Archive for the ‘Collaboration’ Category

Collaboration Tools and smart use of Google (ask Pippa Middleton)

Tuesday, September 27th, 2016

Collaboration Tools and smart use of Google by Kaas & Mulvad.

As Kaas & Mulvad illustrate, collaboration with Google tools can be quite effective.

However, my attention was caught by the last sentence of their first paragraph:

Google Drive makes sharing your files simple. It also allows multiple people to edit the same file, allowing for real-time collaboration. But be aware – don’t share anything in Google, you want to keep secret. (emphasis added)

Pippa Middleton would tell you the same advice applies to the iCloud.

Guidelines for Effective Collaboration – (anything over 1 is poor use of others)

Sunday, March 13th, 2016

Guidelines for Effective Collaboration by

From the webpage:

We are a remote team, therefore effective communication is one of the most important foundations on which we build our technology and our company. Below you will find a thorough guide to enable your work and empower your teammates to get their stuff done, while keeping interruptions to a minimum. These guidelines apply to Ride employees and consultants who work under the Engineering Team.

Before you scan these and nod in agreement, take out a pencil and make a tick for each of the first seven suggestions you have followed before asking others for help in the last week.


Here’s the equation for each request for help:

7/your-tick-count = (anything over 1 is poor use of others)

Now destroy the written evidence and try to do better this week.


Friday, May 22nd, 2015

Authorea is the collaborative typewriter for academia.

From the website:

Write on the web.
Writing a scientific article should be as easy as writing a blog post. Every document you create becomes a beautiful webpage, which you can share.
More and more often, we write together. A recent paper coauthored on Authorea by a CERN collaboration counts over 200 authors. If we can solve collaboration for CERN, we can solve it for you too!
Version control.
Authorea uses Git, a robust versioning control system to keep track of document changes. Every edit you and your colleagues make is recorded and can be undone at any time.
Use many formats.
Authorea lets you write in LaTeX, Markdown, HTML, Javascript, and more. Different coauthors, different formats, same document.
Data-rich science.
Did you ever wish you could share with your readers the data behind a figure? Authorea documents can take data alongside text and images, such as IPython notebooks and d3.js plots to make your articles shine with beautiful data-driven interactive visualizations.

Sounds good? Sign up or log in to get started immediately, for free.

Authorea uses a gentle form of open source persuasion. You can have one (1) private article for free but unlimited public articles. As your monthly rate goes up, you can have an increased number of private articles. Works for me because most if not all of my writing/editing is destined to be public anyway.

Standards are most useful when they are writ LARGE so private or “secret” standards have never made sense to me.

A Latent Source Model for Online Collaborative Filtering

Wednesday, December 10th, 2014

A Latent Source Model for Online Collaborative Filtering by Guy Bresler, George H. Chen, and Devavrat Shah.


Despite the prevalence of collaborative filtering in recommendation systems, there has been little theoretical development on why and how well it works, especially in the “online” setting, where items are recommended to users over time. We address this theoretical gap by introducing a model for online recommendation systems, cast item recommendation under the model as a learning problem, and analyze the performance of a cosine-similarity collaborative filtering method. In our model, each of n users either likes or dislikes each of m items. We assume there to be k types of users, and all the users of a given type share a common string of probabilities determining the chance of liking each item. At each time step, we recommend an item to each user, where a key distinction from related bandit literature is that once a user consumes an item (e.g., watches a movie), then that item cannot be recommended to the same user again. The goal is to maximize the number of likable items recommended to users over time. Our main result establishes that after nearly log(km) initial learning time steps, a simple collaborative filtering algorithm achieves essentially optimal performance without knowing k. The algorithm has an exploitation step that uses cosine similarity and two types of exploration steps, one to explore the space of items (standard in the literature) and the other to explore similarity between users (novel to this work).

The similarity between users makes me wonder if merging results from a topic map could or should be returned on the basis of a similarity of users? On the assumption that at some point of similarity that distinct users share views about subject identity.

You’re not allowed bioinformatics anymore

Monday, July 21st, 2014

You’re not allowed bioinformatics anymore by Mick Watson.

Bump this to the head of your polemic reading list! Excellent writing.

To be fair, collaboration with others is a two-way street.

That is both communities in this tale needed to be reaching out to the other on a continuous basis. It isn’t enough that you offered once or twice and were rebuffed so now you will wait them out.

Successful collaborations don’t start with grudges and bad attitudes about prior failures to collaborate.

I know of two organizations that share common members, operate in the same area and despite both being more than a century old, have had only one, brief, collaborative project.

The collaboration fell apart because leadership in both was waiting for the other to call.

It is hard to sustain a collaboration when both parties considered themselves to be the center of the universe. (I have it on good authority neither one of them are the center of the universe.)

I can’t promise fame, professional success, etc., but reaching out and genuinely collaborating with others will advance your field of endeavor. Promise.

Enjoy the story.

I first saw this in a tweet by Neil Saunders.

Sense Preview

Sunday, January 12th, 2014

Sense is in private beta but you can request an invitation.

Even though the presentation is well rehearsed, this is pretty damned impressive!

The bar for cloud based computing continues to rise.

Follow @senseplatform.

I first saw this at Danny Bickson’s Sense: collaborative data science in the cloud

PS: Learn more about Sense at the 3rd GraphLab Conference.

Log as a Service (Part 1 of 2)

Sunday, June 30th, 2013

Log as a Service (Part 1 of 2) by Oliver Kennedy.

From the post:

Last week I introduced some of the hype behind our new project: Laasie. This week, let me delve into some of the technical details. Although for simplicity, I’ll be using the present tense, please keep in mind that what I’m about to describe is work in progress. We’re hard at work implementing these, and will release when-it’s-ready (tm blizzard entertainment).

So, let’s get to it. There are two state abstractions in Laasie: state land, and log land. I’ll address each of these independently.

See: Laasie: Building the next generation of collaborative applications.

I am partially interested in Laasie because of work that is ongoing to enable ODF markup to support collaborative editing (a special case of change tracking).

I am also interested because authoring topic maps should be a social enterprise, which implies collaborative editing.

Finally, in hopes that collaborative editing will fade the metaphor of a physical document. A “document” will be what we have requested to be displayed at a point in time, populated by particular components and content.

I remain deeply interested in physical texts and their traditions, including transmission.

However, they should not be confused with their simulacra that we make manifest with our computers.

Laasie: Building the next generation of collaborative applications

Monday, June 24th, 2013

Laasie: Building the next generation of collaborative applications by Oliver Kennedy.

From the post:

With the first Laasie paper (ever) being presented tomorrow at WebDB (part of SIGMOD), I thought it might be a good idea to explain the hubbub. What is Laasie?

The short version is that it’s an incremental state replication and persistence infrastructure, targeted mostly at web applications. In particular, we’re focusing on a class of collaborative applications, where multiple users interact with the same application state simultaneously. A commonly known instance of such applications is the Google Docs office suite. Multiple users viewing the same document can simultaneously both view and edit the document.

Do your topic maps collaborate with other topic maps?

FuturICT:… [No Semantics?]

Monday, May 6th, 2013

FuturICT: Participatory Computing for Our Complex World

From the FuturICT FET Flagship Project Summary:

FuturICT is a visionary project that will deliver new science and technology to explore, understand and manage our connected world. This will inspire new information and communication technologies (ICT) that are socially adaptive and socially interactive, supporting collective awareness.

Revealing the hidden laws and processes underlying our complex, global, socially interactive systems constitutes one of the most pressing scientific challenges of the 21st Century. Integrating complexity science with ICT and the social sciences, will allow us to design novel robust, trustworthy and adaptive technologies based on socially inspired paradigms. Data from a variety of sources will help us to develop models of techno-socioeconomic systems. In turn, insights from these models will inspire a new generation of socially adaptive, self-organised ICT systems. This will create a paradigm shift and facilitate a symbiotic co-evolution of ICT and society. In response to the European Commission’s call for a ‘Big Science’ project, FuturICT will build a largescale, pan European, integrated programme of research which will extend for 10 years and beyond.

Did you know that the term “semantic” appears only twice in the FuturICT Project Outline? And both times as in the “semantic web?”

Not a word of how models, data sources, paradigms, etc., with different semantics are going to be wedded into a coherent whole.

View it as an opportunity to deliver FuturlCT results using topic maps beyond this project.

Fast Collaborative Graph Exploration

Thursday, April 18th, 2013

Fast Collaborative Graph Exploration by Dariusz Dereniowski, Yann Disser, Adrian Kosowski, Dominik Pajak, Przemyslaw Uznanski.


We study the following scenario of online graph exploration. A team of $k$ agents is initially located at a distinguished vertex $r$ of an undirected graph. At every time step, each agent can traverse an edge of the graph. All vertices have unique identifiers, and upon entering a vertex, an agent obtains the list of identifiers of all its neighbors. We ask how many time steps are required to complete exploration, i.e., to make sure that every vertex has been visited by some agent. We consider two communication models: one in which all agents have global knowledge of the state of the exploration, and one in which agents may only exchange information when simultaneously located at the same vertex. As our main result, we provide the first strategy which performs exploration of a graph with $n$ vertices at a distance of at most $D$ from $r$ in time $O(D)$, using a team of agents of polynomial size $k = D n^{1+ \epsilon} < n^{2+\epsilon}$, for any $\epsilon > 0$. Our strategy works in the local communication model, without knowledge of global parameters such as $n$ or $D$. We also obtain almost-tight bounds on the asymptotic relation between exploration time and team size, for large $k$. For any constant $c>1$, we show that in the global communication model, a team of $k = D n^c$ agents can always complete exploration in $D(1+ \frac{1}{c-1} +o(1))$ time steps, whereas at least $D(1+ \frac{1}{c} -o(1))$ steps are sometimes required. In the local communication model, $D(1+ \frac{2}{c-1} +o(1))$ steps always suffice to complete exploration, and at least $D(1+ \frac{2}{c} -o(1))$ steps are sometimes required. This shows a clear separation between the global and local communication models.

Heavy going but seems important for graph exploration performance.

See also the special case of exploring trees under related work.

Another possibility for exploring overlapping markup. Each agent has an independent view of one part of the markup trees.

The Power of Collaboration [Cultural Gulfs]

Monday, April 15th, 2013

The Power of Collaboration by Andrea Ruskin.

From the post:

A quote that I stumbled on during grad school stuck with me. From the story of the elder’s box as told by Eber Hampton, it sums up my philosophy of working and teaching:

How many sides do you see?
One,” I said.
He pulled the box towards his chest and turned it so one corner faced me.
Now how many do you see?
Now I see three sides.
He stepped back and extended the box, one corner towards him and one towards me.
You and I together can see six sides of this box,” he told me.

—Eber Hampton (2002) The Circle Unfolds, p. 41–42

Andrea describes a graduate school project to develop a learning resource for Aboriginal students.

A task made more difficult by Andrea being a non-Aboriginal designer.

The gap between you and a topic map customer may not be as obvious but will be no less real.

Collaborating, Online with LaTeX?

Sunday, December 16th, 2012

I saw a tweet tonight that mentioned two online collaborative editors based on LaTeX:




I don’t have the time to look closely at them tonight but thought you would find them interesting.

If collaborative editing is possible for LaTeX, shouldn’t that also be possible for a topic map?

I saw this mentioned in a tweet by Jan-Piet Mens

Collaborative Systems: Easy To Miss The Mark

Sunday, October 21st, 2012

Collaborative Systems: Easy To Miss The Mark by Jocob Morgan.

From the post:

Map out use cases defining who you want collaborating and what results you want them to achieve. Skip this step in the beginning, and you’ll regret it in the end.

One of the things that organizations really need to consider when evaluating collaborative solutions is their use cases. Not only that, but also understanding the outcomes of those use cases and how they can map to a desired feature requirement. Use cases really help put things into perspective for companies who are seeking to understand the “why” before they figure out the “how.”

That’s what a use case is: the distilled essence of a role within your organization, how it will interact with some system, and the expected or desired result. Developing use cases makes your plans, requirements, and specifications less abstract because it forces you to come up with specific examples.

This is why we created a framework (inspired by Gil Yehuda) to address this. It breaks down as follows:

  • — Identify the overall business problem you are looking to solve (typically there are several).
  • — Narrow down the problem into specific use cases; each problem has several use cases.
  • — Describe the situation that needs to be present for that use case to be applicable.
  • — Clarify the desired action.
  • — State the desired result.

For topic maps I would write:

Map out use cases defining what data you want to identify and/or integrate and what results you expect from that identification or integration. Skip this step in the beginning, and you’ll regret it in the end.

If you don’t have an expectation of a measurable result (in businesses a profitable one), your efforts at semantic integration are premature.

How will you know when you have reached the end of a particular effort?

Learning Mahout : Collaborative Filtering [Recommend Your Preferences?]

Friday, August 24th, 2012

Learning Mahout : Collaborative Filtering by Sujit Pal.

From the post:

My Mahout in Action (MIA) book has been collecting dust for a while now, waiting for me to get around to learning about Mahout. Mahout is evolving quite rapidly, so the book is a bit dated now, but I decided to use it as a guide anyway as I work through the various modules in the currently GA) 0.7 distribution.

My objective is to learn about Mahout initially from a client perspective, ie, find out what ML modules (eg, clustering, logistic regression, etc) are available, and which algorithms are supported within each module, and how to use them from my own code. Although Mahout provides non-Hadoop implementations for almost all its features, I am primarily interested in the Hadoop implementations. Initially I just want to figure out how to use it (with custom code to tweak behavior). Later, I would like to understand how the algorithm is represented as a (possibly multi-stage) M/R job so I can build similar implementations.

I am going to write about my progress, mainly in order to populate my cheat sheet in the sky (ie, for future reference). Any code I write will be available in this GitHub (Scala) project.

The first module covered in the book is Collaborative Filtering. Essentially, it is a technique of predicting preferences given the preferences of others in the group. There are two main approaches – user based and item based. In case of user-based filtering, the objective is to look for users similar to the given user, then use the ratings from these similar users to predict a preference for the given user. In case of item-based recommendation, similarities between pairs of items are computed, then preferences predicted for the given user using a combination of the user’s current item preferences and the similarity matrix.

While you are working your way through this post, keep in mind: Collaborative filtering with GraphChi.

Question: What if you are an outlier?

Telephone marketing interviews with me get shortened by responses like: “X? Is that a TV show?”

How would you go about piercing the marketing veil to recommend your preferences?

Now that is a product to which even I might subscribe. (But don’t advertise on TV, I won’t see it.)

Web sequence diagrams

Thursday, May 24th, 2012

Web sequence diagrams

I ran across this while looking for information on Lucene indexing.

It may be that I am confusing the skill of the author with the utility of the interface (which may be commonly available via other sources) but I was impressed enough that I wanted to point it out.

It does seem a bit pricey ($99 for two users) but on the other hand, developing good documentation is (should be) a team based task. This would be a good way to insure a common understanding of sequences of operations.

Are there similar tools you would recommend for team based activities?

Thinking that authoring a topic map is very much a team activity. From domain experts who vet content to UI experts who create and test interfaces to experts who load and maintain content servers and others.

Keeping a common sense of purpose and interdependence (team effort) goes a long way to a successful project conclusion.

GATE Teamware: Collaborative Annotation Factories (HOT!)

Wednesday, May 9th, 2012

GATE Teamware: Collaborative Annotation Factories

From the webpage:

Teamware is a web-based management platform for collaborative annotation & curation. It is a cost-effective environment for annotation and curation projects, enabling you to harness a broadly distributed workforce and monitor progress & results remotely in real time.

It’s also very easy to use. A new project can be up and running in less than five minutes. (As far as we know, there is nothing else like it in this field.)

GATE Teamware delivers a multi-function user interface over the Internet for viewing, adding and editing text annotations. The web-based management interface allows for project set-up, tracking, and management:

  • Loading document collections (a “corpus” or “corpora”)
  • Creating re-usable project templates
  • Initiating projects based on templates
  • Assigning project roles to specific users
  • Monitoring progress and various project statistics in real time
  • Reporting of project status, annotator activity and statistics
  • Applying GATE-based processing routines (automatic annotations or post-annotation processing)

I have known about the GATE project in general for years and came to this site after reading: Crowdsourced Legal Case Annotation.

Could be the basis for annotations that are converted into a topic map, but…, I have been a sysadmin before. Maintaining servers, websites, software, etc. Great work, interesting work, but not what I want to be doing now.

Then I read:

Where to get it? The easiest way to get started is to buy a ready-to-run Teamware virtual server from

Not saying it will or won’t meet your particular needs, but, certainly is worth a “look see.”

Let me know if you take the plunge!


Wednesday, May 2nd, 2012

OpenMeetings: Open-Source Web-Conferencing

From the website:

Openmeetings provides video conferencing, instant messaging, white board, collaborative document editing and other groupware tools using API functions of the Red5 Streaming Server for Remoting and Streaming.

OpenMeetings is a project of the Apache Incubator, the old project website at GoogleCode will receive no updates anymore. The website at Apache is the only place that receives updates.

OpenMeetings is the type of application that could benefit from subject-centric capabilities.

Even “in-house” as they say, not all participants will share a common vocabulary.

There are commercial applications that make that and other unhelpful assumptions. Write if you need contact details.


Thursday, February 16th, 2012

Effectopedia – An Open Data Project for Collaborative Scientific Research, with the aim of reducing Animal Testing by Velichka Dimitrova, Coordinator of the Open Economics Working Group and Hristo Alajdov, Associate Professor at Institute of Biomedical Engineering at the Bulgarian Academy of Sciences.

From the post:

One of the key problems in natural science research is the lack of effective collaboration. A lot of research is conducted by scientists from different disciplines, yet cross-discipline collaboration is rare. Even within a discipline, research is often duplicated, which wastes resources and valuable scientific potential. Furthermore, without a common framework and context, research that involves animal testing often becomes phenomenological and little or no general knowledge can be gained from it. The peer reviewed publishing process is also not very effective in stimulating scientific collaboration, mainly due to the loss of an underlying machine readable structure for the data and the duration of the process itself.

If research results were more effectively shared and re-used by a wider scientific community – including scientists with different disciplinary backgrounds – many of these problems could be addressed. We could hope to see a more efficient use of resources, an accelerated rate of academic publications, and, ultimately, a reduction in animal testing.

Effectopedia is a project of the International QSAR Foundation. Effectopedia itself is an open knowledge aggregation and collaboration tool that provides a means of describing adverse outcome pathways (AOPs)1 in an encyclopedic manner. Effectopedia defines internal organizational space which helps scientist with different backgrounds to know exactly where their knowledge belongs and aids them in identifying both the larger context of their research and the individual experts who might be actively interested in it. Using automated notifications when researchers create causal linkage between parts of the pathways, they can simultaneously create a valuable contact with a fellow researcher interested in the same topic who might have a different background or perspective towards the subject. Effectopedia allows creation of live scientific documents which are instantly open for focused discussions and feedback whilst giving credit to the original authors and reviewers involved. The review process is never closed and if new evidence arises it can be presented immediately, allowing the information in Effectopedia to remain current, while keeping track of its complete evolution.

Sounds interesting but there is no link to the Effectopedia website. Followed links a bit and found: Effectopedia at SourceForge.

Apparently still in pre-alpha state.

I remember more than one workspace project so how do we decide whose identifications/terminology gets used?

Isn’t that the tough nut of collaboration? If scholars (given my background in biblical studies) decide to collaborate beyond their departments, they form projects, but that are less inclusive than all workers in a particular area. The end result being there are multiple projects with different identifications/terminologies. How do we bridge those gaps?

As you know, my suggestion is that everyone keeps their own identifications/terminologies.

Curious though if everyone does, keeps their own identifications/terminologies, if they will be able to read enough of another project’s content to understand that it is meaningful in their quest?

That is a topic map author deciding that two or more representatives represent the same subject may not carry over to users of the topic map having the same appreciation.

Network Graph Visualizer

Wednesday, December 14th, 2011

Network Graph Visualizer

I ran across this at Github while tracking the progress of a project.

Although old hat (2008), I thought it worth pointing out as a graph that has one purpose, to keep developers informed of each others’ activities in a collaborative environment, and it does that very well.

I suspect there is a lesson there for topic map software (or even software in general).

Looking for volunteers for collaborative search study

Sunday, November 13th, 2011

Looking for volunteers for collaborative search study

From the post:

We are about to deploy an experimental system for searching through CiteSeer data. The system, Querium, is designed to support collaborative, session-based search. This means that it will keep track of your searches, help you make sense of what you’ve already seen, and help you to collaborate with your colleagues. The short video shown below (recorded on a slightly older version of the system) will give you a hint about what it’s like to use Querium.

You may also want to visit the Session Search page.

Could be your opportunity to help shape the future of searching! Not to mention being a window into potentials for collaborative topic map authoring!

Chess@home Building the Largest Chess AI ever

Friday, September 9th, 2011

Chess@home Building the Largest Chess AI ever

From the post:

Many people are familiar with the SETI@home project: a very large scale effort to search for patterns from alien civilizations in the ocean of data we receive from the sky, using the computing power of millions of computers around the globe (“the grid”).

SETI@home has been a success, obviously not in finding aliens, but in demonstrating the potential of large-scale distributed computing. Projects like BOINC have been expanding this effort to other fields like biology, medicine and physics.

Last weekend, a team at Joshfire (Thomas, Nathan, Mickael and myself) participated in a 48-hour coding contest called Node Knockout. Rules were simple: code the most amazing thing you can in the weekend, as long as it uses server-side JavaScript.

JavaScript is an old but exciting technology, currently undergoing a major revival through performance breakthroughs, server-side engines, and an array of new possibilities offered by HTML5. It is also the language that has the biggest scale. Any computer connected to the web can run your JavaScript code extremely easily, just by typing an URL in a browser or running a script in a command line.

We decided to exploit this scale, together with Node’s high I/O performance, to build a large-scale distributed computing project that people could join just by visiting a web page. Searching for aliens was quickly eliminated as we didn’t have our own antenna array available at the time. So we went with a somewhat easier problem: Chess.

Easier problem: Take the coming weekend and sketch out how you think Javascript and/or HTML5 are going to impact the authoring/delivery of topic maps.

Collaborating with Selfish People

Saturday, September 3rd, 2011

Topic maps don’t require collaboration to be authored or maintained but unless your client has an unlimited budget, collaboration is one way to extend the reach and utility of your topic map.

The question is how to engender cooperation in an environment populated by selfish users? (US intelligence services being a good example.)

I ran across a grant summary by Jared Saia of the University of New Mexico:

Beyond Tit-for-Tat: New Techniques for Collaboration in Network Security Games

which reads in part:

Motivation and Problem: How can we ensure collaboration on the Internet, where populations are highly fluctuating, selfish, and unpredictable? We propose a new algorithmic technique for enabling collaboration in network security games. Our technique, Secure Multiparty Mediation (SMM), improves on past approaches such as tit-for-tat in the following ways: (1) it works even in single round games; (2) it works even when the actions of players are never revealed; (3) it works even in the presence of churn, i.e. players joining and leaving the game.

It impressed the NSF: Award Abstract #1017509.

Then I found:

Scalable Mechanisms for Rational Secret Sharing.

You probably want to watch Jared Saia’s homepage and publications.

An attempt to create a solution that doesn’t involve changing human nature. The latter being remarkably resistant to change. Just ask the Catholic Church.

Everything is Connected – Building a
collaborative environment on Neo4J

Wednesday, June 8th, 2011

Everything is Connected – Building a collaborative environment on Neo4J


Neil Ellis will be giving a talk on the value of using the Neo4J graph database to build a collaborative infrastructure. He will explore how the choice of storage mechanism affects application design and ultimately what functionality systems provide for their users. Finally, by looking at the value of serendipity in collaborative systems he will hopefully convince you that Neo4J is more than just an alternative to an RDMS.

An interesting presentation on serendipity and graph databases but not very long on technical content about Neo4J. A number of interesting observations and so I do think the presentation is worth the time to watch.

I have written asking that the slides be posted.

The Science and Magic of User and Expert Feedback for Improving Recommendations

Friday, May 27th, 2011

The Science and Magic of User and Expert Feedback for Improving Recommendations by Dr. Xavier Amatriain (Telefonica).


Recommender systems are playing a key role in the next web revolution as a practical alternative to traditional search for information access and filtering. Most of these systems use Collaborative Filtering techniques in which predictions are solely based on the feedback of the user and similar peers. Although this approach is considered relatively effective, it has reached some practical limitations such as the so-called Magic Barrier. Many of these limitations strive from the fact that explicit user feedback in the form of ratings is considered the ground truth. However, this feedback has a non-negligible amount of noise and inconsistencies. Furthermore, in most practical applications, we lack enough explicit feedback and would be better off using implicit feedback or usage data.

In the first part of my talk, I will present our studies in analyzing natural noise in explicit feedback and finding ways to overcome it to improve recommendation accuracy. I will also present our study of user implicit feedback and an approach to relate both kinds of information. In the second part, I will introduce a radically different approach to recommendation that is based on the use of the opinions of experts instead of regular peers. I will show how this approach addresses many of the shortcomings of traditional Collaborative Filtering, generates recommendations that are better perceived by the users, and allows for new applications such as fully-privacy preserving recommendations.

Chris Anderson: “We are leaving the age of information and entering the age of recommendation.”

I suspect Chris Anderson must not be an active library user. Long before recommender systems, librarians have been making recommendations to researchers, patrons and children doing homework. I would say we are returning to the age of librarians, assisted by recommender systems.

Librarians use the reference interview so that based on feedback from patrons they can make the appropriate recommendations.

If you substitute librarian for “expert” in this presentation, it becomes apparent the world of information is coming back around to libraries and librarians.

Librarians should be making the case, both in the literature but to researchers like Dr. Amatriain, that librarians can play a vital role in recommender systems.

This is a very enjoyable as well as useful presentation.

For further information see:

HeyStaks launches: Social and Collaborative Web Search App

Saturday, March 19th, 2011

HeyStaks launches: Social and Collaborative Web Search App

Jeff Dalton’s preliminary notes on a new collaborative web application.

Producing Open Source Software

Friday, February 25th, 2011

Producing Open Source Software

I can’t imagine why my digital page turning should have leap to “Handling Difficult People,” but it did. 😉

Actually just skimming the TOC, this looks like a good book for any open source project.

My question to you, once you have had a chance to read it, could the title also be:

Producing Open Source Topic Maps?

Why/Why Not?

Seems to me that the topic maps community could be more collaborative than it is.

I am sure others feel the same way, so why doesn’t it happen more often?

Software for Non-Human Users?

Sunday, February 13th, 2011

The description of: Emerging Intelligent Data and Web Technologies (EIDWT-2011) is a call for software designed for non-human users.

The Social Life of Information by John Seely Brown and Paul Duguid, makes it clear that human users don’t want to share data because sharing data represents a loss of power/status.

A poll of the readers of CACM or Computer would report a universal experience of working in an office where information is hoarded up by individuals in order to increase their own status or power.

9/11 was preceded and followed by, to this day, by a non-sharing of intelligence data. Even national peril cannot overcome the non-sharing reflex with regard to data.

EIDWT-2011 and conferences like it, are predicated on a sharing of data known to not exist, at least among human users.

Hence, I suspect the call must be directed at software for non-human users.

Emerging Intelligent Data and Web Technologies (EIDWT-2011)

Sunday, February 13th, 2011

2nd International Conference on Emerging Intelligent Data and Web Technologies (EIDWT-2011)

From the announcement:

The 2-nd International Conference on Emerging Intelligent Data and Web Technologies (EIDWT-2011) is dedicated to the dissemination of original contributions that are related to the theories, practices and concepts of emerging data technologies yet most importantly of their applicability in business and academia towards a collective intelligence approach. In particular, EIDWT-2011 will discuss advances about utilizing and exploiting data generated from emerging data technologies such as Data Centers, Data Grids, Clouds, Crowds, Mashups, Social Networks and/or other Web 2.0 implementations towards a collaborative and collective intelligence approach leading to advancements of virtual organizations and their user communities. This is because, current and future Web and Web 2.0 implementations will store and continuously produce a vast amount of data, which if combined and analyzed through a collective intelligence manner will make a difference in the organizational settings and their user communities. Thus, the scope of EIDWT-2011 is to discuss methods and practices (including P2P) which bring various emerging data technologies together to capture, integrate, analyze, mine, annotate and visualize data – made available from various community users – in a meaningful and collaborative for the organization manner. Finally, EIDWT-2011 aims to provide a forum for original discussion and prompt future directions in the area.

Important Dates:

Submission Deadline: March 10, 2011
Authors Notification: May 10, 2011
Author Registration: June 10, 2011
Final Manuscript: July 1, 2011
Conference Dates: September 7 – 9, 2011

KP-Lab System: A Collaborative Environment for Design, Realization and Examination of Different Knowledge Practices

Thursday, October 7th, 2010

KP-Lab System: A Collaborative Environment for Design, Realization and Examination of Different Knowledge Practices Author(s): Ján Parali?, František Babi? Keywords: collaborative system – practices – patterns – time-line – summative information


This paper presents a collaborative working and learning environment called KP-Lab System. It provides a complex and multifunctional application built on principles of semantic web, exploiting also some web2.0 approaches as Google Apps or mashups. This system offers virtual user environment with different, necessary and advanced features for collaborative learning or working knowledge intensive activities. This paper briefly presents the whole system with special emphasis on its semantic-based aspects and analytical tools.

Public Site: (Be aware that FireFox will say this is an untrusted site as of 6 October 2010. Not sure why but I just added a security exception to access the site.)


Exploration of semantic user interfaces is in its infancy and this is another attempt to explore that space.


  1. Create account and login to public site (Organization: none)
  2. Comments on the interface?
  3. Suggestions for changes to interface?
  4. Download/install software (geeks)
  5. Create content (with other class members)
  6. Likes/dislikes managing content on basis of subject identity?

Towards a reputation-based model of social web search

Saturday, October 2nd, 2010

Towards a reputation-based model of social web search Authors: Kevin McNally, Michael P. O’Mahony, Barry Smyth, Maurice Coyle, Peter Briggs Keywords: collaborative web search, heystaks, reputation model


While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (, a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.

The reputation system posed by the authors could easily underlie a collaborative approach to creation of a topic map.

Think collections not normally accessed by web search engines, The National Archives (U.S.) and similar document collections.

Reputation + trails + subject identity = Hard to Beat.

See as a starting point.