THE SQL Server Blog Spot on the Web

Welcome to SQLblog.com - The SQL Server blog spot on the web Sign in | |
in Search

SQLBI - Marco Russo

Please visit the same blog at https://www.sqlbi.com/blog/marco/ - comments are now disabled here but you can comment on the new blog (all blog posts available here are available there, too).
You can follow me on Twitter: @marcorus

Internet of Things in 2015 #summit15 #sqlpass

David DeWitt and Rimma Nehme delivered the day 2 keynote at PASS Summit 2015 talking about Internet of Things. Many PASS attendees know David for providing some of the best keynotes every, technical and without vaporware and marketing stuffs. So this is a good time to discuss about the real stat of Internet of Things (IoT) these days.

It’s easy to talk about IoT, but it’s hard to enter into this complex and heterogeneous world. There are many type of devices, and a simple categorization is consumer vs. industrial, which have very different requirements and features (not to mention cost, power, and standards used). The key to connect IoT devices is the cloud, but communication happens in two directions: from device to cloud, and from cloud to device. There requires different technologies and often different devices, because many of them have only a single role (sending data or receiving commands, sensors or actuators, but certain devices might do both).

As you can imagine, this is a real source of Big Data. I often see Big Data used to manage data that are generated into a structured relational database, which seems to be a non-sense. But with the volume of data generated by IoT this technology makes perfect sense. Of course, Azure has a lot of technologies that helps you manage this amount of data, but since the topic of this blog is Business Intelligence, I’m more interested to what happens when you want to analyze data.

Here, a few technologies that have a certain history (if you know former names) came into play. Data Mining (also known as Machine Learning these days) can be fundamental to make predictions based on previous behaviors. David is great in providing simple examples to explain the concept: a boiler has a pressure sensor and you have to open valve before boiler explodes. You can train the algorithm for predicting boiler failure, or you can provide built-in intelligence in the algorithm, with a predefined limit of bar to open valve. The analysis of this data requires real-time stream analysis, relying on the cloud for this real-time analysis would generate too much traffic and would also have higher latency (dangerous for this type of applications).

Here it comes a new “definition”: fog computing, also known edge computing. The idea is to not move the data to computation, but to move computation to data. However, the IoT is a database problem, which is not managed in this way these days. And this is the main point of this keynote. Proposing a Polybase for IoT that includes:

  • Declarative language: today IoT is based on imperative languages, whereas the goal is to introduce declarative language, such as IoT-SQL (imagine to add a WINDOW and ACTION suffix to a classical SQL query, so that it can act on a range of time, triggering an action when certain conditions happens)
  • Complex object modeling: define a standard structure to identify IoT locations in a hierarchical structure (imagine an object model with an API to navigate hierarchies of objects, traversing path and similar stuffs – similar to many MDX statements we know well)
  • Scalable metadata management: simple abstraction (Metadata, statistics, access privileges) unified to access different devices; metadata includes collection of standard and extended attributes
  • Discrete & Continuous Queries: different query types, such as ExecuteOnce (like a standard SQL), ExecuteForever (continuous flow of responses from device), ExecuteAction (such as ExecuteForever plus an action to execute in defined conditions)
  • Multi-purpose queries: here is the smart idea. With a definition of the process at an higher level, the decision of moving the work in the cloud or at the edge (in the fog) is made by the query optimizer, creating a real query plan that distributes the actual work to different parts of the system depending on the requests

This approach is really ambitious, but an important part of it is the idea of embedding security in the system. If you think about the future of IoT, security is of paramount importance. I don’t know if this will be the future of IoT, but this speech raised points that have to be faced, sooner better than later.

You will be able to watch this keynote soon at PASStv on demand.

Published Thursday, October 29, 2015 7:38 PM by Marco Russo (SQLBI)

Comments

 

_Teva said:

Hello Marco,

Regarding your notes we can split the Iot decision making process in three categories :

- a) the ones that doesn't require an immediate intervention,

- b) the ones that requires an immediate decision when a set of conditions are met,

- c) the ones that mixes a & b,

We can consider the Microsoft band (or similar wearable devices) as a good example of type c.

Imagine we want to track (and keep) the heart rate (at the pulse level), the temperature & the perspiration level (each minute) of each and every band owner on the planet. I'd probably store that once for all on a DataWarehouse (I'd probably create a landing zone and store the data in hdfs files) and query it for dashboarding purposes using a query layer (schema on read), I think I'd give Dax a try.

Such data warehouse would be interesting because it will help us :

-> understand and predict health condition based on that data (detect, monitoring and predicting the evolution of heart conditions, the same for glucose levels for diabetics  and so forth),

-> personnalise treatments based upon personalized data (this can be seen as one goal of the Quantified Self),

If we envision those two dashes as not being time sensitive information that require immediate decision/intervention then we will probably put that information in a nice set of dashboards and follow the structure mentioned in your post -> the device to cloud then cloud to device.

However some other information are time sensitive but in the same time does not require the same amount of data in order to predict/detect anomalies.  For example, if we are ambitious enough to propose a future, maybe an Iot that would be intelligent enough to deliver medication (insulin pump), or to "re-animate" a person by sending the right information at the good time to a pacemaker. This kind of intervention/decision are time sensitive and should not  depend on network availability. I would probably have trained a set of statistical models on a subset of carefully selected data available in the landing zone, and move my predictor right inside the Iot, in order to have the prediction available right after the sensor measured it.

In terms of technologies we have covered the data warehouse part, regarding the embed part (the predictor) I think I'd would probably have worked with python, or with someone who know how to use C or java, I'd have try to store the bare minimum historical data on the device (depending on onboard memory & on board allowed fast write/read access dbms).

Oh, one last thing, I'd like to take this opportunity to thank you for all the great content you've being providing to the community. Thank you !

October 29, 2015 4:07 PM
 

Marco Russo (SQLBI) said:

Thanks for your feedback and appreciation!

October 29, 2015 4:53 PM
New Comments to this post are disabled

About Marco Russo (SQLBI)

Marco Russo is a consultant, writer and trainer specialized in Business Intelligence with Microsoft technologies. He runs the SQLBI.COM website, which is dedicated to distribute resources useful for BI developers, like Integration Services components, Analysis Services models, tools, technical information and so on. Marco is certified as MCT, MCDBA, MCSD.NET, MCSA, MCSE+I.

This Blog

Syndication

Archives

Privacy Statement