Edit: slides

On Thursday, I will be giving a talk at the “The Lived Logics of Database Machinery” workshop, organized by computational culture, which will take place at the Wellcome Collection Conference Centre in London, from 10h to 17h30. I am very much looking forward to this, although I’ll be missing a couple of days from the currently ongoing DMI summer school. This is what I will be talking about:

ORDER BY column_name. The Relational Database as Pervasive Cultural Form

This contribution starts from the observation that, in a way similar to the computational equivalence of programming languages, the major types of database models (network, relational, object-oriented, etc.) and implementations are all able to store and manage a very large variety of data structures. This means that most data structures could be modeled, in one way or another, in almost any existing database system. So why have there been so many intense debates about how to conceive and build database systems? Just like with programming languages, the specific way a database system embeds an abstract concept in a set of concrete methods and mechanisms for specifying, accessing, and manipulating datasets is significant. Different database models and implementations imply different ways of “thinking” data organization, they vary in performance, robustness, and “logistics” (one of the reasons why Oracle’s product succeeded well in the enterprise sector in the 1980s, despite its lack of certain features, was the ability to make backups of a running database), and they provide different modes of interaction with both the data and the system.

The central vector of differentiation, however, is the question how users “see” the data: during the “database debates” of the 1970s and 1980s the idea of the database as a set of tables (relational model) was put in opposition to the vision of the database as a network of records (network model). The difference between the two concerned not only performance, flexibility, and complexity, but also the crucial question who the users of these systems would be in the first place. The supporters of the network model clearly saw the programmer as the target audience for database systems but the promoters of the much simpler relational model and its variants imagined “accountants, engineers, architects, and urban planners” (Chamberlin and Boyce 1974) to directly interact with data by means of a simple query language. While this vision has not played out, according to Michael Stonebreaker’s famous observation, SQL (the most popular, albeit impure implementation of Codd’s relational ideas) has indeed become “intergalactic data-speak” (most packages on the market provide SQL interfaces) and this standardization has strongly facilitated the penetration of database systems into all corners of society and contributed to a widespread “relational view” of data organization and manipulation, even if data modeling is still mostly in expert hands.

The goal of this contribution is to examine this “relational view” in terms of what Jack Goody called the “modes of thought” associated with writing, and in particular with the list form, which “encourages the ordering of the items, by number, by initial sound, by category, etc.” (Goody 1977). As with most modern technologies, the relational model implies a complex set of constraining and enabling elements. The basic structural unit, the “relation” (what most people would simply call a table) disciplines data modeling practices into logical consistency (tables only accept tuples/rows with the same attributes) while remaining “semantically impoverished” (Stonebreaker 1993). Heterogeneity is purged from the relational model on the level of modeling, especially if compared to navigational approaches (e.g. XPath or DOM), but the “set-at-a-time” retrieval concept, combined with a declarative query language, affords remarkable flexibility and expressiveness on the level of data selection. The relational view thus implies an “ontology” consisting of regular, uniform, and only loosely connected objects that can be ordered in a potentially unlimited number of ways at the time of retrieval (by means of the query language, i.e. without having to program explicit retrieval routines). In this sense, the relational model perfectly fits the qualities that Callon and Muniesa (2005) attribute to “powerful” calculative agency: handle a long list of diverse entities, keep the space of possible classifications and reclassifications largely open, multiply possible hierarchies and classifications. What database systems then do, is bridging the gap between these calculative capacities and other forms of agency by relating them to different forms of performativity (e.g., in SQL speak, to SELECT, TRIGGER, and VIEW).
While the relational model’s simplicity has led to many efforts to extend or replace it in certain application areas, its near universal uptake in business and government means that the logistics of knowledge and ordering implied by the relational ontology resonate through the technological layers and database schemas into the domains of management, governance, and everyday practices.

I will argue that the vision of the “programmer as navigator” trough a database (Bachman 1973) has, in fact, given way to a setting where database consultants, analysts, and modelers sit between software engineering on the one side and management on the other, (re)defining procedures and practices in terms of the relational model. Especially in business and government sectors, central forms of management and evaluation (reporting, different forms of data analysis, but also reasoning in terms of key performance indicators and, more generally, “evidence based” management) are directly related to the technological and cognitive standardization effects derived from the pervasiveness of relational databases. At the risk of overstretching my argument, I would like to propose that Thrift’s (2005) “knowing capitalism” indeed knows (largely) in terms of the relational model.

Post filed under abstract, database, epistemolgy, ontologies, software studies.

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