RDF and graphs
Analysis of data management technology optimized for RDF-formatted and/or graph data.
Big Data is Watching You!
There’s a boom in large-scale analytics. The subjects of this analysis may be categorized as:
- People
- Financial trades
- Electronic networks
- Everything else
The most varied, interesting, and valuable of those four categories is the first one.
| Categories: Analytic technologies, Aster Data, Data warehousing, Investment research and trading, Log analysis, MapReduce, RDF and graphs, Specific users, Telecommunications, Web analytics | 3 Comments |
Objectivity Infinite Graph
I chatted Wednesday night with Darren Wood, the Australia-based lead developer of Objectivity’s Infinite Graph database product. Background includes:
- Objectivity is a profitable, decades-old object-oriented DBMS vendor with about 50 employees.
- Like some other object-oriented DBMS of its generation, Objectivity is as much a toolkit for building DBMS as it is a real finished DBMS product. Objectivity sales are typically for custom deals, where Objectivity helps with the programming.
- The way Objectivity works is basically:
- You manage objects in memory, in the format of your choice.
- Objectivity bangs them to disk, across a network.
- Objectivity manages the (distributed) pointers to the objects.
- You can, if you choose, hard code exactly which objects are banged to which node.
- Objectivity’s DML for reading data is very different from Objectivity’s DML for writing data. (I think the latter is more like the program code itself, while the former is more like regular DML.)
- The point of Objectivity is not so much to have fast I/O. Rather, it is to minimize the CPU cost of getting the data that comes across the wire into useful form.
- Darren got the idea of putting a generic graph DBMS front-end on Objectivity while doing a relationship analytics project for an Australian intelligence agency.
- Darren redoubled his efforts to sell the project internally at Objectivity after reading what I wrote about relationship analytics back in 2006 or so.
- There is now a 5 or so person team developing Infinite Graph.
- Infinite Graph is just now going out to beta test.
Infinite Graph is an API or language binding on top of Objectivity that:
- Hides a lot of Objectivity’s complexity.
- Is suitable for graph/relationship analytics.
| Categories: Analytic technologies, Liberty and privacy, Object, Objectivity and Infinite Graph, RDF and graphs | 5 Comments |
The most important part of the “social graph” is neither social nor a graph
“Social graph” is a highly misleading term, and so is “social network analysis.” By this I mean:
There’s something akin to “social graphs” and “social network analysis” that is more or less worthy of all the current hype – but graphs and network analysis are only a minor part of the whole story.
In particular, the most important parts of the Facebook “social graph” are neither social nor a graph. Rather, what’s really important is an aggregate Profile of Revealed Preferences, of which person-to-person connections or other things best modeled by a graph play only a small part.
| Categories: Analytic technologies, Facebook, Games and virtual worlds, Liberty and privacy, RDF and graphs, Web analytics | 7 Comments |
Information found in public-facing social networks
Here are some examples illustrating two recent themes of mine, namely:
- Easily-available information reveals all sorts of things about us.
- Graph-based analysis is on the rise.
Pete Warden scraped all of Facebook’s social graph (at least for the United States), and put up a really interesting-looking visualization of same. Facebook’s lawyer’s came down on him, and he quickly agreed to destroy the data he’d scraped, but also published ideas on how other people could duplicate his work.
Warden has since given an interview in which he outlines some of the things researchers hoped to do with this data: Read more
| Categories: Analytic technologies, Facebook, Liberty and privacy, RDF and graphs | 1 Comment |
Notes on the evolution of OLTP database management systems
The past few years have seen a spate of startups in the analytic DBMS business. Netezza, Vertica, Greenplum, Aster Data and others are all reasonably prosperous, alongside older specialty product vendors Teradata and Sybase (the Sybase IQ part). OLTP (OnLine Transaction Processing) and general purpose DBMS startups, however, have not yet done as well, with such success as there has been (MySQL, Intersystems Cache’, solidDB’s exit, etc.) generally accruing to products that originated in the 20th Century.
Nonetheless, OLTP/general-purpose data management startup activity has recently picked up, targeting what I see as some very real opportunities and needs. So as a jumping-off point for further writing, I thought it might be interesting to collect a few observations about the market in one place. These include:
- Big-brand OLTP/general-purpose DBMS have more “stickiness” than analytic DBMS.
- By number, most of an enterprise’s OLTP/general-purpose databases are low-volume and low-value.
- Most interesting new OLTP/general-purpose data management products are either MySQL-based or NoSQL.
- It’s not yet clear whether MySQL will prevail over MySQL forks, or vice-versa, or whether they will co-exist.
- The era of silicon-centric relational DBMS is coming.
- The emphasis on scale-out and reducing the cost of joins spans the NoSQL and SQL-based worlds.
- Users’ instance on “free” could be a major problem for OLTP DBMS innovation.
I shall explain. Read more
Toward a NoSQL taxonomy
I talked Friday with Dwight Merriman, founder of 10gen (the MongoDB company). He more or less convinced me of his definition of NoSQL systems, which in my adaptation goes:
NoSQL = HVSP (High Volume Simple Processing) without joins or explicit transactions
Within that realm, Dwight offered a two-part taxonomy of NoSQL systems, according to their data model and replication/sharding strategy. I’d be happier, however, with at least three parts to the taxonomy:
- How data looks logically on a single node
- How data is stored physically on a single node
- How data is distributed, replicated, and reconciled across multiple nodes, and whether applications have to be aware of how the data is partitioned among nodes/shards. Read more
| Categories: Cassandra, Data models and architecture, NoSQL, Parallelization, RDF and graphs, Structured documents, Theory and architecture | 11 Comments |
Some NoSQL links
I plan to post a few things soon about MongoDB, Cassandra, and NoSQL in general. So I’m poking around a bit reading stuff on the subjects. Here are some links I found. Read more
| Categories: Amazon and its cloud, Cassandra, Continuent, Google, MySQL, NoSQL, Open source, RDF and graphs, Tokutek | 5 Comments |
Aster Data nCluster 4.5
Like Vertica, Netezza, and Teradata, Aster is using this week to pre-announce a forthcoming product release, Aster Data nCluster 4.5. Aster is really hanging its identity on “Big Data Analytics” or some variant of that concept, and so the two major named parts of Aster nCluster 4.5 are:
- Aster Data Analytic Foundation, a set of analytic packages prebuilt in Aster’s SQL-MapReduce
- Aster Data Developer Express, an Eclipse-based IDE (Integrated Development Environment) for developing and testing applications built on Aster nCluster, Aster SQL-MapReduce, and Aster Data Analytic Foundation
And in other Aster news:
- Along with the development GUI in Aster nCluster 4.5, there is also a new administrative GUI.
- Aster has certified that nCluster works with Fusion I/O boards, because at least one retail industry prospect cares. However, that in no way means that arm’s-length Fusion I/O certification is Aster’s ultimate solid-state memory strategy.
- I had the wrong impression about how far Aster/SAS integration has gotten. So far, it’s just at the connector level.
Aster Data Developer Express evidently does some cool stuff, like providing some sort of parallelism testing right on your desktop. It also generates lots of stub code, saving humans from the tedium of doing that. Useful, obviously.
But mainly, I want to write about the analytic packages. Read more
| Categories: Analytic technologies, Aster Data, Data warehousing, Investment research and trading, RDF and graphs, SAS Institute, Teradata | 2 Comments |
Open issues in database and analytic technology
The last part of my New England Database Summit talk was on open issues in database and analytic technology. This was closely intertwined with the previous section, and also relied on a lot that I’ve posted here. So I’ll just put up a few notes on that part, with lots of linkage to prior discussion of the same points. Read more
Webinar on MapReduce for complex analytics (Thursday, December 3, 10 am and 2 pm Eastern)
The second in my two-webinar series for Aster Data will occur tomorrow, twice (both live), at 10 am and 2 pm Eastern time. The other presenters will be Jonathan Goldman, who was a Principal Scientist at LinkedIn but now has joined Aster himself, and Steve Wooledge of Aster (playing host). Key links are:
- Registration for tomorrow’s webinars
- Replay of the first webinar
- My slides from the first webinar
The main subjects of the webinar will be:
- Some review of material from the first webinar (all three presenters)
- Discussion of how MapReduce can help with three kinds of analytics:
- Pattern matching (Jonathan will give detail)
- Number-crunching (I’ll cover that, and it will be short)
- Graph analytics (I haven’t written the slides yet, but my starting point will be some of the relationship analytics ideas we discussed in August)
Arguably, aspects of data transformation fit into each of those three categories, which may help explain why data transformation has been so prominent among the early applications of MapReduce.
As you can see from Aster’s title for the webinar (which they picked while I was on vacation), at least their portion will be focused on customer analytics, e.g. web analytics.