Matt Stabeler

Gave presentation to Paddy, Davide, Neil Cowzer and Fergal Reid (clique) about my quick and dirty analysis of the dataset that I have collected allready.

Slides

General concensus was that there was not really enough users, and so there were some suggestions about other datasets that might be found -persuade a mobile phone company to give data about user movements. Mine flickr/twitter for geo-tagged photo’s/tweets, and try to determine groups of people based on similar locations.

Also suggested that the GMA is good for visualising data, not greatly interesting, PH is interesting as is SPD. BD is something that is useful as an application to gather data, but would need a very large engineering effort.

Paddy suggested that if we could make the data collection process very easy, then we could throw it out to the student population to start collecting data. Fergal said that in J2ME it would be very difficult, but by sticking to C++ it might work (for Nokia phones).

Also talked about getting ground truth for data, Fergal Suggested collecting accellorometer data too (so if someone asked – how did you verify GPS trace, one can say that we correlated it with the accelorometer data). I also suggested tagging locations.

Determined the following actions:

• Look for access to datasets with good location – 1 week
  • WaveLAN Dataset
  • HeaNET – chase paddy – Eduroam
  • Mine location data from Flickr
• Look at applying analysis to these datasets – specifically
  • Periodicity Hunting
  • Spatial Dependance on the Degree
• See if we can construct overlay over these networks
  • e.g. drop nodes
    • Popular locations
    • popular people
    • Other?
    • Vector clocks might be the way to do it
• Read up about Vector Clocks as suggested in the paper by Klineberg, Watts and ???? at  KDDOA
• Speak to Graham about whether I can easily integrate this data into his code, if so – do it, otherwise think about implementing it seperately(robustly!)

Also planned to meet Paddy again next week to go over these things, and try to hammer out a better plan. Then meet with these people again in three weeks to show where I have go to.

Davide also talked about churn in proximity patterns – might be worth thinking about what this means (example was then a person regularly sees other people, and after a while, one of those people drops off the radar – what does this mean)

Paddy said that in his mind, the long goal is to be able to forward plan using the knowledge of data that has passed (prediction).

Three types of data analysis:

General Mobility Analysis

We calculate the distance between locations at the start of every time period, (e.g. 1 hour) and plot the number of time that particular distance is travelled (to some granularity) over some time period (1 week maybe)

Periodicity Hunting

We measure the time spent at a location, and count the number of times in a bounded time period (say a week), using the same timescale as above to bracket readings.

(people visit common locations frequenly, or the visit some locations for a long period of time. – also think about the case that lots of people visit a common location infrequently/frequently).

Statial Dependance of the Degree

We count the number of devices seen in a given time period (same as above – e.g. 1 hour) and the location

Buddy Discovery

We count the duration of the contacts between pairs (the user and the devices he can see) and also the location of the contacts, and try to see which devices are seen most often, and then try to see which devices are seen at multiple locations. (using the same time period as above – 1 hour slots over a week)

Met with Paddy and Davide and discussed what we have been doing.

• Actions from last meeting:
• Said that I had been collecting data which seems to have good location information.
• Had spoken with prag etc. but not really very useful
• Davide has come up with some great questions for analysis of data
• The only thing I hadn’t done was arrange a presentation for findings so far.

Paddy was happy with the progress so far, and after we discussed a number of things, we came to the following action points:

1. Do a quick and dirty analysis of data
   1. Mobility analysis
   2. Periodicity
   3. Buddys
   4. Spatial degree
   5. Situation detection e.g. what does periodiciy mean?
2. This is so that we can ask:
   • Do we have the data we need already?
   • What are the limitations of the data?
   • Are there other questions we need to ask?
3. Plan a presentation for next wednesday morning (more of a brainstorm) to develop the ideas further, and really try to hammer down the larger plan

Paddy also suggested that we think about putting a paper into ubicomp (deadline 13th March) about our analysis of this data, but put a spin on it, e.g. what does periodicity mean? Can we predict events based on this? – Can we infer some useful context, based simply on the structure of the data, without the need for advanced techniques ( – i call this Urban Guerilla Sensing).

We suggested that we might be able to do two applications based on one of buddy finding analysis part (see mobile_agents and PhD the Story) the first, Paddy dubbed F3 (Facebook Friend Finder) where we encourage people to collect data for us, in return for detecting the presence of other facebook users, and suggesting friends based on frequency of co-location. The second was a similar application, but for regular visitors to research seminars.

I mentioned my vision on the next three points of reference, the first being a paper about the collection and analysis of this dataset, the second being another work which tied this into an simulator for the dataset, which synthesises this data in to a generic set, which can be used to test MANETs etc. The final thing (I didn’t get this far) being the final writeup of my PhD which brings all of these ideas together.

Paddy likes this, and suggested the idea of Pattern Language (used to desrcribe patterns in software engineering) which had recently been applied to Ubicomp environments to describe patterns in situations, Paddy thought that this might be particularly relevent to this, and that he would like to see some language of description emerge from our analysis. This sounds like a great idea. 🙂

Finally, Paddy spoke anbo

Had a discussion with Davide in our cube, about the direction for 2010, he summarised it in an email as follows:

Here are a few ideas which came out in our chat this afternoon…

1. We have to set up a plan for your PhD: it should be something related to mobile phone networks: collecting a rich dataset (looking for what, in particular?), using it (which analysis shall we do on it?), and writing (useful) applications based on that.

2. It would be interesting to study and discover hidden patterns in the phone-mobility network. For example, a person could attend the same locations of somebody else wihtout noticing it, and this could be exploited in finding new friends on FB or new collaborations in a research environment.

3. Mobile phones could be used as probes to collect spatial informations, such as popularity, traffic, access points, temperature, pressure, ecc…

4. An application could be based on finding out a useful spot for installing a power socket, or discovering somebody with a charger when I need it…

5. It would be interesting to check if we can find out “meeting with strangers” in the dataset by Kevin McCarthy, but we still should ask them if they know each other or not.

6. We are going to meet with Kevin McCarthy this month and ask him what they are going to do with their data (and maybe do some work together).

The main points I remember are:

I said that over Christmas I had realised that what we/I am really interested in, is discovering the hidden patterns of life that are around us all the time, but that we cannot see, and in this example, the movement of the inhabitants of a city.

There needs to be some application for this knowledge, for example, Davide suggested a social network application (see above) that detects the presence of other users, and if you are not allready friends on facebook, then maybe you should be if you come into contact with them regularly. He suggested it might be better if we confined it to researchers in a seminar – if you attend the same seminars as others, you should probably be aquainted.

I re-iterated my idea about having a platform upon which many applications could sit, for example the FB app. (above) sits next to other applications that run over the general system, which gives methods and triggers to the application when another user is in proximity (e.g. who is nearby, and methods to communicate with them). This could be implented directly (using bluetooth lookups etc.) or centrally using a combination of location, bluetooth lookups and a shared server.

This platform could be used as a test-bed for researchers too – e.g. researchers could run simple experiments over this network such as testing infection rates in a more realistic environment.

The carrot is the intersting applications that users can use, the reward (for us) is collecting this social interaction data and the stick is having to run the application on the phone.

Had a meeting with Davide to discuss current research,

we talked about what I had discussed with Paddy, and Davide seemed to think this is effectively the same as the E-DTN position paper, and that we should pursue these ideas further.

We decided that in lieu of getting access to largert datasets, we should look at the ones we have allready.  I suggested that we use the Tom dataset that was collected over a week or so when Tom used the N95 I programmed – as a start this might help us to see what the data looks like. He suggested the following tasks:

• Contact Eiko Yoneki to see if she has some datasets that we could use
  • UPDATE: She didn’t have any datasets and she is eager to get some herself…
• Look at Grahams simulator, understand it, and extend it to include the ability for location data to be incorporated
• Generate two graphs from the Tom data (which he called the TomStalker dataset):
  • Latitude vs Longitude vs Number devices seen (3d)
  • Frequency of device spotting vs time
• Look into taking a statistics course at ucd, to learn the techniques of statisical analysis
• Include him in correspondance about this

Had a brief meeting with Prag Sharma, who described to me the sort of things that the clique group were doing.

I explained to him that I was interested in ways of analysing social networks in terms of movement patterns. He mentioned a few datasets that clique has some access to: Conrad and Fergul have access handset data from 6 million Nodes from a telephone network – IDIRO, but he did not know what the data included. Another dataset was NORON data, which is to do with financial fraud and included banking transaction data.

He suggested that a good person to talk to was Derek Green, who he thought was doing  similar work.

We saw Derek in is cube, and it seems he is looking more at social clustering, but we thought there might be some interesting overlap, so I will send him my position paper, and he will send me his recent presentation.

Prag suggested I check out the clique website.

Had a meeting with Paddy (Audio here)

Actions:

• project/ experiment -find some location data extract and parse it,
  • Have to have a set of questions we are going to ask of the data
  • Looking for structures in the data – what are they?
• extract soem temporal and strcutural patterns
• Speak to prag (network clique) to find techniques on how to extract patterns from these networks.
• Email Simon, Aaron and Adrian, to see if they know about, or have dealt with any large datasets which include accurate location information.
• In 6 weeks present to a group of people about initial findings, and ideas for further research. but beforehand, keep in sync with Paddy, and make sure I don’t go off track. Keep a record in a wave about progress.
• Also, investigate movement patterns.

Justifications for this:

Burst Situations – emergency situations – however movement patterns are different in this case.

Focus in on simple pragmatics – I want to be able to send free messages.

Pick social everyday applications that justify it – (parasitic networking?).

Need to focus on detailed stuff – get numbers,

Focus on movement patterns – what recurring patterns are in there – need to data mine it all.

How do we collect data, sufficiently large, and in constrained sets.

Padraig Cunningham group doing network analysis – We can use Paddy’s Social network to get help with things like statistics etc.

Extracitng movement patterns from everyday life – augemtnting with context information.

Set up experments

do blind first  – capture everything from individula, apply analysis just to movement informations plus simulation, then you take in to accoutn context information and see if it improves anything. Theres nothing to say that context is useful all the time. Perhaps we find it reduces the number of hops, this reduces failure rate.

Chapter 1 is the broad view – DTNs Human Movent – underlying model of data transmission. my hypothersis is that human moevemtn patterns will provide an underlying model for transmission of data on ad-hoc networks.

Next actions:

Think up and construct an experiment and do it – short term 3-4 week expt whewre we actually get somehting out of it – needs to be a small constrianed expriment, that takes into account movement and context.

Have to have a set of questions we are going to ask of the data:

Looking for structures in th data what are they?

couple of conversations: Prag Sharma – good talking point – i’ve got this data, I want to use thi

try to characterise the patterns – they might be structural patterns, or temporal –

Had a skype meeting with paddy, and spoke about what I was going to do regards PhD (2009-12-07-time-15_24_48-Skype Call With Paddy)

• I need to: Persuade Paddy that Natural Dynamic Networks idea don’t already exist in the literature
• Convince him that that eperiments that I might construct are actually interesting for some reason – i.e. what is the point
• Really nice to have 1 paragrah story line that we both agree tomorrow that we only change if the experiments tell us so, not just because something else comes along.

Need to be able to say – here’s my definition, here’s what it means, here’s what im gonna experiment on in the next 3-4 months.

Task is to come up with bullet points:

• here’s my definition of NDN
• hows its different from the way others people view networks?
• If this is true, then we can do the following … A, B, C
• if this is tru, then the experiments we can do are …

Forms the starting points of tomorrows meeting, that, we believe the first statement – that nobody has done this before.

Paddy will try to find some literature to find a few rocks to throw at me! so we can defend it.

If we can defend that – we can really question – we conjecture that we might be able to do the following.

Get arguments prepared for:

If we can do opportunistic routing – what do we get out of it, why is it better than what we have at the moment?

Had a meeting with Paddy in O’Sheas.
The main outcome was that I need to come up with some refined research questions, in the next two and a half weeks, that really define what I will do for my PhD, they should be structured in a way that must really get to the point about what people have done so far, what we will do, and how we think this will make some difference. It must be clear what our work compares to and what metrics it can be measured agains. At the lower level, we have to show how these questions lead us to the experiments we want to perform, and how these experiments will help us prove our point. At the highest level, we need to step back and look at the big picture, and see what it is that our work contributes to .

Practically, I will start a google wave as part of the workup of these ideas, which I will share with Paddy, the protocol being that when one of us makes a change, we will email the other.

When Paddy is back, we will get together to go over what we have come up with, and then later in the week I will present a few pages of my ideas to GW, DC and others, to get their input on the direction of the research.

Paddy also said that funding wise, his plan is as follows: He will guarantee that fees will be paid when IRCSET runs out, and if I cannot get an extension, he will make sure that I can get funding from somewhere if he can. The overall plan being that I have another 14 months of funding, meaning that I should spend the next 8 months working on PhD experiments and ideas etc. Then the final 6 months writing up.

Paddy also asked if I wanted to become a part of Clarity,  in the sense that I would get access to their data, and they would be able to tick a box for extra people in Clarity seperately funded by IRCSET.  I did not make a decision, but it sounded like a good idea.

Paddy also asked if I would cover a lecture this week on P2P for Distributed Systems, and when I asked if I would get Leturers pay for it, he said he couldn’t pay me, but he would buy a small piece of technology for me.

I also asked about the possibility of getting  a new laptop, and he said that should be ok, and I spec one up that is not too expensive, but is still future proof . (IBM-Lenovo/Dell/Apple/)

As a suggestion towards good time management, Paddy suggested I make sure I am working on PhD stuff 4 days a week, and spend only 1 day working on related projects.

Had a discussion with GW about other things to test within datasets, for example, what use is it to simulate message passing between users who do not know each other, and if we had some knowledge about this data, can we find a more efficient way to route messages. This led us to talk about:

• how many nodes can you get to within x hops in a network?
  • how many paths between friends need to use strangers and vice versa – can be used rto define privacy rules too 🙂
  • see miklas paper about defining friends and strangers
  • The point being that you may never need to send a message to any body else

Graham showed me how to run the simulator he wrote, and we talked about writing a paper together for an upcoming conference – but decided the deadline was too soon, and that we didn’t have any new results to put into it.