try to make one of them more important than the others.

Around 1997, many of us began to think the popular label IT (information technology) would reconcile these three parts under a single umbrella unique to computing. 3, 7 Time has proved us wrong. IT now connotes technological infrastructure and its financial and commercial applications, but not the core technical aspects of computing.

a Computing Paradigm

There is something unsatisfying about thinking of computing as a “blend of three sub-paradigms.” What new paradigm does the blend produce?

Recent thinking about this question has produced new insights that, taken together, reveal a computing paradigm. A hallmark of this thinking has been to shift attention from computing machines to information processes, including natural information processes

such as DNA transcription. 2, 6 The great principles framework interprets computing through the seven dimensions of computation, communication, co-ordination, recollection, automation, evaluation, and design (see http:// greatprinciples.org). The relationships framework interprets computing as a dynamic field of many “ implementation” and “influencing” interactions. 10 There is now a strong argument that computing is a fourth great domain of science alongside the physical, life, and social sciences. 5

These newer frameworks all recognize that the computing field has expanded dramatically in the past decade. Computing is no longer just about algorithms, data structures, numerical methods, programming languages, operating systems, networks, databases, graphics, artificial intelligence, and software engineering, as it was prior to 1989. It now also includes

there is an
interesting
distinction between
computational
expressions and the
normal language of
engineering, science,
and mathematics.

table 1. Sub-paradigms embedded in computing.

Initiation

Engineering

Create statements about desired system actions and responses (requirements)

hypothesize possible relationships among objects

(theorem)

Construct a model that explains the observation and enables predictions

(model)

Create formal statements of system functions and interactions ( specifications)

Perform experiments and collect data (validate)

Design and implement prototypes (design)

Interpret results Test the prototypes Act on results (predict) Act on results (build)

math Characterize objects of study (definition)

Science Observe a possible recurrence or pattern of phenomena (hypothesis)

Conceptualization

Realization

evaluation Action

Deduce which relation-
ships are true (proof)
Interpret results
Act on results (apply)

table 2. the computing paradigm.

Initiation

Conceptualization

Realization

evaluation

Action

Computing

Determine if the system to be built (or observed) can be represented by information processes, either finite (terminating) or infinite (continuing interactive). Design (or discover) a computational model (for example, an algorithm or a set of computational agents) that generates the system’s behaviors. Implement designed processes in a medium capable of executing its instructions. Design simulations and models of discovered processes. Observe behaviors of information processes. Test the implementation for logical correctness, consistency with hypotheses, performance constraints, and meeting original goals. evolve the realization as needed. Put the results to action in the world. monitor for continued evaluation.

exciting new subjects including Internet, Web science, mobile computing, cyberspace protection, user interface design, and information visualization. The resulting commercial applications have spawned new research challenges in social networking, endlessly evolving computation, music, video, digital photography, vision, massive multiplayer online games, user-generated content, and much more.

The newer frameworks also recognize the growing use of the scientific (experimental) method to understand computations. Heuristic algorithms, distributed data, fused data, digital forensics, distributed networks, social networks, and automated robotic systems, to name a few, are often too complex for mathematical analysis but yield to the scientific method. These scientific approaches reveal that discovery is as important as construction or design. Discovery and design are closely linked: the behavior of many large designed systems (such as the Web) is discovered by observation; we design simulations to imitate discovered information processes. Moreover, computing has developed search tools that are helping make scientific discoveries in many fields.

The newer frameworks also recognize natural information processes in many fields including sensing and cognition in living beings, thought processes, social interactions, economics, DNA transcription, immune systems, and quantum systems. Computing concepts enable new discoveries and understandings of these natural processes.

The central focus of the computing paradigm can be summarized as