I’ve spent a good part of the last two months on the road attending meetings, workshops and conferences and giving talks at various locations from Pisa (of the leaning tower) to Newcastle (of the many bridges).  The following is a short synopsis of the main events (slightly edited version of the post “Six weeks on the road” on my personal blog).

Supportive User Interfaces

Monday 13th June I attended a workshop in Pisa on “Supportive User Interfaces“, which includes interfaces that adapt in various ways to users. This workshop was part of EICS 2011, the ACM conference on “engineering interactive computer systems”.  The majority of people there were involved in various forms of model-based user interfaces in which various models of the task, application and interaction are used to generate user interfaces on the fly. W3C have had a previous group in this area (see reports here and here); Dave Raggett from w3c was at the workshop and it sounds like there will be a new working group soon. This clearly has strong links to various forms of ‘meta-level’ representations of data, tasks, etc.. My own contribution started the day, framing the area, focusing partly on reasons for having more ‘meta-level’ interfaces including social empowerment, and partly on the principles/techniques that need to be considered at a human level.

Following the workshop was a meeting of IFIP Working Group 2.7/13.4. IFIP is the UNESCO founded pan-national agency that national computer societies such as as the BCS in the UK and ACM and IEEE Computer in the US belong to. Working Group 2.7/13.4 is focused on the engineering of user interfaces. I had been actively involved in the past, but have had many years’ lapse. However, this seemed a good thing to re-engage with with my new Talis hat on!

SUI: paper:

• Opening the Box: Meta-level Interfaces Needs and Solutions

Web Science Conference in Koblenz

Jaime Teevan from Microsoft gave the opening keynote at WebSci 2011. I know her from her earlier work on personal information management, but her recent work and keynote was about work on analysing and visualising changes in web pages. Web page changes are also analysed alongside users re-visitation patterns; by looking at the frequency of re-visitation Jaime and her colleagues are able to identify the parts of pages that change with similar frequency, helping them, inter alia, to improve search ranking.

Had many great conversations, some with people I know previously (e.g. the Southampton folks), but also new, including the group at Troy that do lots of work with data.gov. I was particularly interested in some work using content matching to look for links between otherwise unlinked (or only partly inter-linked) datasets. Also lots of good presentations including one on trust prediction and a fantastic talk by Mark Bernstein from Eastgate, which he delivered in blank verse!

My own contribution included the poster that Dave@Talis prepared, which was on the web-scale spreading activation work in collaboration with Univ. Athens. Quite a niche area in a multi-disciplinary conference, so it didn’t elicit quite the interest of the social networking posters, but did lead to a small number of in depth discussions.

In addition I gave talk on the more cognitive/philosophical issues when we start to use the web as an external extension to / replacement of memory, including its impact on education. Got some good feedback from this.

Closing keynote was from Barry Wellman, the guy who started social network analysis way before they were on computers. At one point he challenged the Dunbar number (the idea that there are fundamental cognitive limits on social groups with different sized circles family~6, extended family~20, village~60, large village~200). I wondered whether this was due to cognitive extension with address books etc., but he didn’t seem to think so; there is evidence that some large circles pre-date web (although maybe not physical address books). Made me wonder about itinerant tradesmen, tinkers, etc., even with no prostheses. Maybe the numbers sort of apply to any single content, but are repeated for each new context?

WebSci papers:

• Spreading Activation for Web Scale Reasoning: Promise and Problems
• A Shifting Boundary: the dynamics of internal cognition and the web as external representation

The HCI Conference – Newcastle

I attended the British HCI conference in Newcastle. This was the 25th conference, and as my very first academic paper in computing was at the first BHCI in 1984, I was pleased to be there at this anniversary. The paper I was presenting was a retrospective on vfridge, a social networking site dating back to 1999/2000, it seemed an historic occasion!   The paper related to a dot.com era company, which also involved other current Talis folk: Nadeem Shabir and Justin Leavesley.

Abi Sellen gave the opening keynote on “The Future of Looking Back”, discussing various technologies for digital memory (including of course the Microsoft SenseCam), and their current and potential impact on people and society.  This is an area of growing concern and Memories for Life is one of the UK Computing Grand Challenges.

Gregory Abowd gave the closing keynote. It was great to see Gregory again, we meet too rarely. The main focus of his keynote was on three aspects of research: novelty, value and reliability and how his own work had moved within this space over the years. In particular having two autistic sons has led him in directions he would never have considered, and this immediately valuable work has also created highly novel research. Novelty and value can coexist.

One thing that was interesting with a Talis hat on was a number of people who expressed a degree  of hostility (in an intellectual rather than personal sense) to semantic web technology.  This was because they quite reasonably critiqued the somewhat simplistic notion of ‘meaning’ embodied in the idea of a URI as identifier for real world concepts, or even objects.  Natural language words and terms tend to be used in multifarious and rich ways, which simply cannot be encompassed with a simple one-to-one map to URIs.

To be fair the same critique could be levelled against keys in relational databases or indeed any formal data structure.  However, these tend to have very specific and local uses, whereas SemWeb and Linked Data aims to have more global semantics.  Personally I see this as an exciting challenge for the SemWeb community, how to allow richer human-like kinds of meaning whilst retaining the potential for computer interpretation — SemWeb N.0?

vfridge paper:

• Anatomy of an Early Social Networking Site

Nottingham MRL

I was at Mixed Reality Lab in Nottingham for Joel Fischer‘s PhD viva and while there did a seminar the afternoon on “extended episodic experience” based on Haliyana Khalid‘s PhD work and ideas that arose from it. Basically, whereas ‘user experience’ has become a big issue most of the work is focused on individual ‘experiences’ whereas much of life consists of ongoing series of experiences (episodes) which together make up the whole experience of interacting with a person or place, following a band, etc.

I had obviously not done a good enough job at wearing Joel down with difficult questions in the PhD viva in the morning as he was there in the afternoon to ask difficult questions back of his own

Docfest – Digital Economy Summer School

The last major event was Docfest, which brought together the PhD students from the digital economy centres from around the country. Not sure of the exact count but just short of 150 participants I think. They come from a wide variety of backgrounds, business, design, computing, engineering, and many are mature students with years of professional experience behind them.

This looked like being a super event, unfortunately I was only able to attend for a day However, I had a great evening at the welcome event talking with many of the students and even got to ride in Steve Forshaw‘s Sinclair C5!

My contribution to the event was running the first morning session on ‘creativity’. Surprise, surprise this started with a bad ideas session, but new for me too as the largest group I’ve run in the past has been around 30. There were a number of local Highwire students acting as facilitators for the groups, so I had only to set them off and observe results . At the end of the morning I gave some the theoretical background to bad ideas as a method and in understanding (aspects of) creativity more widely.

Other speakers at the event included Jane Prophet, Chris Csikszentmihalyi and Chris Bonnington, so was sad to miss them; although I did get a fascinating chat with Jane over breakfast in the hotel hearing about her new projects on arts and neural imaging, and on how repetitious writing induces temporary psychosis … That is why the teachers give lines, to send the pupils bonkers!

As part of our support for SSSW2011, the 8th Semantic Web Summer School, taking place this week in Spain, Talis is sponsoring a student to attend the school, who through lack of funds would not otherwise be able to attend. After a significant number of applications for the funded place and a challenging selection process, the grant was awarded to Kelli de Faria Cordeiro. Kelli is a PhD student in the Knowledge Engineering Group (GRECO) of the Computer Science Department / Institute of Mathematics at the Federal University of Rio de Janeiro.

Just before we arrived in Spain for the Summer School, I caught up with Kelli to find out about her research and her hopes for the Summer School.

Tom: In simple terms, what is the focus of your research?

Kelli: My research is centred on Advanced Conceptual Modelling of Complex Information Systems, focusing on Linked Open Data as a Complex Information System. The main issue is how to semantically enrich Linked Data keeping the flexibility of Linked Data Principles.

Tom: How did you come to be doing research in this area? What led you here?

Kelli: I have been studying semantic web for the past 5 years, and I have been an enthusiast on data integration and analysis during my whole professional life. The Linked Data Principles as an approach to integrate heterogeneous and dynamic data called my attention. The possibilities to create a data analysis environment with the data available on the web took my thoughts since I started to study it.

Tom: Could you explain a bit about the context in which your research is taking place?

Kelli: Currently, I am working on the LinkedDataBR Project – Expose, Share and Connection of Open Data Resources on the Web, supported by the National Education and Research Network (RNP) of Brazil. Central functionalities to be included are data cleaning, transformation, association, annotation and referencing to terminology mechanisms. At this project, we have been facing critical issues about the role of ontologies on Linked Open Data Publish Process, and I hope to address some of these issues with my research work. Moreover, there has been a Brazilian governmental movement to open data, leading to the development of applied projects to support it with tools and guidelines with a broad scope. The consequence is the need for human resources qualification, and I also expect to meet this demand with my studies on the subject.

Tom: What are you most looking forward to about the Semantic Web Summer School?

Kelli: I want to improve my learning on Ontology Engineering, Knowledge Representation and Linked Open Data by developing my technical and social skills, having great time with tutors and other students. Besides the contributions to my PhD Thesis, one of the main objectives of attending the school is to learn as much as I can and then share the knowledge with my colleagues in Brazil with whom I am working on Linked Data Research Projects.

Tom: In what ways do you expect the Summer School to benefit your research?

Kelli: The development of my research can benefit as I learn the current key topics in the field. It can also be improved with discussion and validation of the ideas and approaches to solve my research problems with tutors, invited speakers and other students. I look forward discussing the description of aggregate data as well as analytical processing over Linked Data.

Way back in the mists of time, aka July 2005, when I was a young, starry-eyed PhD student, I found myself in the hills outside Madrid with 50 other people fitting the same description. The occasion was the International Summer School on Ontology Engineering and the Semantic Web, and in the week that followed we received a crash course in Semantic Web technologies, delivered by some of the top researchers in the field.

This year I return to the school as a tutor, and I’m taking three of my Talis colleagues with me — one each from the Consulting, Kasabi and Platform teams. I’m intrigued to see what they each make of it, bringing as they do a really diverse range of skills and experience, from training people in the fundamentals of Linked Data, to Web design and user experience, to frontline operation of our Talis Platform.

We’re also very proud to be sponsors of the Summer School. As part of this arrangement we’re sponsoring a student from Brazil to attend the school, and in the next few days I’ll be introducing her and her work through a post on this blog. Stay tuned.

I have recently returned from a trip, which included a PhD viva in Southampton and a visit to University of Athens and then ended up with three days in the heart of the south German countryside in the company of Daniel Kiem’s Data Analysis and Visualization research group at Konstanz, one of the key international groups in visualisation and visual analytics.  This was the group’s annual retreat at an intimate conference hotel run by a relative of one of the group.

Daniel had for a long time been the ‘Mr Pixels’ of visualisation with his ground breaking work on pixel plotting techniques, and following on from his early work, his group grew to be the foremost research group on visualisation in Europe.  However, in recent years Daniel has become the chief European proponent of the emerging field of visual analytics, including being scientific lead of the VisMaster EU Coordinated Action, which lead to the recent roadmap “Mastering the Information Age: Solving Problems with Visual Analytics“.

Visual Analytics is defined as “the science of analytical reasoning facilitated by interactive human-machine interfaces” (Wong and Thomas. Visual Analytics), and is all about harnessing the combined power of the best visualisation and latest machine learning techniques to tackle some of the hardest data-oriented problems from gene sequence matching to disaster management.  During the retreat Daniel recounted a recent meeting with a major politician.  He demonstrated a system visualising historic news data including sentiment analysis.  He entered the politician’s name to filter the stream, and she instantly recognised periods of high-and low popularity that she already knew about following high profile stories, but then she zeroed in on a place on the timeline with negative sentiment.  As they drilled into the data she saw that this was focused on a single country Kazakhstan and she found a particular major story there of which she had previously been unaware.

During the two and half days of the retreat there were around 20 different talks and presentations and each had something of interest.

One broad area that arose in different settings was how to deal with complex time series data.  Traditionally time-series data has been based on regular discrete numerical measurements such as hourly stock prices, or tidal flows.  However, data now often does not fit this model, involving infrequent, but often bursty event-based non-numeric data such sentiment in twitter feeds, and often in vast quantities, such as network analysis data.  Visualisations often include multiple views, and ways to drill down from aggregated views based on structural features such as geography, into particular facets and eventually individual events.

Another area that interested me was the analysis and visualisation of textual data, including streaming real-time text such as twitter and news stories.  While numerical information can often be reduced to simple lines or points in visualisations, to be meaningful text needs to be readable, creating special challenges for the visualisation of very large data sets.  In addition, the textual data often has additional attributes such as the temporal and geographic context of a news story.

As well as plain visualisations, the visual analytics nature of the group was evident in many presentations where different forms of clustering, natural language processing and machine learning were being used as part of the analytic process.  These were applied to a variety of application areas, including the sentiment analysis already mentioned, and also network security, bio-informatics and a large joint German–US project in the final stages of negotiation that will address the resilience of logistic, power and communications networks in the face of natural, technological and human failures.

Peter Bak was at the retreat.  He is an ex-member of the group and now at IBM research in Haifa.  He outlined some of the visualisation challenges he is finding at IBM from shipping logistics to Watson, the Jeopardy-playing computer, which recently won on live television against two past Jeopardy champions.  I have read about the latter before, during its earlier development, but it was fascinating to hear again about the combination of massively parallel and data intensive hypothesis generation followed by more orchestrated ranking and selection.  Whilst still very simple in comparison, it did capture some of the richness of our own ways of tackling problems, and also shows a tantalising glimpse of what can be possible through harnessing the web as data.

While the running of the Jeopardy computer happens in milliseconds the digital forensics to understand what went wrong on certain questions is expected to last nearly a year.  The former is the role purely of automated processing, but the later for human analytics.  I have found similar problems on much smaller datasets (Gb rather than Tb) when using spreading activation algorithms — when emergent results are not as expected it can be a real challenge to drill into massively distributed processes and make sense of the chains of tiny events that gave rise to the visible effect.  However, this seems a core issue for the future of data intensive applications.

Maybe the issue will hinge around layers of control.  In our own minds, many thoughts bubble almost arbitrarily into consciousness, and I am sure many more that we are never aware of, but our conscious processes filter and manage these into a coherent whole: for our own sense of what we are thinking about, for our action in the world, and for communicating to others.  It maybe the same with vast emergent parallel data processing applications, such as Watson; at some levels we may have to accept that things just work or don’t work and not be able to fully ‘debug’ them, but at a higher level, like our own conscious thoughts, we should expect more control, more robustness and more ability to explain and justify actions and decisions.

My own role as the retreat guest was to try to give fresh ideas and hopefully disrupt and inspire the group. This included two talks, running a Bad Ideas creativity session with Geoff, taking part in a session focused on evaluation and a panel on ‘self-marketing’ in research.

For one of my talks I focused on the potential challenges that semantic web data poses for visualisation (see slides).   While there is some work in the area (e.g.  Jean-Daniel Fekete‘s Aviz group at Inria), it is still under explored. The talk was structured around three phases starting with raw non-semantic data (CSV, RDMS, etc.) through to transformation into RDF and finally linked open data.  Some similar issues arise at the two ends of the spectrum, including issues of heterogeneity, some are particular to the semantic nature of data (e.g. the combination of structure and small units of free text in literals), and some to RDF (e.g. schema-less-ness).

The second talk gave some of the theoretical background to Bad Ideas and related creativity techniques (see slides).  As well as the divergent nature of the bad ideas themselves, I focused on the more convergent analytic aspects, in particular the importance of externalisation for external cognition and reflection.

Imagine you’ve just arrived in an unfamiliar place, perhaps on a business trip (or recently beamed down from the Starship Enterprise). One of the first things you’ll probably want to do is find out what things are nearby. Google Maps provides a great “search nearby” function (try entering just a * to get everything), but this is geared more towards businesses, and the data isn’t exactly open, making it hard to reuse in other applications. We wanted to try something similar, using the growing range of liberally-licensed Linked Data sets with a geographic component. Here’s what we did…

We started with the Geonames data set (helpfully converted to RDF by Ian Davis and loaded into a Talis Platform store), all the geo-coded entries from DBpedia, and data about schools from data.gov.uk — we’ll call all the things described in these data sets ‘points of interest’. In truth the geocoded entries in DBpedia are frequently not points but regions/areas, but that’s an issue for another day.

Next, we implemented some cheap and cheerful secret sauce to identify, for each point of interest, all the other PoIs that are ‘nearby’. For each of these pairwise relationships we create two new RDF triples, one stating ‘x near y’, the other ‘y near x’. For example, our algorithm may reveal that Birkby Junior School is near to Kirklees Incinerator, in which case we simply create the RDF triples:

<http://education.data.gov.uk/id/school/107626> <http://open.vocab.org/terms/near> <http://dbpedia.org/resource/Kirklees_Incinerator> .
<http://dbpedia.org/resource/Kirklees_Incinerator> <http://open.vocab.org/terms/near> <http://education.data.gov.uk/id/school/107626> .

We do the same for all pair-wise relations in each cluster of ‘nearby’ items.

The process runs as a series of MapReduce jobs on top of Hadoop, creating a new data set of more than 700 million triples, all of which are links within and between data sets, i.e. those figures don’t include any literals or rdf:type statements describing the points in the original data sets. What we do create along the way are URIs for all the intersection points in the lat/long grid (down to a specific level of granularity). These are linked to the URIs of nearby things from the input data sets, and too each other. We haven’t done the sums yet to identify the proportion of inter- versus intra-data set links, though judicious use of grep and wc -l should address that.

Our definition of ‘near’ was deliberately left loosely defined. It’s way too subjective and context dependent to try and define more precisely, that’s why we kept it vague and left plenty of scope for refinement according to the needs of specific applications. We achieved this in the following ways:

Firstly, imagine an application uses this data set to find all the other things near to a particular point of interest, but also wants to know the distances to each and which is the closest. The data to answer these questions doesn’t need to be materialised in the data set, because the consuming application can dereference the URIs of the nearby points of interest and perform the post-processing of choice on the original geo-coordinates, such as computing the distances between points and finding which is nearest.

Extending this principle, it’s also trivial to use this data as input to a matching process that identifies when the same entity is described in different data sets (with each assigning a different URI). For example, our approach finds that the resource identified by the URI <http://dbpedia.org/resource/Gad%27s_Hill_School> is near to a number of other things, including the resource identified by the URI <http://education.data.gov.uk/id/school/118944>. In fact, both these URIs identify the same resource, "Gad's Hill School", and could be connected using the owl:sameAs property. The cost of assessing whether these two URIs identify the same resource, perhaps by computing the string similarity of the labels assigned to them in each data set, is much lower when using the set of ‘near’ links as input compared to using both data sets in their entirety, as the number of pairwise comparisons that must be made is significantly reduced.

The second mechanism we introduced to ensure the data set could be reused and refined where required was to make the data set as navigable as possible. A typical Web API requires each query to be formulated afresh before it is sent, meaning the data set isn’t easily browsed in an ad-hoc fashion. Instead, logic is required at the client/application-side in order to formulate the next query, before the client or user can move from one record to another. In contrast, this new data set applies — to the physical world — the same ‘follow your nose’ concept that’s so central to Linked Data. The concept is blindingly obvious when you stop and think about it. We navigate the physical world by following our noses, looking for landmarks, finding our bearings, and adjusting our courses — why should it be any different when we navigate the Web from a geographical perspective?

To support this mechanism, the data set includes links that connect each intersection point in the lat/long grid to those that surround it. These links are expressed in terms of compass bearings, allowing an application to move from grid point to grid point in whatever direction they choose, potentially traversing the globe in the process. In reality there are some gaps in coverage, primarily corresponding to oceans and areas of low population density that are typically under-represented in the input data sets; as we currently only materialise data for intersection points that have nearby PoIs, these areas are generally not covered by the grid.

We have a number of plans for enhancements to the data set (not least of which licensing statements and a proper voiD description), but in the meantime we’ve made an initial release of the data set, which is hosted in a Talis Platform store and exposed through the API at http://rdfize.com/geo/point/. The goal of this API is to provide a simple access mechanism for application developers wishing to find all points of interest near to a particular location. To achieve this, developers simply need to construct URIs of the form http://rdfize.com/geo/point/latitude/longitude/ e.g. http://rdfize.com/geo/point/9.022762/38.746719/ (a point close to the centre of Addis Ababa).

Clients performing an HTTP GET on one of these URIs will be redirected to a URI that identifies the nearest grid intersection point (in this case http://rdfize.com/geo/point/9.02/38.75/). From there the client will be redirected (following the widely used HTTP 303 + Content Negotiation pattern) to an HTML or RDF description of that point. Currently we support the RDF/XML, Turtle, N-Triples and RDF/JSON serialisations of RDF, as well as a simple HTML view for human users.

Those looking for a more polished interaction with the data set should sign up for the beta of Kasabi, where we plan to release enhanced versions of the data set in the coming months.

It was announced today that Talis Group Ltd has completed the sale of its Library division (“Talis Information Ltd”) to Capita Group plc. This development raises some interesting opportunities for Talis Research, which remains within the Talis Group subsidiary “Talis Systems Ltd”.

The other teams that remain with Talis include the Platform team, our Linked Data consultancy, the education business responsible for Talis Aspire, and the new venture “Kasabi”. Each of these businesses are predicated on the Linked Data and Semantic Web vision, where the Talis Research team have particular expertise.

Within the Research team we’re particularly excited about the opportunity to work even more closely with these talented teams, to help drive the pace of innovation within Talis Group, and further strengthen our contribution to the Linked Data and Semantic Web field.

In this research seminar recorded at Talis in July 2010, Spyros Kotoulas gives an introduction to the topic of Inferencing over Web Data. The talk is a great introduction for those new to the reasoning in a Semantic Web context.

(See this video on Vimeo for alternative media players.)

Spyros is a post-doctoral researcher In the Knowledge representation and Reasoning group of the Vrije Universiteit Amsterdam, led by Frank van Harmelen. His research interests include large-scale semantic web reasoning, privacy in distributed and peer-to-peer systems, highly distributed applications and large-scale resource discovery. He currently works on the LARKC project.

Links:

• Spyros Kotoulas’s Home Page

In this research seminar recorded at Talis in May 2010, Matthew Rowe describes his Ph.D. research on Disambiguating Identity Web References using Social Data.

(See this video on Vimeo for alternative media players.)

Matt gives the following background to his research:

“The availability of personal information on the Web has lead to a rise in malevolent practices such as identity theft and lateral surveillance, this in turn has forced web users to find and disambiguate web resources containing their personal information. I will present three distinct automated disambiguation techniques designed to overcome the burden placed on web users, each of these techniques fuse Semantic Web technologies with existing state of the art techniques (inference rules, graph-based random walks and machine learning). Social data leveraged from Social Web platforms (Facebook, MySpace and Twitter) is used to support the disambiguation process and a thorough evaluation of the presented techniques demonstrates their ability to outperform humans at the same task.”

At the time of the talk, Matt was a Ph.D. student within the University of Sheffield’s Organisations, Information and Knowledge (OAK) Group. His thesis, successfully defended just days after this seminar was given, explored automated disambiguation techniques supported by data leveraged from Social Web platforms.

His research has drawn on several different areas of Computer Science including the Semantic Web, the Social Web and machine learning. Recent research efforts have centred around the Linked Data field, in particular exploring how URI disambiguation can be tackled from a statistical, machine learning perspective. Matt is now a Research Associate at The Open University’s Knowledge Media Institute.

Links:

• Matt Rowe’s Home Page
• Ph.D. Thesis – Disambiguating Identity Web References using Social Data

When I joined Talis in 2008, a number of my peers in the Semantic Web community commented on the fact that a relatively small company was employing recent Ph.D. graduates. Any development of this sort is a useful data point for gauging the commercial interest in a particular research field (not to mention a whisper of reassurance for those toiling on the rocky road through a Ph.D.!). But what seemed to raise the most eyebrows was that I was joining Talis as a Researcher. Everyone seemed to agree this was a pretty bold move for a company of our size.

So what’s in it for us? Why do we invest in research rather than increasing the dividend we pay to shareholders each year? We do this because we believe that we can be more successful as a company by investing in research, as a driver for medium- and long-term growth of existing strands of the business, and as a potential source of new areas of business we haven’t yet imagined.

Many people who have worked closely with us will know that we value innovation across all areas of the business, while recognising that this means different things depending on the maturity of the corresponding market. So if we value innovation across the business as a whole, why invest in creating a dedicated research team? Shouldn’t all teams be responsible for conducting their own research, as their work priorities demand?

There will always be a strong element of this at Talis, as many of our recent developments demonstrate (take Aspire and Kasabi for example). However, any researcher will tell you (as will anyone else who has to combine research with other responsibilities), conducting considered, rigorous, fundamental research requires certain freedoms that other teams at Talis don’t always have — their work is too important; freedom to explore new ideas without fear of failure, and freedom to dig deep into those ideas without distraction. Talis provides a superb environment for the former, and I’m working on my own abilities to deliver on the latter!

This highlights a fundamental contrast between Talis Research and my experience of research in an academic context (admittedly this is limited to my time as a Ph.D. student in the early 21st century, and is certainly no reflection on KMi and The Open University, one of the most supportive and creative research environments I’ve ever set foot in). At Talis we will need to spend significant periods of time with our ‘research attention‘ far closer to the coal-face than a typical university research group is used to. This can be unsettling if we measure ourselves in conventional academic terms.

The flip side is this: with no requirement on publications as a metric of success, unless we want them to be, we can free ourselves up to ask the questions to which we as a company want answers, safe in the knowledge that it’s the quality of the answers that matter, irrespective of the outcome.

Do we know precisely how to answer these questions, in this specific context? Do we know precisely how Research fits within the growing, evolving organism that is Talis? No. Not fully. But as we’re growing we’re working it out. This blog is the start of our account of the journey. We hope to see you along the way.

Tom Heath.