Of Media, Data, Documents: An Argument for the Importance of Relational Technology to the Project of Humanities Computing

Of Media, Data, Documents: An Argument for the
Importance of Relational Technology to the Project of Humanities
Computing

Rafael
C.
Alvarado

McGraw Center for Teaching and Learning
Princeton University
alvarado@princeton.edu

1999

University of Virginia

Charlottesville, VA

ACH/ALLC 1999

editor

encoder

Sara
A.
Schmidt

Summary
The idea that relational database technology is inappropriate for humanities
computing applications (HCA) is now commonplace. A standard argument is that
the allegedly two-dimensional tabular format of relational data is an
outmoded method of information storage that forces us to "dumb down" our
material (Hockey 1998). Others argue that hierarchical and object-oriented
structures are better suited to the modeling of sequentially arbitrary
format of semiotic and textual information (Simons 1997). Still others,
while not opposed to database technology in principle, find no use for it.
Such views in general do not follow from a thorough-going familiarity with
either the practical possibilities or theoretical implications of the
relational model. Far from being outmoded, the relational model--first
articulated by Codd in 1970 (1970) and now the foundation for a number
database management systems--stands as one of the few fundamental conceptual
innovations in the history information technology. In addition, it is one of
the only such technologies to avail itself fully of the hypertextual
properties of the digital medium. Indeed, this feature allows us to address
another commonplace among humanities computing specialists be addressed,
namely the idea that marked-up documents remain "too smart" for the tools
used to access them (Seaman 1998).
The value of the relational model to an HCA is best seen in the context of
the three-tiered architecture of humanities computing applications. At one
level, we have first-order collections of primary sources in the form of
digital media objects, such as electronic texts, digital images, digital
sounds, videos and other media types, including numeric datasets. At a
higher level we have the second-order interpretive and rhetorical documents
that result from the selection, manipulation and incorporation of
first-order objects into presentations of various formats, such as books,
slide shows, web sites, interactive programs, and virtual reality spaces.
Between these two levels resides a layer of metadata and tools that provide
access to and control over first-order media to aid scholars in the
production of second-order documents. I call this middle layer a "digital
collections management system" (DCMS).
From a structural perspective, an HCA is a system whereby syntagmatically
structured documents are transformed by various operations--pattern
matching, selection, sorting, collection, subtraction, addition, etc.--and
incorporated into other syntagmatic structures. Thus, the middle layer is
essentially a transformational engine. The capabilities of this engine are a
direct function of the operations it is able to perform.
One form of DCMS consists of metadata stored in the form of SGML-encoded text
files or in the headers of media files, which files are in turn stored in a
file system and indexed by a web-accessible search engine, such as OpenText.
There are two frequently cited advantages to this type of DCMS. The first is
that since the media files in a collection possess their own data,
information can travel with the files and thus not be tied to any DCMS. The
second is that the hierarchical and sequential format of SGML allows us to
model data in the form that it is actually found, and therefore to access
and work with the media in an intuitive fashion.
Although both of these features are useful, they possess serious drawbacks.
In the first case, there are good reasons for decoupling media files from
their metadata since, except for properties intrinsic to the digital file
itself--such as its size, location and format--none of the metadata itself
belongs to the media file. For example, all information about a media file
source--such as a photographic negative owned by an archive--is logically
independent of the derived digital file and associated with any other media
files derived from the same source, or which have been copied and modified
from an archived digital file. The non-intrinsic relationship between media
file and metadata is even more pronounced in the case of the
representational content associated with the file source--such as the
figures that populate a painting or text. Linking metadata to media means
creating redundant metadata instances, which is not only inefficient, but a
problem to maintain.
Regarding the second point, the hierarchical and syntagmatic format of
metadata storage is an advantage only if one is interested in performing
comparatively few structural transformations on the first-order material one
seeks to deploy in second-order works. For example, if one uses electronic
texts to search for extractable passages, or to build concordance files, or
to describe the statistical distribution of words and passages in a text or
corpus, the structural operations required to perform such tasks can be
accomplished with a standard indexing engine. However, if one is interested
in the systematic decomposition of texts, and in the recombination of
textual elements with elements drawn from other texts or from metadata
associated with other media objects, then the transformational requirements
for these operations will exceed what is provided by an indexing engine by
itself. For example, to conduct the kind of structural analysis of mythic
texts proposed by Lévi-Strauss (Lévi-Strauss 1963), one must be able to
describe a text in a paradigmatic, polythetic form. But it is precisely this
form of organization that the mark-up approach inhibits, first by its
emphasis on syntagm over paradigm, and second by the effective prohibition
of over-lapping tags.
It is significant that both of these problems derive from the same
intuitivist principles that argue against the use of the relational model.
For both problems can be addressed precisely to the degree that the
decidedly unintuitive principles of normalization are employed in the design
of a DCMS data model. This is because normalization forces us to analyze our
material, not simply describe it. In the process of defining the categories
and relationships associated with the materials of humanistic research, we
not only demystify objects by disarticulating from them the data that they
supposedly contain, we raise the relationships between objects to the level
of observation when they would otherwise remain hidden. The great advantage
of this work is not only that we produce a more flexible and powerful DCMS,
but that we produce a database that becomes an encyclopedic work in
itself.
In my talk I will illustrate these points with examples from HCAs at
Princeton. In doing so I will discuss the design of a DCMS that combines the
advantages of mark-up technologies (such as XML), relational database
technologies, and object-oriented application technologies.

References

E.
F.
Codd

A Relational Model of Data for Large Shared Data
Banks

Communications of the ACM

13

377-387
1970

>
S.
Hockey

Humanities Computing and Scholarship in the 21st
Century

A special presentation in a New York University series
of colloquia on uses of computers and communications, given Friday,
April 3 at 2 p.m. in Room 109 Warren Weaver Hall, 251 Mercer Street
at West 4th St.

1998

C.
Lévi-Strauss

Structural anthropology

Claire
Jacobson

Brooke
Grundfest
Schoepf

New York
Basic Books
1963

D.
Seaman

Presentation on the Electronic Text Center at the
University of Virginia

A talk given the New Tools for Teaching and Research
Seminar at Princeton University, June 1998

1998

G.
F.
Simons

Conceptual modeling versus visual modeling: a
technological key to building consensus

Computers in the Humanities

30

303-319
1997