A Solid foundation for social apps?

Tim Berners-Lee recently posted an open letter on the web, announcing the launch of Solid, a new technology platform that he and his team at MIT have been working on for the past few years, to the wider online community. Like a lot of people these days, he’s not too happy about the way our personal data is being controlled and exploited by providers of online services, but when the father of the web is telling you how it’s not gone the way he intended, you may want to prick up your ears even if you personally have no problem with the way things are. Not only that, but when he says he’s come up with something that we can use to set things right, it’s probably worth checking out.

We’ve all seen the headlines that result when a company with a business model based on aggregating and monetising personal data gets negligent or unscrupulous with the data in its possession, but these incidents are really just symptoms of a more fundamental issue concerning the architecture of basically every popular online social application out there. Even if we imagine a perfect world of ideal application providers that are completely open and honest about how they use your data and never suffer any security breaches, the fact remains that they, not you, control the data you’ve given them. You still own it, yes, but they control it.

Why is this an important distinction? The answer has to do with the coupling of your data with the specific services you’re using: you can’t have one without the other. As a result, your data is broken up into pieces that are kept in separate bins, one for each service, even when it would be really to helpful to have it all in the same place. If you want to use several services that all use the same data, you have to upload it to each one separately, and that’s assuming that you have or can get the data in a reusable format, which isn’t always the case. It would make a lot more sense to have just a single copy of the data and permit the services to access that – within privacy parameters that you have complete control of – and it would be even better if you could move your data to a different location without breaking all those services that depend on it.

Sound good? Well, the people behind Solid apparently want you to be able to do just that. Their proposed solution is based on decoupling data from applications and storing it in units called PODs (short for personal online data store). Applications built on the Solid platform can access the data in your POD if you give them permission to do so, but they don’t control the data, so they can’t impose any artificial restrictions on how you use, combine and reuse data from different sources. The end-users of Solid are thus empowered to make the best possible use of their data while retaining full control of what data they disclose and to whom, which is very much what I’m aiming for in my own research; I can easily see collaborative knowledge discovery as an app implemented on Solid or some similar platform.

So that’s the theory, anyway. What about reality? I can’t claim to have examined the platform in great depth, but at least on the surface, there are a number of things that I like about it. It’s built on established W3C specifications in what looks like a rather elegant way where linked data technologies are used to identify data resources and to represent semantic links between them – for example, between a photo published by one user and a comment on the photo posted by another. Besides your data, your POD also holds your identity that you use to access various resources, somewhat like you can now use your Google or Facebook credentials to log in to other vendors’ services, but without the dependence on a specific service to authenticate your identity. Of course, you still need to get your Solid POD from somewhere, but you’re free to choose whichever provider suits you best, or even to set up your own Solid server if you have the motivation and the means.

Whether Solid will catch on as a platform for a new class of social web apps is not just a matter of whether it is technically up to the challenge, though. The point of social media is very much to have everyone in your social network using the same applications, so the early adopters won’t have much of an impact if their friends decide that it’s just so much more convenient to keep using the apps where they already have all their connections and content rather than to switch platforms and build everything up all over again – which, of course, is precisely the sort of thinking the providers of those apps are counting on and actively reinforcing. People like me may give Solid a go out of sheer curiosity, but I suspect that the majority can’t be bothered unless there are Solid apps available that let them do things they really want to do but haven’t been able to before. Taking control of your own data is a noble principle for sure, but is it enough to attract a critical mass of users?

Then there’s the question of how the Solid ecosystem will work from a business perspective. The supply of interesting applications is going to be quite limited unless there’s money to be made by developing them, and presumably the revenue-generation models of centralised social apps can’t be simply dropped in a decentralised environment such as Solid without any modifications. We pretty much take it for granted now that we can “pay” for certain kinds of services through the act of using them and generating data for the service provider to use as raw material for services that the provider’s actual customers will pay good money for, but would – and should – this work if the provider could no longer control the data? On the other hand, would we be willing to pay for these services in cash rather than data, now that we’ve grown so used to getting them for “free”? Then again, there was a time when it was not at all clear how some of today’s multi-billion-dollar companies were ever going to turn a profit, so maybe we just need the right sort of minds to take an interest for these things to get figured out.

It’s also worth noting that Solid is by no means the only project aiming to make the web less centralised and more collaborative. There is a substantial community of researchers and developers working on solutions to various problems in this area, as evidenced by the fact that Solid is but one of dozens of projects showcased at the recent DWeb Summit in San Francisco, so it may well turn out that even if Solid itself won’t take off, some other similar thing will. I won’t be betting any money on any of the contenders just yet, but I probably will get myself a Solid POD to play with so I can get a better idea of what you can do with it.

Tips for MSCA hopefuls

This year’s call for MSCA Individual Fellowship applications closed recently, with a grand total of 9,830 applications received – apparently a record number for MSCA and in fact for Horizon 2020 in general. Good luck to everyone who submitted! Soon after I started my own fellowship at the Insight Centre, I was invited by one of the people who helped me prepare my proposal to participate in a seminar on MSCA and speak on my experiences as a successful candidate. The presentation I gave there was quite well received, so I thought I’d share my little tips and tricks in the blog as well, even though the timing isn’t arguably the greatest, given that it won’t be until sometime in the spring that the next call opens.

First of all, if you’re considering applying but having some doubts, I heartily recommend that you go through with it. Although technically MSCA fellowships are H2020 projects, which may sound a bit frightening, the proposal process is actually quite lightweight, with the length of the research plan limited to ten pages and the budget being a simple function of which country you’re going to, how many months you will spend there and whether you have a family. The same goes for how the projects are managed, so you don’t need to worry that you’ll end up spending an inordinate portion of your precious research time cranking out deliverables instead of generating results. So, without further ado, here are my top 5 tips for would-be MSCA fellows:

1. Find the right host

I’ve already mentioned in a previous post that it boosts your chances considerably if the strengths of your prospective host complement yours. It certainly doesn’t hurt if there’s someone at the host institution – ideally, your prospective supervisor – that you already know and have developed a rapport with, but you shouldn’t get too hung up on that particular point; what really matters from the reviewers’ point of view is whether the place where you are proposing to carry out your project is the best possible environment for that project. Consider what the host can offer you in terms of things such as training, research infrastructure and potential collaborators, and make sure that you have a persuasive argument that comes across in your proposal. Also, keep in mind that there is expected to be two-way knowledge transfer between the researcher and the host, so it’s not just about what you can get from the host – it’s also about what you can bring to the host.

2. Get all the help you can

The most important part of the proposal is the actual research to be done – objectives, methodology, etc. – and that’s all up to you (plus, to a certain extent, your supervisor of course, but they’re likely to have quite a few things on their plate besides this). However, for everything else, don’t hesitate to take advantage of any support that the host institution can offer you in preparing the proposal. The odds are that there are people there who have done this sort of thing before and know what reviewers look for in a proposal in terms of facts and figures, hosting arrangements, available research services and so forth. They may also have access to external experts and offer to send your proposal to them for feedback, and I think it goes without saying that you should accept such an offer. What I found particularly useful was ideas on how to communicate my research results to non-academic audiences, since my first instinct (and I’m pretty sure I’m not alone in this) is to just write papers for journals and conferences and let others worry about public relations, and this cost me some crucial points when I applied for the first time.

3. It’s all about you

This is another thing I touched upon in that earlier post: MSCA fellowships are unusual, if not unique, in that their impact is measured in terms of the career development of the fellow as a European researcher. Therefore you should consider starting not with the question “What do I want to study?” but with “What do I want to be?” The answer won’t give you your research topic, but it will affect the way you go about choosing one and developing a plan around it. Do you want to work in academia or industry? In what sort of role? Or maybe you’re interested in starting your own company? Whatever your target is, state it clearly in the proposal and make sure that everything else in the proposal – research activities, training, etc. – is aligned with that target. If you’re not quite sure what you want and would prefer to keep your options open, pick a career goal anyway and pretend that you do know that’s where you’re headed; there’s nothing wrong with changing your mind later, but it doesn’t look good if you don’t seem to have any sort of long-term vision of your career. Of course, if you’ve come up with a work plan that can support multiple career paths equally well, it shouldn’t hurt if you point this out in the proposal.

4. Give details generously

This is really a more general formulation of the previous point: it can be tempting to keep things a bit vague, but every bit of vagueness will make your proposal seem that much less convincing – and remember, the bar is high and competition fierce, so every little bit counts. This goes for your career objectives, but other things as well; for example, when describing how you plan to disseminate the results of your research, try to come up with tentative titles for the papers you’re going to write and to identify specific journals and conferences where you will aim to publish those papers. If you can name some likely co-authors, even better, and it’s also good to consider how you will measure the impact of your dissemination and communication activities (e.g., number of paper citations, number of people reached). Likewise, in your implementation plan, provide as much detail as you can (without breaking the page limit) on things such as work breakdown, timetables, deliverables and milestones; in the real world, you won’t be expected to follow that plan to the letter, but you do need to demonstrate in the proposal that there’s a clear path from where you are now to where you want to be at the end of the project.

5. Focus your efforts right

Having only ten pages to explain your research plan in full detail is a blessing but also a curse, because rationing out those ten pages between the things you want to say may prove quite a challenge. To have an idea of where you should be concentrating your best efforts, keep always in mind the three evaluation criteria and their weights relative to your overall score: excellence counts for 50%, impact for 30% and implementation for 20%, so it’s a good rule of thumb to allot 5, 3 and 2 pages for the corresponding proposal sections, respectively. However, if you’re working on a revision of a proposal that didn’t get funding the first time around, you also need to consider your previous evaluation scores, because the law of diminishing returns applies as your score for a given criterion approaches 5. So, if you did very well on excellence but not quite as well on the other two criteria, you’re likely to get a bigger increase in your total score for the same amount of effort if you focus on impact and implementation, even though excellence weighs as much as the other two combined. You’ll definitely want to improve any criterion score lower than 4, and the verbal feedback in the evaluation report should give you a pretty good idea of how you can do that.

So that’s it! I hope you found these tips useful and will come back to them when it’s time to start preparing an application for the next MSCA IF call.

First blood

Time to look at the first results from my project! Well, not quite – the first results are in a literature survey I did immediately after starting the project and made into a journal manuscript. I’m currently waiting for the first round of reviews to come in, but in the meantime I’ve been busy developing my ideas about collaborative knowledge discovery into something a bit more concrete. In particular, I’ve been thinking about one of the potential obstacles to effective collaboration from the data owner’s perspective: privacy.

In the aftermath of the much publicised Facebook–Cambridge Analytica scandal, one would at least hope that people are becoming more wary about sharing their personal data online. On the other hand, with the General Data Protection Regulation in full effect since 25 May, a huge number of people are now covered by a piece of legislation that grants them an extensive set of personal data control rights and has the power to hurt even really big players (like Facebook) if they don’t respect those rights. Of course, it’s still up to the people to actually exercise their rights, which may or may not happen, but after all the GDPR news, emails and “we use cookies” notices on websites, they should be at least vaguely aware that they have them.

The increased awareness of threats to privacy online and the assertion of individuals, rather than corporations, as the owners of their personal data are welcome developments, and I like to think that what I’m trying to accomplish is well aligned with these themes. After all, the collaborative knowledge discovery platform I’m building is intended to empower individual data owners: to help them extract knowledge from their own data for their own benefit. This does not make the privacy issue a trivial one, however – in fact, I wouldn’t be surprised if it turned out that people are more uneasy about sharing a small portion their data with an individual analyst focusing on their case specifically than about using an online service that grabs and mines all the data it can but does so in a completely impersonal manner. The platform will need to address this issue somehow lest it end up defeating its own purpose.

The angle from which I decided to approach the problem involves using a domain ontology and a semantic reasoner, which are technologies that I had been interested in for quite some time but hadn’t really done anything with. As I was doing the literature survey, I became increasingly convinced that an underlying ontology would be one of the key building blocks of the new platform, but it was also clear to me that I would need to start by modelling some individual aspect of collaboration as a proof of concept, so that I would fail fast if it came to that. If I started working top-down to produce a comprehensive representation of the entire domain, in the worst case I might take ages to discover nothing but that it wasn’t a very viable approach after all.

All this came together somewhat serendipitously when I found out that the 2nd International Workshop on Personal Analytics and Privacy (PAP 2018), held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018) in Dublin, had an open call for papers. The submission deadline was coming up in a month – enough time to put together some tentative results, though nothing hugely impressive – and coincided rather nicely with the date when I was planning to fly to Finland for my summer holidays. In about two weeks I had the first version of the manuscript ready, with another two left over for revisions.

The ontology I designed is based on the idea of a data owner and a data analyst (or possibly any number of either) using the collaborative knowledge discovery platform to negotiate the terms of their collaboration. Each uses the platform to specify requirements, but from opposing perspectives: the data analyst specifies analysis tasks, which require certain data items as input, while the data owner specifies privacy constraints, which prevent certain data items from being released to the data analyst. The data owners, data analysts, data items, analysis tasks and privacy constraints are all registered as individuals in the ontology and linked with one another such that a reasoner is able to use this information to detect conflicts, that is, situations where a data item is required for a data analysis task but not released by the data owner.

To resolve such conflicts, the data owner and the data analyst may, for example, agree that the analyst receives a version of the dataset from which the most sensitive information has been removed. Removing information reduces the utility of the data, but does not necessarily make it completely useless; finding a balance where the data owner’s privacy preferences are satisfied while the data analyst still gets enough material to work with is the essence of the negotiation process. The ontology is meant to support this process by not just pointing out conflicts, but by suggesting possible resolutions based on recorded knowledge about the utility effects of different methods of transforming data to make it less sensitive.

For the PAP workshop paper, I only had time to design the logic of conflict detection in any detail, and there also was no time to test the ontology in a real-world scenario or even a plausible approximation of one. It therefore hardly seems unfair that although the paper was accepted for a short oral presentation at the workshop, it was not accepted for inclusion in the post-proceedings. Obviously it would have been nicer to get a proper publication out of it, but I decided to go ahead and give the presentation anyway – ECML-PKDD is the sort of conference I might have gone to even if I didn’t have anything to present, and since the venue is a 25-minute walk away from my house, the only cost was the registration fee, which I could easily afford from the rather generous allowance for sundry expenses that came with the MSCA fellowship.

Croke Park may seem like an unlikely place to have a conference, but it is in fact a conference centre as well as a stadium, and seems to work perfectly well as a venue for an academic event – meeting spaces, catering and all. Besides Croke Park, we had Mansion House for the welcome reception and Taylor’s Three Rock for the conference banquet, so can’t complain about the locations. The regular programme was quite heavy on algorithms, which isn’t really my number one area of interest, but I did manage to catch some interesting application-oriented papers and software demos. What I enjoyed the most, however, were the keynote talks by Corinna Cortes, Misha Bilenko and Aris Gionis; there were two others that I’m sure I also would have found very interesting but was unable to attend, because there was a rather important deadline coming up and so I had to zig-zag between Croke Park and DCU to make sure I got everything finished on time.

My own talk went reasonably well I felt, with an audience of about twenty and some useful discussion afterwards on how I might go about modelling and quantifying the concept of utility reduction. On the last day of the conference, which was today, I went to another workshop, the 3rd Workshop on Data Science for Social Good (SoGood 2018), with presentations on how machine learning and data mining techniques can be used to address societal issues such as homelessness and corruption. I especially enjoyed the last one, if enjoy is the right word – it dealt with efforts to combat human trafficking by means of data science, certainly a worthy cause if ever there was one, but also rife with difficulties from the scarcity of good input data to the nigh-impossibility of devising an ethically justifiable experiment when there are literally lives at stake. Plenty of food for thought there, and a fine way to finish off this week of conference activities; on Monday it’s back to business as usual.

Rocky road to Dublin

When I first arrived at DCU to begin my MSCA fellowship, my supervisor paraded me around the place introducing me to various people, most of whose names I promptly forgot. (Sorry!) What did stick in my mind, however, were the numerous congratulations I received on winning such a competitive grant, which the Marie Curie fellowship certainly is, and on getting such a sweet deal, which it most definitely is. But it wasn’t all good times and glowing reviews, the way here – far from it, in fact. That’s why I thought I’d share the story of how I came to be in Dublin and how things might have turned out quite differently, had I been a bit less perseverant than I was.

I got my doctorate in 2014 at the relatively mature age of 35, having gone through a process that was a good deal more convoluted than it was, in theory, supposed to be. Following my academic baptism of fire, working as a Master’s student in a project dealing with computational quality assurance of spot welding joints, I embarked on a rather erratic journey that saw me dabble with research topics as varied as exergame design and ethics of scam baiting. I eventually pieced together my dissertation around the theme of knowledge discovery in databases, focusing on what the overall process looks like from different perspectives and how it can be supported effectively.

Not only was the process of writing my thesis unnecessarily complicated, but so was the process of getting the manuscript reviewed and accepted for publication. When the pre-examination phase was finally over and I was galloping, or at least cantering briskly, into the home stretch, there turned out to be one more obstacle to clear: finding an external examiner for the defence. I pitched a number of names to my then-supervisor, but one by one they all respectfully declined the invitation, not having the time to spare or not feeling that the topic of the thesis was close enough to their area of expertise. Fortunately, one of them offered the suggestion that Alan Smeaton of DCU might be our man.

This turned out to be a considerable stroke of luck: Alan was indeed kind enough to accept the job, and his style of handling it was very much what I’d hoped for, making this test of my ability to defend my magnum opus feel much less like a test and more like a friendly conversation on a topic of mutual interest. Some among the audience even described the proceedings as entertaining, which is hardly the word that first comes to my mind when I think about thesis defences! In all honesty, it’s not the word I’d choose to describe mine either – I was far too nervous to be entertained – but it did feel quite rewarding after all the hard work (not to mention a not negligible amount of self-doubt) to talk for several hours as an equal to a senior academic who had taken such an interest in my research.

Having thus finished my doctorate, it was time to make some decisions. I felt that I wasn’t quite done with academia yet, but I also felt that I needed some kind of change, and besides, there’s a certain amount of pressure on fresh post-docs to go explore the world beyond their alma mater for a while. Since I happen to have a great deal of appreciation for many things Irish (including, but not limited to, grain-based fermented beverages and traditional music), this seemed like a potential opportunity to combine business with pleasure, so the next time I visited Dublin, I met up with Alan to have a cup of tea and a chat on the possibility of moving there to work with him, provided that some funding could be secured. Later that year, we submitted our first proposal for an MSCA Individual Fellowship to the European Commission.

MSCA wasn’t the only funding source we considered; I also applied to the Academy of Finland for a grant that would have involved me mostly staying in Finland at the University of Oulu but spending a mobility period of six months or so at DCU. However, the feedback I got from the Academy did not paint a rosy picture of my chances of winning the grant even after several iterations, and in the meantime, I was feeling rather aimless in my research and finding the idea of switching to industry more and more attractive. Unsurprisingly, my publication record for these past few years is not exactly impressive, but thankfully, the MSCA reviewers seemed to be more interested in what I wanted to accomplish than in what I’d (not) accomplished before. With the assistance of DCU’s excellent research support staff, Alan and I were able to put together a good plan, and then, after a very encouraging round of reviews, to make it even better for our second attempt. This was in 2016; in early 2017 we got the notification that the fellowship had been awarded. After that, I went on working in Oulu until the end of the year to complete my contract, took January 2018 off to wind down and make arrangements, moved to Dublin at the end of January and started the new project on the 1st of February.

If there’s a lesson to take home from all of this, I guess it would be that even if you haven’t been the most shining star of your peer group during your PhD studies, that doesn’t mean you’re out of options if you want to keep pursuing a career in research. Sure, some funders may dismiss you on the basis of your CV alone if they don’t think it shows enough potential, but still, a well prepared proposal can go a long way if you know your strengths and build your research plan around them. MSCA Individual Fellowships are perhaps a more forgiving funding source than many others, since they’re explicitly meant to help you advance your career and thus come with the built-in assumption that there’s something important you don’t yet have but can gain by carrying out the right sort of project with the right sort of host. So, if you’ve found yourself a host with a set of known strengths that complement the ones you’ve demonstrated in your previous work, you already have a pretty solid foundation to build your proposal on.

Getting started

Welcome to You Know Nothing, Socrates! The theme of this blog is knowledge, or more specifically – because that sure could use some narrowing down – the intersection of knowledge (in the philosophical sense) and computing. Knowledge, of course, is a notoriously elusive concept once you start trying to pin it down, which is why I’ve decided to name the blog after the famous Socratic paradox, apocryphal though it may be. And before you ask: yes, the title is also a Game of Thrones reference. Get over it.

To make matters worse, we haven’t been content to just assert that we as human beings have the ability to know various things and to derive new knowledge from evidence. Instead, ever since the invention of the modern digital computer, we’ve been very keen on the idea of replicating, or at least imitating, that ability in machines. This pursuit has given rise to fields of computer science research such as knowledge representation and knowledge discovery; this is the area where I’ve been working throughout my career as a researcher, and also the main subject area that I’ll be writing about.

A bit of context: I’m currently working as a Marie Curie Individual Fellow at the Insight Centre for Data Analytics in Dublin, Ireland. The project I’m working on, titled KDD-CHASER, deals with remote collaboration for the extraction of useful knowledge from personal data, such as one might collect using a wearable wellness device designed to generate meaningful metrics on the wearer’s physical activity and sleep. These products are quite popular and, presumably, useful, but for most users their utility is limited to whatever analyses the product has been programmed to give them. The research I’m doing aims for the creation of an online platform that could be used by users of personal data capturing devices to discover additional knowledge in their data with the help of expert collaborators.

As long as the KDD-CHASER project is running, which is until the end of January 2020, I will be using this blog as a communication channel (among others) to share information about its progress and results with the public. However, I’m also planning to post more general musings on topics that are related to, but not immediately connected with, the work I’m doing in the project. These, I hope, will be enough to keep the blog alive after the project is done and I move on to other things. Not that I’m expecting those other things to be radically different from the things I’m involved in at the moment, but hey, you never know.

There certainly isn’t a shortage of subject matter to draw on: besides the under-the-hood mechanics of computers capable of possessing and producing knowledge, there’s the philosophical dimension of them that I’m also deeply interested in – another reason for my choice of blog title. From here it’s not much of a conceptual leap to the even more bewildering philosophical questions surrounding the notion of artificial intelligence, so I might take the occasional stab at those as well. I fully expect to come to the conclusion that I really know absolutely nothing, but whether I’ll be any the wiser for it remains to be seen.