Call for Papers Shanghai, 4-5 June 2015 – International Conference on City Sciences (ICCS 2015): New architectures, infrastructures and services for future cities

Call for Papers from  International  Conference on City  Sciences  (ICCS  2015): New  architectures,  infrastructures  and  services  for  future cities co-organized by City sciences where I teach

 

 

 

Call for Papers  Shanghai,  4-5  June  2015

International  Conference on City  Sciences  (ICCS  2015): New  architectures,  infrastructures  and  services  for  future cities

The   new   science   of   cities   stands   at   a   crossroads.   It   encompasses   rather   different,   or   even  conflicting,  approaches.  Future  cities  place  citizens  at  the  core  of  the  innovation  process  when  creating  new  urban  services,  through  “experience  labs”,  the  development  of  urban  apps  or  the  provision   of     ”open    data”.     But     future     cities     also    describe     the     modernisation     of    urban  infrastructures     and    services    such    as    transport,    energy,    culture,    etc.,    through    digital    ICT  technologies:   ultra-­‐fast   fixed  and  mobile  networks,  the  Internet  of  things,  smart  grids,  data  centres,   etc.  In  fact  during  the  last   two  decades local   authorities  have  invested   heavily  in  new  infrastructures   and  services,   for  instance  putting  online  more  and  more  public  services  and  trying    to   create   links   between  still prevalent silo   approaches   with   the   citizen   taking   an  increasingly  centre-­‐stage  role.  However,  so  far  the  results  of  these  investments  have  not  lived  up  to  expectations,  and  particularly  the  transformation  of  the  city  administration  has  not  been  as    rapid   nor   as   radical   as   anticipated.   Therefore,   it   can   be   said   that   there   is   an   increasing  awareness  of  the  need  to  deploy  new  infrastructures  to  support  updated  public  services  and  of  the     need    to   develop   new    services    able    to   share    information    and    knowledge    within    and  between   organizations   and   citizens.   In   addition,   urban   planning   and   urban   landscape   are  increasingly    perceived   as   a   basic   infrastructure,   or   rather   a   framework,   where   the   rest   of  infrastructures  and  services  rely  upon.  Thus,  as  an  overarching  consequence,  there  is  an  urgent  need   to   discuss  among  practitioners  and   academicians  successful  cases  and   new  approaches  able  to  help  to  build  better  future  cities.

Taking  place  in  Shanghai,   the  paradigm  of  challenges  for  future  cities  and   a  crossroad   itself  between   East  and   West,  the  International   Conference  on  City  Sciences  responds  to  these  and  other   issues  by  bringing  together  academics,  policy  makers,  industry  analysts,  providers  and  practitioners     to    present    and    discuss    their    findings.    A    broad    range    of    topics    related  to  infrastructures    and   services   in   the   framework   of   city   sciences   are   welcome   as   subjects   for  papers,  posters  and  panel  sessions:

  • Developments  of   new  infrastructures  and  services  of   relevance  in  an  urban  context:  broadband,    wireless,   sensors,   data,   energy,   transport,   housing,   water,   waste,   and  environment.
  • City sustainability  from  infrastructures  and  services
  • ICT-­‐enabled  urban  innovations
  • Smart city  developments  and  cases
  • Social and  economic  developments  citizen-­‐centric
  • Renewed  government  services  in  a  local  level
  • Simulation and  modelling  of  the  urban  context
  • Urban landscape  as new infrastructure Additional relevant topics  is  also  welcomed.

Authors  of  selected   papers  from  the  conference  will  be  invited   to   submit  to   special  issues  of International  peer-reviewed  academic  journals.

Important  deadlines:

  • 20 February:  Deadline  for  Abstracts  and  Panel  Session  Suggestions
  • 30 March:  Notification  of  Acceptance
  • 30  Apr:  Deadline  for  Final  Papers  and  Panel  Session  Outlines
  • 4- 5  June:  International  Conference  on  City  Sciences  at  Tongji  University  in  Shanghai,  PR  China

Submission of  Abstracts:

Abstracts  should   be  about  2  pages  (800  to   1000  words)  in   length   and  contain  the   following

information:

  • Title of  the  contribution
  • A  research  question
  • Remarks on  methodology
  • Outline of  (expected)  results
  • Bibliographical notes  (up  to  6  main  references  used  in  the  paper)
  • All abstracts  will  be  subject  to  blind  peer  review  by  at  least  two  reviewers.

conference link: International  Conference on City  Sciences  (ICCS  2015): New  architectures,  infrastructures  and  services  for  future 

 

 

 

 

Data Science at the command line – Book and workshop ..

 

 

 

 

 

 

 

 

 

 

I am reading a great book called Data Science at the Command line

The author Jeroen Janssens has a workshop in London on Data Science at the command line which I am attending

Here is a brief outline of some of the reasons why I like this approach ..

I have always liked the Command line .. from my days of starting with Unix machines. I must be one of the few people to actually want a command line mobile phone!

 If you have worked with Command line tools, you already know that they are powerful and fast.
For data science especially, that’s relevant because of the need to manipulate data and work with a range of products that can be invoked through a shell like interface
The book is based on the Data science toolbox – created by the author as an Open source tool and is brief and concise(187 pages). The book focuses on specific commands / strategies that can be linked together using simple but powerful command line interfaces
Examples include:
using tools such as json2csv tapkee dimensionality reduction library  and Rio (created by the author). Rio loads CSVs into R as a data.frame, executes given commands and gets the output as CSV or PNG )
run_experiment -  a SciKit-Learn command-line utility for running a series of learners on datasets specified in a configuration file.
tools like topwords.R
and many others
By co-incidence I read this as I was working on this post:  command line tools can be 235x faster than your hadoop cluster

I recommend both the book and the workshop.

 UPDATE:

a) I have been informed that there is a 50% discount offered for students, academics, startups and NGOs for the workshop
b) Jeroen says that:  The book is not really based on the Data Science Toolbox, but rather provides a modified one so that you don’t have to install everything yourself in order to get started. You can download the VM HERE

Data Science for IoT: The role of hardware in analytics

 

 

 

 

 

This post is leading to vision for Data Science for IoT course/certification. Please sign up on the link if you wish to know more when launched in Feb.

Often, Data Science for IoT differs from conventional data science due to the presence of hardware. Hardware could be involved in integration with the Cloud or Processing at the Edge (which Cisco and others have called Fog Computing). Alternately, we see entirely new classes of hardware specifically involved in Data Science for IoT(such as synapse chip for Deep learning)

Hardware will increasingly play an important role in Data Science for IoT. A good example is from a company called Cognimem which natively implements classifiers(unfortunately, the company does not seem to be active any more as per their twitter feed)

In IoT, speed and real time response play a key role. Often it makes sense to process the data closer to the sensor. This allows for a limited / summarized data set to be sent to the server if needed and also allows for localized decision making.  This architecture leads to a flow of information out from the Cloud and the storage of information at nodes which may not reside in the physical premises of the Cloud.

In this post, I try to explore the various hardware touchpoints for Data analytics and IoT to work together.

Cloud integration: Making decisions at the Edge

Intel Wind River edge management system certified to work with the Intel stack  and includes capabilities such as data capture, rules-based data analysis and response, configuration, file transfer and  Remote device management

Integration of Google analytics into Lantronix hardware –  allows sensors to send real-time data to any node on the Internet or to a cloud based application.

Microchip integration with Amazon Web services  uses an  embedded application with the Amazon Elastic Compute Cloud (EC2) service. Based on  Wi-Fi Client Module Development Kit . Languages like Python or Ruby can be used for development

Integration of Freescale and Oracle which consolidates data collected from multiple appliances from multiple Internet of things service providers.

Libraries

Libraries are another avenue for analytics engines to be integrated into products – often at the point of creation of the device. Xively cloud services is an example of this strategy through xively libraries

APIs

In contrast, keen.io provides APIs for IoT devices to create their own analytics engines ex (smartwatch Pebble’s using of keen.io)  without locking equipment providers into a particular data architecture.

Specialized hardware

We see increasing deployment  of specialized hardware for analytics. Ex egburt from Camgian which uses sensor fusion technolgies for IoT.

In the Deep learning space, GPUs are widely used and more specialized hardware emerges such as IBM’s synapse chip. But more interesting hardware platforms are emerging such as Nervana Systems which creates hardware specifically for Neural networks.

Ubuntu Core and IFTTT spark

Two more initiatives on my radar deserve a space in themselves – even when neither of them have currently an analytics engine:  Ubuntu Core – Docker containers+lightweight Linux distribution as an IoT OS and IFTTT spark initiatives

Comments welcome

This post is leading to vision for Data Science for IoT course/certification. Please sign up on the link if you wish to know more when launched in Feb.

Image source: cognimem

Understanding the nature of IoT data

This post is in a series Twelve unique characteristics of IoT based Predictive analytics/machine learning .

I will be exploring these ideas in the Data Science for IoT course /certification program when it’s launched.

Here, we discuss IoT devices and the nature of IoT data

Definitions and terminology

Business insider makes some bold predictions for IoT devices

The Internet of Things will be the largest device market in the world.

By 2019 it will be more than double the size of the smartphone, PC, tablet, connected car, and the wearable market combined.

The IoT will result in $1.7 trillion in value added to the global economy in 2019.

Device shipments will reach 6.7 billion in 2019 for a five-year CAGR of 61%.

The enterprise sector will lead the IoT, accounting for 46% of device shipments this year, but that share will decline as the government and home sectors gain momentum.

The main benefit of growth in the IoT will be increased efficiency and lower costs.

The IoT promises increased efficiency within the home, city, and workplace by giving control to the user.

And others say internet things investment will run 140bn next five years

 

Also, the term IoT has many definitions – but it’s important to remember that IoT is not the same as M2M (machine to machine). M2M is a telecoms term which implies that there is a radio (cellular) at one or both ends of the communication. On the other hand, IOT means simply connecting to the Internet. When we are speaking of IoT(billions of devices) – we are really referring to Smart objects. So, what makes an Object Smart?

What makes an object smart?

Back in 2010, the then Chinese Premier Wen Jiabo once said “Internet + Internet of things = Wisdom of the earth”. Indeed the Internet of Things revolution promises to transform many domains .. As the term Internet of Things implies (IOT) – IOT is about Smart objects

 

For an object (say a chair) to be ‘smart’ it must have three things

-       An Identity (to be uniquely identifiable – via iPv6)

-       A communication mechanism(i.e. a radio) and

-       A set of sensors / actuators

 

For example – the chair may have a pressure sensor indicating that it is occupied

Now, if it is able to know who is sitting – it could co-relate more data by connecting to the person’s profile

If it is in a cafe, whole new data sets can be co-related (about the venue, about who else is there etc)

Thus, IOT is all about Data ..

How will Smart objects communicate?

How will billions of devices communicate? Primarily through the ISM band and Bluetooth 4.0 / Bluetooth low energy. Certainly not through the cellular network (Hence the above distinction between M2M and IoT is important). Cellular will play a role in connectivity and there will be many successful applications / connectivity models (ex Jasper wireless). A more likely scenario is IoT specific networks like Sigfox (which could be deployed by anyone including Telecom Operators).  Sigfox currently uses the most popular European ISM band on 868MHz (as defined by ETSI and CEPT), along with 902MHz in the USA (as defined by the FCC), depending on specific regional regulations.

Smart objects will generate a lot of Data ..

Understanding the nature of IoT data

In the ultimate vision of IoT, Things are identifiable, autonomous, and self-configurable. Objects  communicate among themselves and interact with the environment. Objects can sense, actuate and predictively react to events

Billions of devices will create massive volume of streaming and geographically-dispersed data. This data will often need real-time responses. There are primarily two modes of IoT data: periodic observations/monitoring or abnormal event reporting. Periodic observations present demands due to their high volumes and storage overheads. Events on the other hand are one-off but need a rapid reponse. If we consider video data(ex from survillance cameras) as IoT Data, we have some additional characteristics.

Thus, our goal is to understand the implications of predictive analytics to IoT data. This ultimately entails using IoT data to make better decisions.

I will be exploring these ideas in the Data Science for IoT course /certification program when it’s launched. Comments welcome. In the next part of this series, I will explore Time Series data

 

Content and approach for a Data Science for IoT course/certification

 

 

 

 

 

 

UPDATE: 

Feb 15:  Applications now open -  Data Science for IoT Professional development short course at Oxford University  - more coming soon. Any questions, please email me at ajit.jaokar at futuretext.com

We are pleased to announce support from Mapr, Sigfox, Hypercat and Red Ninja for the Data Science. Everyone finishing the course will receive a University of Oxford certificate showing that they have completed the course. Places are limited – so please apply soon if interested

In a previous post, I mentioned that I am exploring creating a course/certification for Data Science for IoT

Here are some more thoughts

I believe that this is the first attempt to create such a course/program

I use the the phrase “Data Science” to collectively mean Machine learning/Predictive analytics

There are ofcourse many Machine Learning courses – the most well known being Andrew Ng’s course at Coursera/Stanford and the domain is complex enough as it is.

Thus, creating a course/ certification covering both Machine Learning/Predictive analytics and also IoT can be daunting

However, the sector specific focus gives us some unique advantages

Already at UPM (Universidad Politechnica de Madrid) I teach Machine Learning/Predictive analytics for the Smart cities domain through their citysciences program (the remit there being to create a role for the Data Scientist for a Smart city)

So, this idea is not totally new for me ..

Based on my work at UPM (for Smart cites) – teaching DataScience for a specific domain (like IoT) has both challenges but also some unique advantages

The challenges are: You have an extra level of complexity to deal with (in teaching IoT alongwith Predictive analytics)

But the advantages are:

a) The IoT domain focus allows us to be more pragmatic by addressing unique Data Science problems for IoT

b) We can take a Context based learning approach - a technique more common in Holland and Germany for teaching Engineering disciplines – and which I have used in teaching computer science to kids at feynlabs

c)  We don’t need to cover the maths upfront

d)  The participant can be productive faster and apply ideas faster to industry

Here are my thoughts on the elements such a program could cover based on the above approach: 

1) Unique characteristics – IoT ecosystem and data

2) Problems and datasets. This would cover specific scenarios and datasets needed (without addressing the predictive aspects)

3) An overview of Machine learning techniques and algorithms (Classification, Regression, Clustering, Dimensionality reduction etc) – this would also include the basic Math techniques needed for understanding algorithms

4) Programming python scikit-learn

5) Specific platforms/case studies

 Time series data(Mapr)

Sensor fusion for IoT(Camgian – Egburt)

NoSQL data for IoT (ex mongodb for IoT) ,

managing very high volume IoT data Mapr loading time series database 100 million points second

I also include image processing with sensors / IoT(ex surveillance cameras)

Hence,

IBM – Detecting skin cancer more quickly with visual machine learning

Real time face recognition using Deep learning algorithms

and even – Combining the Internet of Things with deep learning / predictive algorithms @numenta 

To conclude:

The above approach for teaching a course on Data Science for IoT  would help focus Machine Learning / Predictive algorithms in a real life problem solving scenario for IoT

Comments welcome.

You can sign up for more information at  futuretext and also follow me on twitter @ajitjaokar

Image source: wired

A business model for IoT retail(Beacon) : ‘Datalogix like’ insights which tie the social to the physical through Data Science and IoT?

 

 

 

 

This post is a part of my Data Science for IoT course

Note: In this post – I am not interested in the Datalogix – store card model. More to the implications of what it could mean for IoT .

Late last year, Oracle acquired a company called Datalogix ..

A Christmas gift perhaps for Larry Ellison – but with profound and disruptive implications

Datalogix does something very unique .. and had been on my radar especially for it’s relationship to facebook

The EFF describes this process in more detail which I summarize (Deep dive facebook and datalogix – what’s actually getting shared )

Datalogix is an advertising metrics company that describes its data set as including “almost every U.S. household and more than $1 trillion in consumer transactions.” It specifically relies on loyalty card data – cards anyone can get by filling out a form at a participating grocery store.

Data from such loyalty programs is the backbone of Datalogix’s advertising metrics business

What data is actually exchanged?

Datalogix assesses the impact of Facebook advertisements on shopping in the physical world.

Datalogix begins by providing Facebook with a (presumably enormous) dataset that includes hashed email addresses, hashed phone numbers, and Datalogix ID numbers for everyone they’re tracking. Using the information Facebook already has about its own users, Facebook then tests various email addresses and phone numbers against this dataset until it has a long list of the Datalogix ID numbers associated with different Facebook users.

Facebook then creates groups of users based on their online activity. For example, all users who saw a particular advertisement might be Group A, and all users who didn’t see that ad might be Group B. Then Facebook will give Datalogix a list of the Datalogix ID numbers associated with everyone in Groups A and B and ask Datalogix specific questions – for example, how many people in each group bought Ocean Spray cranberry juice? Datalogix then generates a report about how many people in Group A bought cranberry juice and how many people in Group B bought cranberry juice. This will provide Facebook with data about how well an ad is performing, but because the results are aggregated by groups, Facebook shouldn’t have details on whether a specific user bought a specific product. And Datalogix won’t know anything new about the users other than the fact that Facebook was interested in knowing whether they bought cranberry juice.

This is very interesting and powerful

But lets think beyond store cards .. Think IoT / Beacons

Substitute ‘store cards’ with ‘Retail IoT’ and you have a very unique models that could power IoT in Retail powered by IoT analytics

Beacon based shopping alredy exists via companies like estimote

So, my point is .. the model(independent  of Datalogix the company) could be used to close the loop between the Physical and the social. IoT / Data Science / Data analytics will pay a key role here

Comments welcome on twitter @ajitjaokar

IoT and the Rise of the Predictive Organization

 

 

 

 

 

 

 

 

 

 

 

I will be launching a newsletter starting in Jan 2015 to cover these ideas in detail.

You can sign up for the newsletter at futuretext IoT Machine Learning – Predictive Analytics – newsletter

I will also be launching a course/certification for “Data Science in IoT” at Oxford, London and San Francisco – email me at ajit.jaokar at futuretext.com if you want to know more

 

In the Godfather II, Hyman Roth said to Micheal Corleone

             ’Michael – we are bigger than US Steel“.

Over the holiday season,  I said this to my friend Jeremy Geelan when I was comparing the Mobile industry to the IoT.

The term Internet of Things was coined by the British technologist Kevin Ashton in 1999, to describe a system where the Internet is connected to the physical world via ubiquitous sensors. Languishing depths of academia(at least here in Europe …) – IoT had it’s netscape moment early in 2014 when Google acquired Nest

Mobile is huge and has dominated the Tech landscape for the last decade.

But the Internet of Things(IoT) will be bigger.

How big?

Here are some numbers. Souce (adapted from  David Wood blog )

By 2020, we are expected to have 50 billion connected devices

To put in context:

  • The first commercial citywide cellular network was launched in Japan by NTT in 1979.
  • The milestone of 1 billion mobile phone connections was reached in 2002.
  • The 2 billion mobile phone connections milestone was reached in 2005.
  • The 3 billion mobile phone connections milestone was reached in 2007.
  • The 4 billion mobile phone connections milestone was reached in February 2009.
  • We reached 7.2 billion active mobile connections 2014

So, 50 billion by 2020 is a massive number by a factor, and no one doubts that number any more.

But IoT is much more than the number of connections – it’s all about the Data and the intelligence that can be gleaned from the Data.

As more objects are becoming embedded with sensors and gain the ability to communicate, new business models emerge.

IoT also creates new pathways for information to travel – especially across an Organization’s bounday and across it’s value chain and in engaging with their customers.

This Data and the Intelligence gleaned from it – will fundamentally transform organizations creating a new kind of ‘Predictive Organization’ which has Predictive analytics / Machine Learning at it’s core i.e. Algorithms that will learn from experience.

Machine learning is the study of algorithms and systems that improve their performance with experience. There are broadly two ways for algorithms to learn:  Supervised learning(where the algorithm is trained in advance using labelled data sets) and unsuprevised learning (with no prior learning – ex with methods like Clustering etc).

Machine Learning algorithms take the billions of Data points as inputs and extract actionable insights from ther data. So, the Predictive Organization starts with the prediction process and then creates a feedback loop through measuring and managing. Crucially, this tales place across the boundary of the Enterprise

I believe there are twelve unique characterictics of IoT based Predictive analytics/machine learning

1)     Time Series Data: Processing sensor data.

2)     Beyond sensing: Using Data for improving lives and businesses.

3)     Managing IoT Data.

4)     The Predictive Organization: Rethinking the edges of the Enterprise: Supply Chain and CRM impact

5)     Decisions at the ‘Edge’

6)     Real time processing.

7)     Cognitive computing – Image processing and beyond.

8)     Managing Massive Geographic scale.

9)     Cloud and Virtualization.

10)  Integration with Hardware.

11)  Rethinking existing Machine Learning Algorithms  for the IoT world.

12)  Co-relating IoT data to social data – the Datalogix model for IoT

Indeed one could argue that IoT leads to the creation of new types of organization – for instance  based on the sharing economy based on converging the digital and the physical world.

I will be launching a newsletter starting in Jan 2015 to cover these ideas in detail.

You can sign up for the newsletter at futuretext IoT Machine Learning – Predictive Analytics – newsletter

I will also be launching a course/certification for “Data Science in IoT” at Oxford, London and San Francisco - email me at ajit.jaokar at futuretext.com if you want to know more

Image source: wikipedia

IoT Machine Learning – Predictive Analytics – newsletter

 

 

 

Greetings!

In January,  I am launching a newsletter focusing on IoT, Machine Learning and Predictive analytics

This is a key, complex domain which I believe will be very significant going forward

some of the themes I will cover are:

Time series data from sensors 

Real time analytics

Streaming

In memory databases etc

Startup business models

Question is:

What other topics should I include considering the niche theme i.e. IoT and Machine Learning / Predictive Analytics

You can sign up on www.futuretext.com or email me at ajit.jaokar@futuretext.com or respond in Twitter @ajitjaokar

Image source: http://ml.cmu.edu/

ForumOxford: Internet of Things Conference 2015 listed among 40 most important #IoT events to attend this year ..

What a nice way to end the year ..

Jeremey Geelan who created a list of the top 40 Internet of Things Conferences to attend in 2015 has added the forumoxford : 2015 Internet of Things conference  to the list of 40 important Internet of Things conferences for 2015

Date: 6 November, 2014

Venue: Rewley House, University of Oxford
URL: forumoxford : 2015 Internet of Things conference

co-chaired by me and Tomi Ahonen. Now in it’s 10th year. Mark the dates!

full list again  list of the top 40 Internet of Things Conferences to attend in 2015

 

Infographic – The evolution of wireless networks

PS
I get many such requests to post infographics ..
But this one is good
Comes from a reliable source (New Jersey Institute of Technology - Online Masters of Science in Electrical Engineering)

Infographic – The evolution of wireless networks

New Jersey Institute of Technology’s Online Master of Science in Electrical Engineering