Ian Lumb’s Data Science Portfolio

Ian Lumb’s Data Science Portfolio

I had the very fortunate opportunity to present some of my research at GTC 2017 in Silicon Valley. Even after 3 months, I found GTC to be of lasting impact. However, my immediate response to the event was to reflect upon my mathematical credentials – credentials that would allow me to pursue Deep Learning with the increased breadth and depth demanded by my research project. I crystallized this quantitative reflection into a very simple question: Do I need to go back to school? (That is, back to school to enhance my mathematical credentials.)

There were a number of outcomes from this reflection upon my math creds for Deep Learning. Although the primary outcome was a mathematical ‘gap analysis’, a related outcome is this Data Science Portfolio that I’ve just started to develop. You see, after I reflected upon my mathematical credentials, it was difficult not to broaden and deepen that reflection; so, in a sense, this Data Science Portfolio is an outcome of that more-focused reflection.

As with the purely mathematical reflection, the effort I’m putting into self-curating my Data Science Portfolio allows me to showcase existing contributions (the easy part), but simultaneously raises interesting challenges and opportunities for future efforts (the difficult part). More on the future as it develops …

For now, the portfolio is organization into two broad categories:

  • Data Science Practitioner – intended to showcase my own contributions towards the practice of Data Science
  • Data Science Enabler – intended to showcase those efforts that have enabled other Data Scientists

At the end, and for now, there is a section on my academic background – a background that has shaped so much of those intersections between science and technology that have been captured in the preceding sections of the portfolio.

Although I expect there’ll be more to share as this portfolio develops, I did want to share one observation immediately: When placed in the context of a portfolio, immune to the chronological tyranny of time, it is fascinating to me to see themes that form an arc through seemingly unrelated efforts. One fine example is the matter of semantics. In representing knowledge, for example, semantics were critical to the models I built using self-expressive data (i.e., data successively encapsulated via XML, RDF and ultimately OWL). And then again, in processing data extracted from Twitter via Natural Language Processing (NLP), I’m continually faced with the challenge of ‘retaining’ a modicum of semantics in approaches based upon Machine Learning. I did not plan this thematic arc of semantics; it is therefore fascinating to see such themes exposed – exposed particularly well by the undertaking of portfolio curation.

There’s no shortage of Data Science portfolios to view. However one thing that’s certain, is that these portfolios are likely to be every bit as diverse and varied as Data Science itself, compounded by the uniqueness of the individuals involved. And that, of course, is a wonderful thing.

Thank you for taking the time to be a traveller at the outset of this journey with me. If you have any feedback whatsoever, please don’t hesitate to reach out via a comment and/or email to ian [DOT] lumb [AT] gmail [DOT] com. Bon voyage!

On Knowledge-Based Representations for Actionable Data …

I bumped into a professional acquaintance last week. After describing briefly a presentation I was about to give, he offered to broker introductions to others who might have an interest in the work I’ve been doing. To initiate the introductions, I crafted a brief description of what I’ve been up to for the past 5 years in this area. I’ve also decided to share it here as follows: 

As always, [name deleted], I enjoyed our conversation at the recent AGU meeting in Toronto. Below, I’ve tried to provide some context for the work I’ve been doing in the area of knowledge representations over the past few years. I’m deeply interested in any introductions you might be able to broker with others at York who might have an interest in applications of the same.

Since 2004, I’ve been interested in expressive representations of data. My investigations started with a representation of geophysical data in the eXtensible Markup Language (XML). Although this was successful, use of the approach underlined the importance of metadata (data about data) as an oversight. To address this oversight, a subsequent effort introduced a relationship-centric representation via the Resource Description Format (RDF). RDF, by the way, forms the underpinnings of the next-generation Web – variously known as the Semantic Web, Web 3.0, etc. In addition to taking care of issues around metadata, use of RDF paved the way for increasingly expressive representations of the same geophysical data. For example, to represent features in and of the geophysical data, an RDF-based scheme for annotation was introduced using XML Pointer Language (XPointer). Somewhere around this point in my research, I placed all of this into a framework.

A data-centric framework for knowledge representation.

A data-centric framework for knowledge representation.

 In addition to applying my Semantic Framework to use cases in Internet Protocol (IP) networking, I’ve continued to tease out increasingly expressive representations of data. Most recently, these representations have been articulated in RDFS – i.e., RDF Schema. And although I have not reached the final objective of an ontological representation in the Web Ontology Language (OWL), I am indeed progressing in this direction. (Whereas schemas capture the vocabulary of an application domain in geophysics or IT, for example, ontologies allow for knowledge-centric conceptualizations of the same.)  

From niche areas of geophysics to IP networking, the Semantic Framework is broadly applicable. As a workflow for systematically enhancing the expressivity of data, the Framework is based on open standards emerging largely from the World Wide Web Consortium (W3C). Because there is significant interest in this next-generation Web from numerous parties and angles, implementation platforms allow for increasingly expressive representations of data today. In making data actionable, the ultimate value of the Semantic Framework is in providing a means for integrating data from seemingly incongruous disciplines. For example, such representations are actually responsible for providing new results – derived by querying the representation through a ‘semantified’ version of the Structured Query Language (SQL) known as SPARQL. 

I’ve spoken formally and informally about this research to audiences in the sciences, IT, and elsewhere. With York co-authors spanning academic and non-academic staff, I’ve also published four refereed journal papers on aspects of the Framework, and have an invited book chapter currently under review – interestingly, this chapter has been contributed to a book focusing on data management in the Semantic Web. Of course, I’d be pleased to share any of my publications and discuss aspects of this work with those finding it of interest.

With thanks in advance for any connections you’re able to facilitate, Ian. 

If anything comes of this, I’m sure I’ll write about it here – eventually!

In the meantime, feedback is welcome.

Recent Articles on Bright Hub

I’ve added a few more articles over on Bright Hub:

RDF-ization: Is That What I’ve Been Up To?

Recently, on his blogKingsley Idehen wrote:

RDF-ization is a term used by the Semantic Web community to describe the process of generating RDF from non RDF Data Sources such as (X)HTML, Weblogs, Shared Bookmark Collections, Photo Galleries, Calendars, Contact Managers, Feed Subscriptions, Wikis, and other information resource collections.

Although Idehen identifies a number of data sources, he does not explicitly identify two data sources I’ve been spending a fair amount of time with over the past few years: 

Of course, whether the motivation is personal/social-networking or scientific/IT related, the attention to RDF-ization is win-win for all stakeholders. Why? Anything that accelerates the RDF-ization of non-RDF data sources brings us that much closer to realizing the true value of the Semantic Web.

Annotation Modeling: In Press

Our manuscript on annotation modeling is one step closer to publication now, as late last night my co-authors and I received sign-off on the copy-editing phase. The journal, Computers and Geosciences, is now preparing proofs.
For the most part then, as authors, we’re essentially done.
However, we may not be able to resist the urge to include a “Note Added in Proof”. At the very least, this note will allude to:

  • The work being done to refactor Annozilla for use in a Firefox 3 context; and
  • How annotation is figuring in OWL2 (Google “W3C OWL2” for more).

Stay tuned …

CANHEIT 2008: Update on Semantic Topologies Presentation

As I blog, CANHEIT 2008 is winding down …

And although my entire presentation will soon appear online at the conference’s Web site, I thought I’d share here an updated version of the approach image shared previously.

As you’ll see from the presentation, this work is now progressing well. There should be more to share soon.

CANHEIT 2008: Enhanced Abstract

The program specifics for CANHEIT 2008 are becoming available online.
The enhanced abstract for one of my presentations is as follows:

From the Core to the Edge: Automating Awareness of Network Topology through Knowledge Representation

Ian Lumb – Manager Network Operations, Computing and Network Services (York University)

Abstract

Like many other institutions of higher education, York University makes extensive use of Open Source software. This is especially true in the case of monitoring and managing IP (Internet Protocol) devices. On the monitoring front, extensive manual configuration is currently required to make monitoring solutions (e.g., NAGIOS) aware of the topology of the York network. And with respect to managing, NetDisco automatically discovers assets placed on the network, but is unable to abstract away unnecessary complexity in, e.g., rendering schematics of the network topology. These and other examples suggest that NAGIOS and NetDisco operate in the realm of data, and possibly information, but are unable to envisage network topology from a knowledge-representation perspective. Thus the current focus is on applying a recently developed knowledge-representation platform to such routine requirements in network monitoring and management. The platform is based on Sematic Web standards and implementations and has already been proven effective in various scientific contexts. Ultimately our objective is to extract data automatically discovered by NetDisco, represent it using the knowledge-based platform, and transform a topology-aware representation of the data into configuration data that can be ingested by NAGIOS.

A visual representation of the approach is illustrated below.