Infovis vs. Statistical Graphics Authors: Andrew Gelman and Antony Unwin.
One commonality between information visualization and statistical graphics is that it is easily possible to do both poorly. 😉
This is an entertaining overview of both. Recommended.
ORA (Organization Risk Analyzer) is a toolkit developed for the analysis of organizational networks that could prove to be very useful for topic maps when viewed as networks.
From the website:
*ORA is a dynamic meta-network assessment and analysis tool developed by CASOS at Carnegie Mellon. It contains hundreds of social network, dynamic network metrics, trail metrics, procedures for grouping nodes, identifying local patterns, comparing and contrasting networks, groups, and individuals from a dynamic meta-network perspective. *ORA has been used to examine how networks change through space and time, contains procedures for moving back and forth between trail data (e.g. who was where when) and network data (who is connected to whom, who is connected to where …), and has a variety of geo-spatial network metrics, and change detection techniques. *ORA can handle multi-mode, multi-plex, multi-level networks. It can identify key players, groups and vulnerabilities, model network changes over time, and perform COA analysis. It has been tested with large networks (106 nodes per 5 entity classes).Distance based, algorithmic, and statistical procedures for comparing and contrasting networks are part of this toolkit.
Comments on which parts of this toolkit you find the most useful welcome.
International Network for Social Network Analysis
An organization focused on social networks (no surprise there) but also the source of a number of interesting resources, such as software and data sets.
There is a workshop on NetworkX to be offered at Sunbelt 2011.
Registration for the workshop closes 24 January 2011.
The family tree topic map demonstrated by Eric Freese, years ago now, is one example of a social network.
Both the site and organization merit a close look.
From the website:
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
….
Features:
- Standard graph-theoretic and statistical physics functions
- Easy exchange of network algorithms between applications,
disciplines, and platforms- Many classic graphs and synthetic networks
- Nodes and edges can be "anything"
(e.g. time-series, text, images, XML records)- Exploits existing code from high-quality legacy software in C,
C++, Fortran, etc.- Open source (encourages community input)
- Unit-tested
NetworkX is a nice way to display topic maps as graphs.
Its importance for topic maps lies in the ability to study properties of nodes (representatives of subjects, including relationships) and composition of nodes (merging in topic map speak).
Principal Programming Paradigms is an outgrowth of Concepts, Techniques, and Models of Computer Programming
, which is highly recommended for your reading pleasure.
I don’t think there are any universal threads running through semantic technologies but that may simply be my view of those technologies.
Questions:
The Beauty of Data Visualization David McCandless, TED presentation on data visualization, via Alex Popescu.
The visualization of global media fear is a great example.
Visualization can lead to discovery of patterns in information.
It also illustrates how selection of information influences the visualization.
Along with swine flu and killer wasps, I would have included reports of terrorist plots.
Unlike the Fall sweeps, terrorist plots aren’t announced by terrorists in advance. Terrorist plots are announced by, wait for it, governments.
Would plotting government sponsored announcements of terrorist plots illustrate the chimerical nature of reports of terrorist activity?
Questions (class activity):
Actually it is called YouTube Name Mashup.
Enter a first and last name and it selects random YouTube videos.
It requires Chrome or Safari. (Seriously, even Mozilla dies.)
The association part?
Do this at a party, with or without topic mappers.
Divide into two teams. Each team has a turn at suggesting a first and last name for submission.
Teams have until the videos stop to write down a relationship (association to you topic map readers) with the roles in the relationship, between any subject in one video with any subject in the other video.
The best relationship is determined by applause from those attending the party.
Five rounds maximum.
Remember, the point of this exercise is to have fun and practice some imaginative thinking.
User Performance Using An Ontology-Driven Information Retrieval (ONTOIR) System Authors: Myongho Yi
Also published by VDM Verlag User Performance Using An Ontology-Driven Information Retrieval (ONTOIR) System
Abstract:
Enhancing the representation and relationship of information through ontology is a promising alternative approach for knowledge organization. This improved knowledge organization is vital for collocation of information and effective and efficient searching. This study concerned the testing of user performance when searching an ontology-driven information retrieval (ONTOIR) system that shows explicit relationships among resources. The study explores the possibilities of improving user performance in searching for information. The goal was to examine whether or not ontology enhances user performance in terms of recall and search time. The experiment was conducted with 40 participants to evaluate and compare the differences in user performance (recall and search time) between an ontology-driven information retrieval system and a traditional, thesaurus-driven information retrieval system.
Better recall and shorter search time were found when conducting relationship-based queries in an ontology-driven information retrieval system as compared to a thesaurus-based system. Further studies comparing user performance with a cluster-based search engine and an ontology-driven information retrieval system are needed.
FYI, the first link is $89.00 less than the second one.
A topic map used to deliver an ontology driven search and navigation of information interface.
From the website:
OpenNLP is an organizational center for open source projects related to natural language processing. Its primary role is to encourage and facilitate the collaboration of researchers and developers on such projects.
OpenNLP also hosts a variety of java-based NLP tools which perform sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference using the OpenNLP Maxent machine learning package.
OpenNLP is incubating at the Apache Software Foundation (ASF).
Another set of NLP tools for topic map authors.
Lecture notes by Ryan O’Donnell for his Fall 2009 course at Carnegie Mellon University.
Probability is an important topic (sorry) in a number of CS areas.
Probabilistic merging is why I mention it here but understanding it more broadly will be useful in other CS areas as well.
Provenance for Aggregate Queries Authors: Yael Amsterdamer, Daniel Deutch, Val Tannen
Abstract:
We study in this paper provenance information for queries with aggregation. Provenance information was studied in the context of various query languages that do not allow for aggregation, and recent work has suggested to capture provenance by annotating the different database tuples with elements of a commutative semiring and propagating the annotations through query evaluation. We show that aggregate queries pose novel challenges rendering this approach inapplicable. Consequently, we propose a new approach, where we annotate with provenance information not just tuples but also the individual values within tuples, using provenance to describe the values computation. We realize this approach in a concrete construction, first for “simple” queries where the aggregation operator is the last one applied, and then for arbitrary (positive) relational algebra queries with aggregation; the latter queries are shown to be more challenging in this context. Finally, we use aggregation to encode queries with difference, and study the semantics obtained for such queries on provenance annotated databases.
Not for the faint of heart reading.
But, provenance for merging is one obvious application of this paper.
For that matter, provenance should also be a consideration for TMQL.
Comment to win a copy of Data Analysis with Open Source Tools
From the blog:
Want to win a copy? I have five of them up for grabs. For a chance to win, leave a comment below by January 9, 2011, 10:00pm PST. Tell us what you used to make your very first graph. Pencil and graph paper? Excel? R? Jelly beans?
I have entered a comment. Thought I should pass the opportunity along.
Apache OODT is the first ASF Top Level Project status for NASA developed software.
From the website:
Just what is Apacheâ„¢ OODT?
It’s metadata for middleware (and vice versa):
- Transparent access to distributed resources
- Data discovery and query optimization
- Distributed processing and virtual archives
But it’s not just for science! It’s also a software architecture:
- Models for information representation
- Solutions to knowledge capture problems
- Unification of technology, data, and metadata
Looks like a project that could benefit from having topic maps as part of its tool kit.
Check out the 0.1 OODT release and see what you think.
Apache Mahout – Data Mining Class at the Illinois Institute of Technology, by Dr. David Grossman.
Grossman is the co-author of: Information Retrieval: Algorithms and Heuristics (The Information Retrieval Series)(2nd Edition)
The class was organized by Grant Ingersoll, see: Apache Mahout Catching on in Academia.
Definitely worth a visit to round out your data mining skills.
10 Ways to be a Marketing Genius Like Lady Gaga
Just another log for the discussion of how to market topic maps.
A very amusing slide deck that could prove to be quite useful.
I am not real sure of an equivalent for the monster claw hand as marketing for topic maps. Suggestions welcome!
The Top Five Information Management Meltdowns of 2010
Every year produces a number of stories like these.
Pick one and from the published reports, describe how you would incorporate topic maps to help lead to a different outcome. (3-5 pages, citations)
PS: It may be possible that topic maps play no direct role in avoiding the problem but lead to a more useful system.
Lucene and Solr: 2010 in Review
Great highlights of a busy and productive year for both Lucene and Solr.
The US Census Bureau has made its database of economic indicators searchable.
You can even download data for further manipulation, although I must admit being bemused by the “Download straight from website to Excel.”
I wonder if they mean “Excel” in the sense of any old spreadsheet program like people who say “photocopy” by another name, that starts and ends with an “X?” 😉
Probably not. They probably mean a specific bit of software.
Thought I would mention it as one of the many data sources from the US government that can be re-purposed for use with a topic map.
I am sure other governments make similar data sources available.
If you have a favorite one, please forward the URL, a brief description and any comments you want to make about using it with topic maps.
Moving Forward is a blog I discovered via alls things cataloged.
From the Forward blog:
Forward is a Resource Discovery experiment that builds a unified search interface for library data.
Today Forward is 100% of the UW System Library catalogs and two UW digital collections. The project also experiments with additional search contextualization by using web service APIs.
Forward can be accessed at the URL:
http://forward.library.wisconsin.edu/.
Sounds like a great opportunity for topic map fans with an interest in library interfaces to make a contribution.
The Topincs 5.2.0 release has a number of enhancements and bug fixes.
Including Topincs 5.2.0 eliminates all problems on tap devices, like the iPhone and the iPad.
Not owning either one I will have to rely on reports from others.
Download: http://www.cerny-online.com/topincs/downloads/topincs-5.2.0.tar.gz
See the Topincs homepage for the manual and other information.
Indexicality: Understanding mobile human-computer interaction in context Authors: Jesper Kjeldskov, Jeni Paay Keywords: Mobile computing, indexicality, physical context, spatial context, social context, prototype systems, field evaluation, public transport, healthcare, sociality
Abstract:
A lot of research has been done within the area of mobile computing and context-awareness over the last 15 years, and the idea of systems adapting to their context has produced promising results for overcoming some of the challenges of user interaction with mobile devices within various specialized domains. However, today it is still the case that only a limited body of theoretically grounded knowledge exists that can explain the relationship between users, mobile system user interfaces, and their context. Lack of such knowledge limits our ability to elevate learning from the mobile systems we develop and study from a concrete to an abstract level. Consequently, the research field is impeded in its ability to leap forward and is limited to incremental steps from one design to the next. Addressing the problem of this void, this article contributes to the body of knowledge about mobile interaction design by promoting a theoretical approach for describing and understanding the relationship between user interface representations and user context. Specifically, we promote the concept of indexicality derived from semiotics as an analytical concept that can be used to describe and understand a design. We illustrate the value of the indexicality concept through an analysis of empirical data from evaluations of three prototype systems in use. Based on our analytical and empirical work we promote the view that users interpret information in a mobile computer user interface through creation of meaningful indexical signs based on the ensemble of context and system.
One of the more interesting observations by the authors is that the greater the awareness of context, the less information that has to be presented to the user. For a mobile device, with limited display area that is an advantage.
It would be an advantage for other interfaces because even with a lot of screen real estate, it would be counter-productive to over run the user with information about a subject.
Present them with the information relevant to a particular context, leaving the option for them to request additional information.
Bribing Statistics by Aleks Jakulin.
Self reporting (I paid a bribe is the name of the application) of bribery in the United States is uncommon, at least characterized as a bribe.
There are campaign finance reports and analysis that link organizations/causes to particular candidates. Not surprisingly, candidates vote in line with their major sources of funding.
The reason I mention it here is to suggest that topic maps could be used to provide a more granular mapping between contributions, office holders (or agency staff) and beneficiaries of legislation or contracts.
None of those things exist in isolation or without identity.
While one researcher might only be interested in DARPA contracts (to use a U.S. based example), the contract officers and the beneficiaries of those contracts, another researcher may be collecting data on campaign contributions that may include some of the beneficiaries of the DARPA contracts.
Topic maps are a great way to accumulate that sort of research over time.
Parallel prototyping leads to better design results, more divergence, and increased self-efficacy Authors: Steven P. Dow, Alana Glassco, Jonathan Kass, Melissa Schwarz, Daniel L. Schwartz, and Scott R. Klemmer
Abstract:
Iteration can help people improve ideas. It can also give rise to fixation, continuously refining one option without considering others. Does creating and receiving feedback on multiple prototypes in parallel, as opposed to serially, affect learning, self-efficacy, and design exploration? An experiment manipulated whether independent novice designers created graphic Web advertisements in parallel or in series. Serial participants received descriptive critique directly after each prototype. Parallel participants created multiple prototypes before receiving feedback. As measured by click-through data and expert ratings, ads created in the Parallel condition significantly outperformed those from the Serial condition. Moreover, independent raters found Parallel prototypes to be more diverse. Parallel participants also reported a larger increase in task-specific self-confidence. This article outlines a theoretical foundation for why parallel prototyping produces better design results and discusses the implications for design education.
Interesting that I should stumble on this article after posting about parallel processing.
A useful heuristic for the development of subject identifications in an organization.
Is Parallel Programming Hard, And, If So, What Can You Do About It? Editor Paul E. McKenney
Kirk Lowery forwarded this link to my attention.
Just skimming the first couple of chapters, I have to say it has some of the most amusing graphics I have seen in any CS book.
Performance, productivity and generality are all goals of parallel programming, as cited by this book.
I have to wonder though if subject recognition tasks, analogous to computer vision, that are inherently parallel.
Doing them in parallel does not make them easier but not doing them in parallel certainly makes them harder.
For example, consider the last time you failed to recognize someone who wasn’t in the location or context where you normally see them.
Do you recognize the context in addition or in parallel to your recognition of the person’s face?
Questions:
Map of American English Dialects and Subdialects
From Flowingdata.com a delightful map of American English dialects and subdialects. Several hundred YouTube videos are accessible through the map as examples.
Interesting example of mapping but moreover, looks like an excellent candidate for a topic map that binds in additional resources on the subject.
Enjoy!
Algorithms, Jeff Erickson’s lecture notes.
Mentioned in a post on the Theoretical Computer Science blog, What Lecture Notes Should Everyone Read?.
From the introduction:
Despite several rounds of revision, these notes still contain lots of mistakes, errors, bugs, gaffes, omissions, snafus, kludges, typos, mathos, grammaros, thinkos, brain farts, nonsense, garbage, cruft, junk, and outright lies, all of which are entirely Steve Skiena’s fault. I revise and update these notes every time I teach the course, so please let me know if you find a bug. (Steve is unlikely to care.)
The notes are highly amusing and useful to anyone seeking to improve current subject identification (read searching) practices.
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