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
C-15-E Firestone Library
Princeton, New Jersey 08544


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.


Codd, E.F. 1970. "A Relational Model of Data for Large Shared Data Banks." Communications of the ACM 13, 377-387.

Hockey, S. 1998. "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.

Lévi-Strauss, C. 1963. Structural anthropology (trans.) Jacobson, Claire Schoepf, Brooke Grundfest. New York: Basic Books.

Seaman, D. 1998. "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.

Simons, G.F. 1997. "Conceptual modeling versus visual modeling: a technological key to building consensus." Computers in the Humanities 30, 303-319.