I am interested initially in two kinds of mapping of data this semester. The first is a physical, geographic mapping of purchases. By capturing purchase locations (and perhaps later production, manufacturing or agricultural sources), I can root the purchases in space and create spatial relations among them that bear no relationship to their inherent similarities or differences. I wonder, as I undertake this effort, whether I won't see patterns emerge among seemingly unrelated datasets. Do I buy more food when I'm in the city or the suburbs? Does more entertainment spending happen on the West Coast or the East Coast.
Similarly, by capturing dates (and times when possible) I can relate the purchases temporally to things like political events, personal life changes, seasons, etc. What interesting relationships pop up with that kind of review? This, however, will wait until later.
The second type of mapping I want to explore is conceptual mapping of data. How do my purchases group or relate to one another? Is spending greater on staples or on entertainment? If I take it further and characterize those purchases further to describe how I felt when I made the purchase or who the purchase was for I can examine data relationships differently. Do I buy things in brick and mortar stores or at farmer's markets and thrift stores? Are purchase obligatory or discretionary? Which ones make me happy and which ones do I resent?
This approach to examining my spending lends itself more towards conceptual mapping schema like network mapping or to serve as fodder for generative art pieces. They could introduce random data to influence audio pieces or alter color palettes in video, for example. As a result, these pieces may or may not reveal the underlying topic of interest (consumerism, purchases, etc.) but may still expose patterns in the underlying data.
Getting Underway
So the first order of business, after having begun the onerous process of scanning receipts, verifying OCR and filling in missing data from the receipts themselves is identifying how I want to look at this data. Specifically, what other ways do I want to review the data other than just geographically or by totals? What questions do I want to ask now? Later?
I've decided that one way to explore the possible questions that I might ask is by beginning the work of categorizing or classifying the receipts. Already, as I've begun to review the data pulled via OCR from the receipts themselves, more ideas have come to me. I expect that to continue as I dive deeper into this process.
I have a couple of pieces that suggest looking at data as it relates to other data. My Monument to Applied Mathematics is a dynamic three dimensional sculptural graph illustrating the results of how related groups of statistical data influence one another. How does public spending on education influence tax revenues or juvenile crime, for example.
For this semester, I am examining my own spending behavior over several years. Rather than analyze the data to learn something from it, I am interested in numerical relationships between spending data and what kind of art pieces they produce when some aesthetic behavior or characteristics are applied to those relationships.
In general, as a database professional I am interested in data, data relationships, getting meaning from data (information or knowledge as opposed to more data), as a mathematician, patterns in those data appeal to me and as an artist I want to see how those data and patterns can create experiences or art pieces that reveal something about the data or simply just create something “pretty.”
Before any of these relationships can be examined or exploited, the data must be categorized in order to reveal relationships.
So, the next step for me, to serve my longer-term interests is to understand the ideas behind the classification of data.
Ontologies, Categories and Grouping, oh my!
According to Wikipedia, in general ontology means “ the study of the nature of being, existence or reality in general, as well as of the basic categories of being and their relations.” In particular, in the context of my profession, information technology, they define it as “a formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain. “
I like the layered meaning because the latter, the formal representation, lends itself to my immediate interests. Once I can exploit the tools and techniques for creating an ontological view of my data, I can abstract it further in a way that is more in keeping with the philosophical definition. The code, tools, mechanisms, etc. will allow me to create representations of the information buried in and related to my receipts in a way that they allow me to explore the study of myself or my existence in this culture in a way. Issues of consumerism, privacy, etc. all seem rich fodder for examination once the data is collected and classified.
With the rapid advancements in technology and a greater understanding of the power of well-designed information systems, more and more organizations are looking hard at how they manage data, particularly how they get information from the data that they have. With this growing awareness and commitment to managing data as assets rather than as artifacts of transactions, more work is being done in the area of examining ontologies as they relate to business data and practices. They're being used extensively in biomedical research to facilitate data sharing and cross-domain research, for example.
In my work I am spending a good amount of time reviewing the projects and tools that have been developed as a result of an effort of the National Cancer Institute (“NCI”) of the National Institutes of Health (“NIH”) to allow data sharing far beyond the bounds where that has historically occurred. Fears about intellectual property, incompatibilities among proprietary systems and data formats as well as immature data communications technologies have been a “perfect storm” of hurdles that create disincentives to explore data sharing. However, growing citizen awareness of data management possibilities as well as political pressure to manage data more effectively has created a climate where government agencies now require that data collected for research funded in whole or in part by the US Federal Government must be available to be shared with other researchers.
To share data, however, there must either be standards for data collection itself or for communicating characteristics of data (“metadata”) so that potential users can translate it into a format where they can integrate it into their own systems or data sets. As these vocabularies or data dictionaries are developed and adopted on a large scale, referencing those definitions becomes an information management problem in and of itself. How do I find out if anyone has information on genetic markers for Hispanic children below the age of 15 or occupational data for their parents in a pool of tens of thousands of data elements unless those metadata are categorized in a way that facilitates finding that data. Text searching, after all, is still an ineffective way to mine a large pool of data to get a list of search results that having real meaning.
Effective categorization of data allows users of that data to navigate large data sets efficiently in order to find the information that they need. This is where ontologies step in.
Glossary: http://www.jfsowa.com/ontology/gloss.htm
Standards and Standards Bodies
(is-a, has-a)
History
Aristotle's Categories- substance,
- quantity,
- quality,
- relation,
- place,
- time,
- position,
- state,
- action,
- being acted on
Symbolic Logic and Logic Tools
Ramon Lull (1235 – 1316) from Majorca – Lull (“Lullus”) posited that each area of scientific discovery had a root number of fundamental principals and terms that can “be accepted without further inquiry.”
"The diagram ... is the tree of nature and logic by the thirteenth century poet, philosopher, and missionary Ramon Lull. The main trunk supports a version of the tree of Porphyry, which illustrates Aristotle's categories. The ten leaves on the right represent ten types of questions, and the ten leaves on the left are keyed to a system of rotating disks for generating answers. Such diagrams and disks comprise Lull's Ars Magna (Great Art), which was the first attempt to develop mechanical aids to reasoning. It served as an inspiration to the pioneer in symbolic logic, Gottfried Wilhelm Liebniz.
Modern Ontologies
A new development in data management that can be useful for navigating large datasets capitalizes on the development of these ontological mapping schema and other mechanisms to allow computers systems to do part of this work for us. It is the emergence of the Semantic Web.
The Semantic Web
The inventor of the Internet, Tim Berners-Lee proposed “a web of data that can be processed directly and indirectly by machines.” Specifically, Lee says “[t]he Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” Currently, the web is a rich source of material that is largely made available through the use of browsers for people to read, interpret, amend, etc. This last part of the scenario is the critical one – the traditional corpus of web content requires that it be read and interpreted by people. The Semantic Web, on the other hand, proposes an evolution of the web as a platform where data available for computers to analyze, assemble and use to create meaning that can then be used more effectively.
Responding to Berner's Lee's proposal, the W3C, the governing body of the World Wide Web, created the Semantic Web Coordination Group and a host of other working groups to conceptualize how this next generation Internet should work and to define standards that will allow diverse communities to work together to realize this vision.
The Semantic Web Activity site (http://www.w3.org/2001/sw/ ) describes what this means in a way that I find easy to understand. They say:
The Semantic Web is a web of data. There is lots of data we all use every day, and it is not part of the web. I can see my bank statements on the web, and my photographs, and I can see my appointments in a calendar. But can I see my photos in a calendar to see what I was doing when I took them? Can I see bank statement lines in a calendar?
Why not? Because we don't have a web of data. Because data is controlled by applications, and each application keeps it to itself.
The Semantic Web is about two things. It is about common formats for integration and combination of data drawn from diverse sources, where on the original Web mainly concentrated on the interchange of documents. It is also about language for recording how the data relates to real world objects. That allows a person, or a machine, to start off in one database, and then move through an unending set of databases which are connected not by wires but by being about the same thing.
Scientific American creates a compelling scenario that illustrates what the development of a Semantic Web would look like in the future as people conduct the regular business of their lives.
John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole Publishing Co., Pacific Grove, CA, ©2000. Actual publication date, 16 August 1999.
