoop is hard; we’re here to make it easier for you.”
If enterprises aren’t yet happily running business intelligence against Hadoop, what are they doing with it instead? I took the opportunity to ask Cloudera, whose answers didn’t contradict anything I’m hearing elsewhere. As Cloudera tells it (approximately — this part of the conversation* was rushed): Read more
Notes on Hadoop adoption
I successfully resisted telephone consulting while on vacation, but I did do some by email. One was on the oft-recurring subject of Hadoop adoption. I think it’s OK to adapt some of that into a post.
Notes on past and current Hadoop adoption include:
- Enterprise Hadoop adoption is for experimental uses or departmental production (as opposed to serious enterprise-level production). Indeed, it’s rather tough to disambiguate those two. If an enterprise uses Hadoop to search for new insights and gets a few, is that an experiment that went well, or is it production?
- One of the core internet-business use cases for Hadoop is a many-step ETL, ELT, and data refinement pipeline, with Hadoop executing some or many of the steps. But I don’t think that’s in production at many enterprises yet, except in the usual forward-leaning sectors of financial services and (we’re all guessing) national intelligence.
- In terms of industry adoption:
- Financial services on the investment/trading side are all over Hadoop, just as they’re all over any technology. Ditto national intelligence, one thinks.
- Consumer financial services, especially credit card, are giving Hadoop a try too, for marketing and/or anti-fraud.
- I’m sure there’s some telecom usage, but I’m hearing of less than I thought I would. Perhaps this is because telcos have spent so long optimizing their data into short, structured records.
- Whatever consumer financial services firms do, retailers do too, albeit with smaller budgets.
Thoughts on how Hadoop adoption will look going forward include: Read more
| Categories: Cloud computing, Data warehouse appliances, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, Investment research and trading, Telecommunications | 3 Comments |
Introduction to Neo Technology and Neo4j
I’ve been talking some with the Neo Technology/Neo4j guys, including Emil Eifrem (CEO/cofounder), Johan Svensson (CTO/cofounder), and Philip Rathle (Senior Director of Products). Basics include:
- Neo Technology came up with Neo4j, open sourced it, and is building a company around the open source core product in the usual way.
- Neo4j is a graph DBMS.
- Neo4j is unlike some other graph DBMS in that:
- Neo4j is designed for OLTP (OnLine Transaction Processing), or at least as a general-purpose DBMS, rather than being focused on investigative analytics.
- To every node or edge managed by Neo4j you can associate an arbitrary collection of (name,value) pairs — i.e., what might be called a document.
Numbers and historical facts include:
- > 50 paying Neo4j customers.
- Estimated 1000s of production Neo4j users of open source version.*
- Estimated 1/3 of paying customers and free users using Neo4j as a “system of record”.
- >30,000 downloads/month, in some sense of “download”.
- 35 people in 6 countries, vs. 25 last December.
- $13 million in VC, most of it last October.
- Started in 2000 as the underpinnings for a content management system.
- A version of the technology in production in 2003.
- Neo4j first open-sourced in 2007.
- Big-name customers including Cisco, Adobe, and Deutsche Telekom.
- Pricing of either $6,000 or $24,000 per JVM per year for two different commercial versions.
| Categories: Market share and customer counts, Neo Technology and Neo4j, Open source, Pricing, RDF and graphs, Structured documents, Telecommunications | 11 Comments |
Quick-turnaround predictive modeling
Last November, I wrote two posts on agile predictive analytics. It’s time to return to the subject. I’m used to KXEN talking about the ability to do predictive modeling, very quickly, perhaps without professional statisticians; that the core of what KXEN does. But I was surprised when Revolution Analytics told me a similar story, based on a different approach, because ordinarily that’s not how R is used at all.
Ultimately, there seem to be three reasons why you’d want quick turnaround on your predictive modeling: Read more
| Categories: Business intelligence, Investment research and trading, KXEN, Predictive modeling and advanced analytics, Revolution Analytics, Telecommunications, Web analytics | 10 Comments |
Cool analytic stories
There are several reasons it’s hard to confirm great analytic user stories. First, there aren’t as many jaw-dropping use cases as one might think. For as I wrote about performance, new technology tends to make things better, but not radically so. After all, if its applications are …
… all that bloody important, then probably people have already been making do to get it done as best they can, even in an inferior way.
Further, some of the best stories are hard to confirm; even the famed beer/diapers story isn’t really true. Many application areas are hard to nail down due to confidentiality, especially but not only in such “adversarial” domains as anti-terrorism, anti-spam, or anti-fraud.
Even so, I have two questions in my inbox that boil down to “What are the coolest or most significant analytics stories out there?” So let’s round up some of what I know. Read more
| Categories: Analytic technologies, Google, Health care, Investment research and trading, Predictive modeling and advanced analytics, Scientific research, Telecommunications, Web analytics | 6 Comments |
Notes on the analysis of large graphs
This post is part of a series on managing and analyzing graph data. Posts to date include:
- Graph data model basics
- Relationship analytics definition
- Relationship analytics applications
- Analysis of large graphs (this post)
My series on graph data management and analytics got knocked off-stride by our website difficulties. Still, I want to return to one interesting set of issues — analyzing large graphs, specifically ones that don’t fit comfortably into RAM on a single server. By no means do I have the subject figured out. But here are a few notes on the matter.
How big can a graph be? That of course depends on:
- The number of nodes. If the nodes of a graph are people, there’s an obvious upper bound on the node count. Even if you include their houses, cars, and so on, you’re probably capped in the range of 10 billion.
- The number of edges. (Even more important than the number of nodes.) If every phone call, email, or text message in the world is an edge, that’s a lot of edges.
- The typical size of a (node, edge, node) triple. I don’t know why you’d have to go much over 100 bytes post-compression*, but maybe I’m overlooking something.
*Even if your graph has 10 billion nodes, those can be tokenized in 34 bits, so the main concern is edges. Edges can include weights, timestamps, and so on, but how many specifics do you really need? At some point you can surely rely on a pointer to full detail stored elsewhere.
The biggest graph-size estimates I’ve gotten are from my clients at Yarcdata, a division of Cray. (“Yarc” is “Cray” spelled backwards.) To my surprise, they suggested that graphs about people could have 1000s of edges per node, whether in:
- An intelligence scenario, perhaps with billions of nodes and hence trillions of edges.
- A telecom user-analysis case, with perhaps 100 million nodes and hence 100s of billions of edges.
Yarcdata further suggested that bioinformatics use cases could have node counts higher yet, characterizing Bio2RDF as one of the “smaller” ones at 22 billion nodes. In these cases, the nodes/edge average seems lower than in people-analysis graphs, but we’re still talking about 100s of billions of edges.
Recalling that relationship analytics boils down to finding paths and subgraphs, the naive relational approach to such tasks would be: Read more
| Categories: Analytic technologies, Aster Data, Data models and architecture, Hadoop, Health care, MapReduce, RDF and graphs, Scientific research, Telecommunications, Yarcdata and Cray | 20 Comments |
Relationship analytics application notes
This post is part of a series on managing and analyzing graph data. Posts to date include:
- Graph data model basics
- Relationship analytics definition
- Relationship analytics applications (this post)
- Analysis of large graphs
In my recent post on graph data models, I cited various application categories for relationship analytics. For most applications, it’s hard to get a lot of details. Reasons include:
- In adversarial domains such as national security, anti-fraud, or search engine ranking, it’s natural to keep algorithms secret.
- The big exception — influencer analytics, aka social network analysis — is obscured by a major hype/reality gap (so, come to think of it, is a lot of other predictive modeling).
Even so, it’s fairly safe to say:
- Much of relationship analytics is about subgraph pattern matching.
- Much of relationship analytics is about identifying subgraph patterns that are predictive of certain characteristics or outcomes.
- An important kind of relationship analytics challenge is to identify influential individuals.