This PhD thesis utilises diagrams as a language for research and design practice to critically investigate algorithmic prediction. As a tool for practice-based research, the language of diagrams is presented as a way to
read algorithmic prediction as a set of intricate computational geometries, and to write it through critical practice immersed in the very materials in question: data and code. From a position rooted in graphic and interaction design, the research uses diagrams to gain purchase on algorithmic prediction, making it available for examination, experimentation, and critique. The project is framed by media archaeology, used here as a methodology through which both the technical and historical "depths" of algorithmic systems are excavated.

My main research question asks:

How can diagrams be used as a language to critically investigate algorithmic prediction through design practice?

This thesis presents two secondary questions for critical examination, asking:

Through which mechanisms does thinking/writing/designing in diagrammatic terms inform research and practice focused on algorithmic prediction?

As algorithmic systems claim to produce objective knowledge, how can diagrams be used as instruments for speculative and/or conjectural knowledge production?

I contextualise my research by establishing three registers of relations between diagrams and algorithmic prediction. These are identified as: Data Diagrams to describe the algorithmic forms and processes through which data are turned into predictions; Control Diagrams to afford critical perspectives on algorithmic prediction, framing the latter as an apparatus of prescription and control; and Speculative Diagrams to open up opportunities for reclaiming the generative potential of computation. These categories form the scaffolding for the three practice-oriented chapters where I evidence a range of meaningful ways to investigate algorithmic prediction through diagrams.

This includes, the 'case board' where I unpack some of the historical genealogies of algorithmic prediction. A purpose-built graph application materialises broader reflections about how such genealogies might be conceptualised, and facilitates a visual and subjective mode of knowledge production. I then move to producing 'traces', namely probing the output of an algorithmic prediction system|in this case YouTube recommendations. Traces, and the purpose-built instruments used to visualise them, interrogate both the mechanisms of algorithmic capture and claims to make these mechanisms transparent through data visualisations. Finally, I produce algorithmic predictions and examine the diagrammatic "tricks," or 'chicanes', that this involves. I revisit a historical prototype for algorithmic prediction, the almanac publication, and use it to question the boundaries between data-science and divination. This is materialised through a new version of the almanac - an automated publication where algorithmic processes are used to produce divinatory predictions.

My original contribution to knowledge is an approach to practice-based research which draws from media archaeology and focuses on diagrams to investigate algorithmic prediction through design practice. I demonstrate to researchers and practitioners with interests in algorithmic systems, prediction, and/or speculation, that diagrams can be used as a language to engage critically with these themes.

Case Board, Traces, & Chicanes

Diagrams for an archaeology of algorithmic prediction through critical design practice.

PhD Thesis in progress (submitted for examination on 29.01.2020)
Practice-based research by David Benqué at the School of Communication, Royal College of Art, London UK. This research is supported by Microsoft Research Cambridge (UK) as part of their PhD scholarship programme.
Supervisors: Prof. Teal Triggs (RCA), Richard Banks (Microsoft Research)

Complex network diagrams typically involve specific place of icons, connections and labels using a tool like Visio or OmniGraffle using a mouse and constantly zooming in and out for single pixel placement. The goal behind drawthe.net, was to be able to describe the digram in a text file and have it rendered in SVG in the browser.

I simply wanted to be able to draw network diagrams as fast as it could be done on a dry-erase board without using a mouse.

drawthe.net draws network diagrams dynamically from a text file describing the placement, layout and icons. Given a yaml file describing the hierarchy of the network and it's connections, a resulting diagram will be created.

Describe your diagrams with a simple text language and automatically generate an image you can export.
Why?

You love diagrams but don't want to spend time positioning elements.

Your diagram keeps evolving, you want the layout to re-adjust automatically.

You prefer to describe your diagrams with an intuitive text description.

It has always been painful to do ASCII diagrams by hand. This perl application allows you to draw ASCII diagrams in a modern (but simple) graphical interface.

The ASCII graphs can be saved as ASCII or in a format that allows you to modify them later.

SQL::Translator is a group of Perl modules that manipulate structured data definitions (mostly database schemas) in interesting ways, such as converting among different dialects of CREATE syntax (e.g., MySQL-to-Oracle), visualizations of schemas (pseudo-ER diagrams: GraphViz or GD), automatic code generation (using Class::DBI), converting non-RDBMS files to SQL schemas (xSV text files, Excel spreadsheets), serializing parsed schemas (via Storable, YAML and XML), creating documentation (HTML and POD), and more. New to version 0.03 is the ability to talk directly to a database through DBI to query for the structures of several databases.

Through the separation of the code into parsers and producers with an object model in between, it's possible to combine any parser with any producer, to plug in custom parsers or producers, or to manipulate the parsed data via the built-in object model. Presently only the definition parts of SQL are handled (CREATE, ALTER), not the manipulation of data (INSERT, UPDATE, DELETE).

react-developer-roadmap - Roadmap to becoming a React developer in 2018

draw.io is free online diagram software for making flowcharts, process diagrams, org charts, UML, ER and network diagrams

Infer a probabilistic schema for a MongoDB collection.
Usage

mongodb-schema can be used as a command line tool or programmatically in your application as a node module.

mongodb-schema mongodb://localhost:27017 db.collection