Meta University/Context

As Moore's Law shifted the paradigm of data processing by claiming that data processing capabilities will grow at an exponential pace. This exponential growth assumption has already transformed economic activities around the world, and most importantly it must be assimilated into knowledge dissemination strategy. The essential thesis of Moore's Law is that it articulated the causal relations between data processing capabilities with an observable physical metrics of data processing instrumentations. This causal revelation of assigning data processing capability with physical scales may provide an testable thesis to position Data Science as the foundation of modern knowledge management approaches. Since all knowledge must be representable in some form of processable data, the functions of universities in general are about creating intuitive data presentation, and providing of highly-available data content.

Moore's Law as the central thesis

Moore's Law is instrumental for shifting the paradigms of institutional and individual learning, because it provides an testable theory to relate the spatial and temporal dimensions of computational/decision-making activities. Before Moore's Law, computational tasks are considered to be a pure mental exercise for humans, the mechanical devices for computation are usually clumsy and rigid, so that they can hardly display any kind of intelligence. Given Moore's Law's revelation, the speed and capabilities of computation, can be extrapolated to various possibilities, therefore, it creates a societal rhythm in planning on how to cram more decision-making, or accountable activities in certain unit spacetime and energy. This revelation creates a tangible way to plan for technology and business process deployment. Therefore, we must be reminded of this intellectual revelation for planning MU. However, it is important to note that Moore's Law has been tighly associated with the idea of exponential growth, while the precise ratio of exponential growth may change from industry to industry.

The Space and Time in Data Universe

The universal context of interest in Meta University is about organizing data in namespaces and temporal contexts. To define a namespace, a pre-defined set of symbols or vocabulary must be formulated, so that it becomes possible to use the symbol set to recursively represent the possibilities of data points. When it comes to representing temporal contexts. There are three generic types of relative temporal positioning, past, present, and future. We related each temporal positioning with one kind of data:

1. File for data in the Past, since we will subscribe to the immutability of past events given causal belief. To make it more concrete, PKC follows MediaWiki's convention to define a unique namespace, called File, for every file.
2. Page for data in the Present. In other words, pages are data interfacing with its consumer at the current stage. Similarly, PKC also utilizes MediaWiki's convention to define a namespace for all pages. In MediaWiki, page namespace is called: Main. We will follow this convention in PKC.
3. Service for data into the Future. A set of computing services will be provisioned to continuously render new data into the future. As of 2022, MedaWiki has not had a namespace dedicated to Service, but it does define an explicit service APIs. To provide a consistent abstraction framework, MU-compliant PKCs will explicitly reserve a namespace, called Service namespace. This namespace will be defined in PKC's MediaWiki LocalSettings.php configuration file. We will likely use the metaNamespace as defined in MediaWiki.org, to explicitly reserve the namespace:Service.

This classification of data in spacetime context, in terms of File/Page/Service, lead our attention to present data to human even non-human data consumers to treat data in a topological vocabulary, enabling a self-contained vocabulary to define data for all application domains.

Services for Data Manipulation

There are many powerful instruments that are changing the way we deal with data, and all things connected by data. One of the instruments is microservice. To learn more about how to apply this instrument in real world applications, please see Sam Newman's talk. In contrast to the term: microservices, macroservices are composed of microservices, and they are often called monolith computing services.