Some insights from the talk of Richard Soley in the IoT summit in INTEROP

The opening speaker in the IoT summit yesterday was Richard Soley, the person behind Object Management Group.  Richard talked about the "industrial Internet". He started his talk by having a nice slide in which the Internet now substitutes many thing we have done in the past

However, not everything changed by the Internet, since in many cases enterprises lack the "Internet thinking".  Examples are: manufacturing, energy grids, jet engines, oil and gas exploration and more are handled exactly as were done before.

This is due to the fact that the people involved including technical people are stuck in the way of thinking of the past.

Richard talked about the Industrial Internet Consortium which is a separate entity and not part of OMG (a correction that Richard made to my original posting). 
It has 85 members (at the time of the talk) and growing.  It is intended to study testbeds  in this area.  The Internet of Things is a crucial component in the industrial internet game.

One more insight from Richard is that "people don't read".  Everybody re-invents the wheel, since the current generation of professional people don't read and are not familiar with the state of the practice.  This is consistent with our finding in the event processing area where people prefer to reinvent the wheel and don't even know the wheel exists.  Another perspective of Richard's talk you can find in the article by Chris Taylor, who was the session organizer.   

Gartner hype cycle July 2014

Gartner published its hype cycle report recently.   The "Internet of Things" is now at the top of the hype cycle, defined as "peak of inflated expectation".   "Big Data" which has been there before, and now moved down the line of disillusionment.   Another hype in the height is the "natural language question answering" that was hyped by IBM's Watson.   In the upwords side we can see among other things: software-defined anything, connected home, and prescriptive analytics.    Note that in the right-hand side there are technologies which are considered mature, such as: speech recognition, enterprise 3D printings and in-memory analytics.  "Complex event processing" is moving slowly down the disillusionment path. 

My upcoming talk in INTEROP, September 29, NYC

I was invited to give a talk in the Internet of Things summit that will take place in INTEROP, in NYC September 29. 

My talk will be about "The Internet of Things and Personalization", the area I am investigating nowadays.

Other speakers will be  Richard Soley, CEO of OMG, and John Morris, VP of ComplexIT. 
The moderator is Chris Taylor from TIBCO, a well known writer in this area.

Anybody that wishes to meet me in the NYC area during that week - please let me know. 

On data centric, decision centric, and situation centric - a response to Chris Taylor's "time and effort we waste on big data"

Some times there are scientific truths,  Nicolaus Copernicus coined the "heliocentric hypothesis", which states that the earth is revolving around the sun, and not vice versa.  His hypothesis was proved as a scientific facts.

The centric orientation is often a question for dispute, in a past post on this blog, I wrote about the dispute between Plato who advocated a society-centric approach, and Aristotle who advocated individual-centric approach.  

Chris Taylor recently wrote in the"Real-time & Complex Event Processing" site a post entitled: "The time and efforts we waste about big data".   Chris used the analog of "Tower of Babel"  and criticized the efforts invested in accumulating data within large warehouses, and the "data centric" approach, advocating another approach  "decision centric" approach. Stating ---  let's architect the "big data" around decisions, identify decisions required first, and then manage data as part of the decision architecture, making it decision centric.

  Let me add another view point here.  

 If we look at the sources of Big Data in 2015, we'll see the most of the data will come from sensors, and the second source is social media, where enterprise data which is the more familiar world became the minority.   If we look at the value of data the "Internet of Things",  one of its main values is the ability to detect situations and act upon them (in either reactive or proactive way).  Thus the center is neither data, nor decision, it is about situations, it becomes situation centric, and the architecture is around -- which situation we wish to identify, and then what data we need for that, and sometimes also what decisions we need when the situation is detected (note, the decision can be trivial, since when a situation occurs there is a single action associated with it, so it is not necessarily decision centric).

We have mentioned data-centric, decision-centric, and situation-centric.   Maybe one of the conclusions we can draw from Chris' analogy of "The Tower of Babel" is that there is no single viewpoint.  

Sometimes there is a need to accumulate data without a-priori knowledge what it will be used for. Medical data, for example, can be accumulated and lead to unexpected results, which will drive new type of decisions, and/or new situations we'll wish to identify.    In this case the data-centric approach is valid. 

In an organized world of structured processes with well-defined decisions, the decision-centric approach makes sense. As an example, when the main process is credit approval, this is a well-defined decision that centers both processes and data around it.

In the new world based on "Internet of Things" - situation-centric might become more dominant, and if we look at where big data really is -- we'll see more and more situation-centric in the universe.

Unlike the "heliocentric hypothesis" which is a scientific fact,  we don't have single scientific truth, but when anybody invests time and effort on big data, one has better to sort out what is the best value, instead of assuming that accumulating data is the value. 

My talk in DEBS 2014 on the Internet of Everything

I am writing this post from the hotel "Meluha the Fern" in Mumbai.  Arrived here on Friday and had also an opportunity to do some sightseeing. Will write my impressions from Mumbai at a later phase.

Today DEBS 2014 started, the conference is being held in IIT Bombay.   The first day has been the tutorial day. I have delivered (by myself, my co-authors did not arrive) a tutorial on the "Internet of Everything".

This is the next in the tradition of tutorials that I am giving in DEBS since 2008.   As usual I have posted the tutorial on slideshare.  The problem with the slideshare conversion is that it messes up the animations, but I guess that it is readable anyway.  I'll write about the rest of the conference soon.  Enjoy!

IoT and the senior citizen

I have not written for a while,  spent a few days in vacation in Rhodes in a family trip - album is available on facebook, and then I was busy preparing a presentation for the board of directors of the college which sponsors the "Institute of Technological Empowerment" which I am now working on establishment.  The presentation is in Hebrew, have not made a lot of presentations in Hebrew recently, and intend to create an English version to share it with larger audience.  When I'll do, I'll post it on slideshare.

I came across a blog post by Stephenson Strategies entitled: "Seniors and the Internet of Things: Empowerment and Security".   As a matter of fact senior citizen are one of the target populations of the institute I am trying to establish, and we are working with experts in gerontology.  The Internet of Things provides opportunities to empower aging population to maintain independent life in various means, by using smart systems that receives events from sensors and determine actions, which are mostly alerts to the person, family, healthcare taker and more.   The cited post relates to health issues, but this can extend to other issues that can improve quality of life and increase the person's security.  I'll write more about concrete projects we are planning later. 

On latent data

I came across a post by EwanD from Microsoft entitled: “Latent Data” – the secret sauce of the Internet of Things. Since I am interested in both secret sauces and IoT, I was curious to understand what is this sauce. 

It seems that the term latent data refers to data that is typically not available, and also data that does not have any meaning on its own, and need to be aggregated, or joined with other data to be useful.  

Indeed IoT brings to the picture a lot of data that has not been available previously, and in my terminology, much of this data is about event that occur.   Sometimes the raw events are of interest, sometimes the interest is on derived events that are aggregation, transformation, or function that involve multiple events, and possibly also historical data and state information.   Note that when latent data becomes available it is not latent anymore,  and also that latent is a relative term, some piece of data can be available to somebody, and concealed from somebody else.     From this post one can learn what Microsoft sees its role in the IoT era, what I understood is that the role is twofold: both provider of OS for embedded systems, and as a cloud provider.   I am now trying to understand roles of different players in the IoT world, looking for sponsors for my recent activity.  

The Technological Empowerment Institute -- first exposure

I have written a month ago about moving on, I still need to post  a summary of my IBM time, but it will have to wait as I am quite busy in my new role.  The role is an attempt to establish (from scratch) an applied research institute called "The Technological  Empowerment Institute (TEI)".   This is a first of a series of posts about the institute's plan.    

The slide below shows the idea in a nutshell:

The domain that we are looking at is in general, exploiting Internet of Things for societal purposes. 

The mission is to help developing areas, first in the Israeli periphery, in this case, the concentration will be on the northern part of Israel, and developing countries over the world.  

The idea is to create partnership with:

1. Multidisciplinary researchers dealing with technology, the human aspect of creating and consuming smart systems (a topic that anybody  following this blog realized I have been focused in the last couple of years), and the domain oriented research (agriculture, gerontology, healthcare and more). The affiliated researchers will be international.

2. Partnership with high-tech companies for using their platforms and products for the implementation projects (see below).

3. Using students projects and internship program to carry out concrete implementation projects that fit the institute's mission.

4. Partnership with academic institutes in developing countries to collaborate on the above.

In the next series of posts I'll write about each of these items.  I am now spending much of my time in creating all these partnership -- a big challenge, and also fun. 

My talk in the Technion Big Data workshop

Yesterday, I gave a talk in the Technion Computer Engineering Big Data days --  the talk dealt with three topics:  why  the Internet of things did not happen yet,  very brief introduction to "The Event Model", and a new introduction of the Technological Empowerment Institute.  I'll write more about the institute soon.

The talk is on slideshare - enjoy.

On the PLAY project

I have spent this week some time in cold Brussels, in my role of reviewer of the PLAY project, which was a project in the framework of the ICT program of the European Union.    There are quite a few projects that have event processing at their core, in fact early in 2014 we'll be involved in two new projects: SPEED and FERARI, about which I'll write in a later phase.  Being a reviewer, I accompanied the PLAY project since its beginning -- starting with the "kick-off" review, and continuing to the three annual reviews.  As a reviewer, my role is both to evaluate what was done and provide comments and evaluations, and also to be a kind of mentor for the project and try to help them going in the right direction.    The project has evolved during these years, started with event-driven services as a motivation, and in addition touched topics like Internet of Things and events coming from sensors.   It uses RDF and semantic web technology to describe events and patterns, and also plays with the idea of event marketplace, an idea that deserves more discussion in one of the next posts. As for the event processing part, they have developed distributed ETALIS,   I guess that this will be replaced if they want to take it to the real life, as logic programming based languages are great for the few people who understand how to program with them, and a barrier to others.  While this is a research project, and in real-life setting  this implementation will probably be replaced, the approach taken have a promise.  There will also be some follow-ups to this project, which is something that is desirable for these projects, the "after life".   On the whole, this is an opportunity both to assist and to learn, and I hope to hear about the "after life" in the future.  

On Dynamic M2M Event Processing

M2M is one of the realizations of the Internet of Things which attracts a lot of work recently.  Event Processing is in the core of such applications. They don't work in the traditional Internet model of - store and search, but they are intended to alert or act now.     
An interesting presentation from Eclipse Con 2014 (planned for March 2014) is entitled "On  Dynamic M2M Event Processing".  This presentation (marked as a draft) is  by  Hitachi and Oracle.    It talks about event processing within remote devices embedded within   the OSGi component model.  Worth reading -- and we'll see a lot more in this direction. 

On machine learning as means for decision velocity

Chris Taylor has written in the HBR Blog a piece that advocates the idea that machine learning should be used to handle the main issue of big data - decision velocity.  I have written recently on decision latency, which according to some opinions - real-time analytics will be the next generation of what big data is about.

Chris' thesis is that the amount of data is substantially increasing with the Internet of Things, and thus one cannot get a decision manually in viewing all relevant data,  there will also not be enough data scientists to look at the data.   Machine learning which is goal oriented and not hypothesis asserting oriented will take this role.     I agree that machine learning will take a role in the solution, but here are some comments about the details:

Currently machine learning is off-line technology, case sensitive, and cannot be the sole source for decisions.

It is off-line technology, systems have to be trained, and typically it looks at historical data in perspective and learns trends and patterns using statistical reasoning methods.  There are cases of applying continuous learning, which again done mostly off-line, but is incrementally updated on-line.    When a pattern is learned it needs to be detected in real-time on streaming data, and here technology like event processing is quite useful, since what it does is indeed detect that predefined patterns occur on streaming data.  These predefined patterns can be achieved by machine learning.    The main challenge will be the online learning -- when the patterns need change, how fast this can be done in learning techniques.  There are some attempts at real-time machine learning (see presentation about Tumra as an example), but it is not a mature technology yet.

Case sensitive means that there is no one-size-fits-all solution for machine learning, and for each case the models have to be established in a very specific way for that case.  Thus, the shortage in data scientists will be replaced by shortage of statisticians,  there are not enough skills around to build all these systems, thus the state of the art need to be improved to make the machine learning process itself more automated.

Last but not least - I have written before that get decisions merely based on history is like driving a car by looking at the rear mirror.  Conclusion from historical knowledge should be combined with human knowledge and experience sometimes over incomplete or uncertain information.  Thus besides the patterns discovered by machine learning, a human expert may also insert additional patterns that should be considered, or modify the machine learning introduced patterns.

Proactive event processing for intelligent transportation system

I came across a new publication whose citation is: " Yongheng Wang, "A Proactive Complex Event Processing Method for Intelligent Transportation Systems," Lecture Notes on Information Theory, Vol.1, No.3, pp. 109-113, Sept. 2013. doi: 10.12720/lnit.1.3.109-113".   This paper is a follow-up to our work on proactive event-driven computing,  and applies events coming from the Internet of Things towards intelligent transportation system, proactively mitigating traffic congestion.  This work originates in China, who made Internet of Things as its flagship project.   We have looked at similar problem  as one of our use cases for EU project proposal (that did not win the lottery).

  

From Australia - 2013: The Year of the Internet of Things

The complexevents site by David Luckham has undergone a face-lifting and has a nice new format.

In its current feature article it links to an article from CSIRO, Australia,  entitled: "2013: The Year of the Internet of Things".   Arkady Zaslavsky from CSIRO, who deals for years in context aware computing points out several successful IoT projects in Australia -- one of them is in agriculture, using sensors to check the best conditions for certain plants, other examples are public transportation track in big cities and monitoring sporting performance.   The illustration above taken from this article compares 2010 and (predicted) 2015 quantities of  Internet data in petabytes (petabyte = 1 million gigabytes) by industries, where in 2010 most data was created by humans, and the shift is that most data on the Internet will be created by sensors.      

The potential dark side of the Internet of Things

Thanks to Rainer von Ammon who attracted my attention to an article entitled "Murder by Internet",  that points out some of the dangers of connecting everything to the Internet,  some of them such as manipulating pacemakers and controlling vehicles from remote may result in using the Internet for murder, and commit various crimes that will be difficult to detect.   Security of such systems will become crucial!  Event processing can also be used as enabling technology for such security, but cyber crimes pose challenges that law enforcement today is not ready to handle.  

The Internet of Things in practice

I came across a post in the "smarter planet blog" entitled "The Internet of Things start to bear fruits" by Paul Brody,  it claims that the "Internet of Things" vision is beginning to be in reality, and that the major enabling factor is the availability of low cost scalable connectivity.  The data transfer cost is below $1 for GB.  

It surveys some initiatives like: Raspberry Pi, Sensordone, and the Nokia initiative to become the "where company".

Event processing is one of the ingredients of the Internet of Things, and on this space,  Brody mentions IFTTT (If this than that).  Note that I have written recently about ON{X} which is of similar flavor to IFTTT.

On the Internet of EVERYTHING

Cisco came out recently with the concept of  "The Internet of EVERYTHING".

While the "Internet of Things" deal with connecting anything to the Internet, the Internet of EVERYTHING deal with the things and the semantic connection among things that can make the world actionable in real-time.  A simple example is the car theft example. 

A car is connected to the Internet through its GPS sensor that reports its location, it has also semantic relations to a list of eligible drivers who are permitted to drive in this car, each of these persons is also connected to the Internet using his or her mobile phone, thus the Internet knows the person's location  (disregard the privacy issue for this scenario!),  so if the car is moving (inferred from the GPS change in location), and all the eligible drivers are not in the car - it means that the car is stolen, and it can then report to the police and have them track its location.  

This scenario is based on - things,  contexts of things, and processing events about things..   It is actually quite straightforward from technology point of view.  I wonder if the "Internet of EVERYTHING" will survive the buzzword test of time... 

Intelligent Business Operations - a medical use case

Within the recent year Gartner promotes the term "Intelligent Business Operations" (IBO)  - not to confuse with Business Intelligence (BI).  Roy Schulte from Gartner wrote about "operational IQ".   I am looking now at the concepts and facilities of IBO, in Gartner's view.    One way to study it is by looking on a recent post by Jim Sinur (also from Gartner).  Jim provides a success story in the medical domain, resource allocation in surgeries.   The ingredients of this scenario are:

1.  Simulation-based optimization of scheduling and resource allocation in off-line for all surgeries planned for the next day.
2. Real-time tracking of everything: physicians, nurses, equipment; monitor of procedure duration and status - using sensors, cameras and in Jim's terminology - exploiting the "Internet of Things".
3. Determination of things already going wrong (not according to plan) or expected deviations from plan
4. Re-applying the simulation based optimization (this time online!) and get updated resource allocation plan.

This may be instance of the "detect-forecast-decide-act" pattern we have identified as the basis of proactive computing, although in Jim's scenario it can also be reactive (the deviation from plan already occurred - there is no need to forecast anything).     


I'll write more about the IBO concept and some additional ingredients of it soon.  
Since the term "intelligence" is now back in fashion,  it would be nice to have metrics for the IQ of some operational process like the surgery management.
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