RDF and graphs
Analysis of data management technology optimized for RDF-formatted and/or graph data.
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
7-10 years ago, I repeatedly argued the viewpoints:
- Relational DBMS were the right choice in most cases.
- Multiple kinds of relational DBMS were needed, optimized for different kinds of use case.
- There were a variety of specialized use cases in which non-relational data models were best.
Since then, however:
- Hadoop has flourished.
- NoSQL has flourished.
- Graph DBMS have matured somewhat.
- Much of the action has shifted to machine-generated data, of which there are many kinds.
So it’s probably best to revisit all that in a somewhat organized way.
I hoped to write a reasonable overview of current- to medium-term future IT innovation. Yeah, right. But if we abandon any hope that this post could be comprehensive, I can at least say:
1. Back in 2011, I ranted against the term Big Data, but expressed more fondness for the V words — Volume, Velocity, Variety and Variability. That said, when it comes to data management and movement, solutions to the V problems have generally been sketched out.
- Volume has been solved. There are Hadoop installations with 100s of petabytes of data, analytic RDBMS with 10s of petabytes, general-purpose Exadata sites with petabytes, and 10s/100s of petabytes of analytic Accumulo at the NSA. Further examples abound.
- Velocity is being solved. My recent post on Hadoop-based streaming suggests how. In other use cases, velocity is addressed via memory-centric RDBMS.
- Variety and Variability have been solved. MongoDB, Cassandra and perhaps others are strong NoSQL choices. Schema-on-need is in earlier days, but may help too.
2. Even so, there’s much room for innovation around data movement and management. I’d start with:
- Product maturity is a huge issue for all the above, and will remain one for years.
- Hadoop and Spark show that application execution engines:
- Have a lot of innovation ahead of them.
- Are tightly entwined with data management, and with data movement as well.
- Hadoop is due for another refactoring, focused on both in-memory and persistent storage.
- There are many issues in storage that can affect data technologies as well, including but not limited to:
- Solid-state (flash or post-flash) vs. spinning disk.
- Networked vs. direct-attached.
- Virtualized vs. identifiable-physical.
- Graph analytics and data management are still confused.
1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.
- The predictive experimentation I wrote about over Thanksgiving calls naturally for some BI/dashboarding to monitor how it’s going.
- If you think about Nutonian’s pitch, it can be approximated as “Root-cause analysis so easy a business analyst can do it.” That could be interesting to jump to after BI has turned up anomalies. And it should be pretty easy to whip up a UI for choosing a data set and objective function to model on, since those are both things that the BI tool would know how to get to anyway.
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:
- Suppose we have lots of logs about lots of things.* Machine learning can help:
- Notice what’s an anomaly.
- Group* together things that seem to be experiencing similar anomalies.
- That can inform a BI-plus interface for a human to figure out what is happening.
Makes sense to me. (Edit: ScalingData subsequently launched, under the name Rocana.)
* 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
|Categories: Business intelligence, Greenplum, Hadoop, Hortonworks, Log analysis, Neo Technology and Neo4j, Nutonian, Predictive modeling and advanced analytics, RDF and graphs, WibiData||4 Comments|
I’ve heard a lot of buzz recently around Spark. So I caught up with Ion Stoica and Mike Franklin for a call. Let me start by acknowledging some sources of confusion.
- Spark is very new. All Spark adoption is recent.
- Databricks was founded to commercialize Spark. It is very much in stealth mode …
- … except insofar as Databricks folks are going out and trying to drum up Spark adoption.
- Ion Stoica is running Databricks, but you couldn’t tell that from his UC Berkeley bio page. Edit: After I posted this, Ion’s bio was quickly updated.
- Spark creator and Databricks CTO Matei Zaharia is an MIT professor, but actually went on leave there before he ever showed up.
- Cloudera is perhaps Spark’s most visible supporter. But Cloudera’s views of Spark’s role in the world is different from the Spark team’s.
The “What is Spark?” question may soon be just as difficult as the ever-popular “What is Hadoop?” That said — and referring back to my original technical post about Spark and also to a discussion of prominent Spark user ClearStory — my try at “What is Spark?” goes something like this:
- Spark is a distributed execution engine for analytic processes …
- … which works well with Hadoop.
- Spark is distinguished by a flexible in-memory data model …
- … and farms out persistence to HDFS (Hadoop Distributed File System) or other existing data stores.
- Intended analytic use cases for Spark include:
- SQL data manipulation.
- ETL-like data manipulation.
- Streaming-like data manipulation.
- Machine learning.
- Graph analytics.
Teradata Aster 6 has been preannounced (beta in Q4, general release in Q1 2014). The general architectural idea is:
- There are multiple data stores, the first two of which are:
- The classic Aster relational data store.
- A file system that emulates HDFS (Hadoop Distributed File System).
- There are multiple processing “engines”, where an engine is what occupies and controls a processing thread. These start with:
- Generic analytic SQL, as Aster has had all along.
- SQL-MR, the MapReduce Aster has also had all along.
- SQL-Graph aka SQL-GR, a graph analytics system.
- The Aster parser and optimizer accept glorified SQL, and work across all the engines combined.
There’s much more, of course, but those are the essential pieces.
Just to be clear: Teradata Aster 6, aka the Teradata Aster Discovery Platform, includes HDFS compatibility, native MapReduce and ways of invoking Hadoop MapReduce on non-Aster nodes or clusters — but even so, you can’t run Hadoop MapReduce within Aster over Aster’s version of HDFS.
The most dramatic immediate additions are in the graph analytics area.* The new SQL-Graph is supported by something called BSP (Bulk Synchronous Parallel). I’ll start by observing (and some of this is confusing):
- BSP was thought of a long time ago, as a general-purpose computing model, but recently has come to the fore specifically for graph analytics. (Think Pregel and Giraph, along with Teradata Aster.)
- BSP has a kind of execution-graph metaphor, which is different from the graph data it helps analyze.
- BSP is described as being a combination hardware/software technology, but Teradata Aster and everybody else I know of implements it in software only.
- Aster long ago talked of adding a graph data store, but has given up that plan; rather, it wants you to do graph analytics on data stored in tables (or accessed through views) in the usual way.
Use cases suggested are a lot of marketing, plus anti-fraud.
*Pay no attention to Aster’s previous claims to do a good job on graph — and not only via nPath — in SQL-MR.
So far as I can infer from examples I’ve seen, the semantics of Teradata Aster SQL-Graph start:
- Ordinary SQL except in the FROM clause.
- Functions/operators that are the arguments for FROM; of course, they output tables. You can write these yourself, or use Teradata Aster’s prebuilt ones.
Within those functions, the core idea is: Read more
|Categories: Application areas, Aster Data, Business intelligence, Data models and architecture, Data warehousing, Hadoop, Parallelization, Predictive modeling and advanced analytics, RDF and graphs, Teradata||4 Comments|
When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.
- The four biggies are:
- MapReduce. Duh.
- SQL, specifically Impala. This is as opposed to the uneasy Hive/MapReduce layering.
- “Math” , which seems to mainly be through partnerships with SAS and Revolution Analytics. I don’t know a lot about how these work, but I presume they bypass MapReduce, in which case I could imagine them greatly outperforming Mahout.
- Stream processing (Storm) is next in line.
- Graph — e.g. Giraph — rises to at least the proof-of-concept level. Again, the hope would be that this well outperforms graph-on-MapReduce.
- Charles is also seeing at least POC interest in Spark.
- But MPI (Message Passing Interface) on Hadoop isn’t going anywhere fast, except to the extent it’s baked into SAS or other “math” frameworks. Generic MPI use cases evidently turn out to be a bad fit for Hadoop, due to factors such as:
- Low data volumes.
- Latencies in various parts of the system
HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.
Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more
|Categories: Cloudera, Complex event processing (CEP), Databricks, Spark and BDAS, Endeca, Hadoop, HP and Neoview, MapReduce, Predictive modeling and advanced analytics, RDF and graphs, Revolution Analytics, SAS Institute, Teradata||22 Comments|
Over the past week, discussion has exploded about US government surveillance. After summarizing, as best I could, what data the government appears to collect, now I ‘d like to consider what they actually do with it. More precisely, I’d like to focus on the data’s use(s) in combating US-soil terrorism. In a nutshell:
- Reporting is persuasive that electronic surveillance data is helpful in following up on leads and tips obtained by other means.
- Reporting is not persuasive that electronic surveillance data on its own uncovers or averts many terrorist plots.
- With limited exceptions, neither evidence nor logic suggests that data mining or predictive modeling does much to prevent domestic terrorist attacks.
Consider the example of Tamerlan Tsarnaev:
In response to this 2011 request, the FBI checked U.S. government databases and other information to look for such things as derogatory telephone communications, possible use of online sites associated with the promotion of radical activity, associations with other persons of interest, travel history and plans, and education history.
While that response was unsuccessful in preventing a dramatic act of terrorism, at least they tried.
As for actual success stories — well, that’s a bit tough. In general, there are few known examples of terrorist plots being disrupted by law enforcement in the United States, except for fake plots engineered to draw terrorist-leaning individuals into committing actual crimes. One of those examples, that of Najibullah Zazi, was indeed based on an intercepted email — but the email address itself was uncovered through more ordinary anti-terrorism efforts.
As for machine learning/data mining/predictive modeling, I’ve never seen much of a hint of it being used in anti-terrorism efforts, whether in the news or in my own discussions inside the tech industry. And I think there’s a great reason for that — what would they use for a training set? Here’s what I mean. Read more
|Categories: Application areas, Predictive modeling and advanced analytics, RDF and graphs, Surveillance and privacy, Text||9 Comments|
My clients at Cloudant, Couchbase, and 10gen/MongoDB (Edit: See Alex Popescu’s comment below) all boast the feature incremental MapReduce. (And they’re not the only ones.) So I feel like making a quick post about it. For starters, I’ll quote myself about Cloudant:
The essence of Cloudant’s incremental MapReduce seems to be that data is selected only if it’s been updated since the last run. Obviously, this only works for MapReduce algorithms whose eventual output can be run on different subsets of the target data set, then aggregated in a simple way.
These implementations of incremental MapReduce are hacked together by teams vastly smaller than those working on Hadoop, and surely fall short of Hadoop in many areas such as performance, fault-tolerance, and language support. That’s a given. Still, if the jobs are short and simple, those deficiencies may be tolerable.
A StackOverflow thread about MongoDB’s version of incremental MapReduce highlights some of the implementation challenges.
But all practicality aside, let’s return to the point that incremental MapReduce only works for some kinds of MapReduce-based algorithms, and consider how much of a limitation that really is. Looking at the Map steps sheds a little light: Read more
|Categories: Cloudant, Couchbase, EAI, EII, ETL, ELT, ETLT, Hadoop, MapReduce, MongoDB, RDF and graphs||1 Comment|
I haven’t done a notes/link/comments post for a while. Time for a little catch-up.
1. MySQL now has a memcached integration story. I haven’t checked the details. The MySQL team is pretty hard to talk with, due to the heavy-handedness of Oracle’s analyst relations.
2. The Large Hadron Collider offers some serious numbers, including:
- 1 petabyte/second.
- 6 x 109 collisions/second.
- Only 1 in 1013 collision records kept (which I guess knocks things down to a 100 byte/second average, from the standpoint of persistent storage).
- Real-time filtering by a cluster of several thousand machines, over a 25 nanosecond period.
3. One application area we don’t talk about much for analytic technologies is education. However: Read more
|Categories: Cache, memcached, Memory-centric data management, MySQL, Open source, Petabyte-scale data management, RDF and graphs, Scientific research||Leave a Comment|