Wednesday, September 17, 2014

Three neglected principles of Big Data: identifiers, immutability, and introspection

My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published last year by Morgan Kaufmann.

There are three crucial topics related to data preparation that are omitted from virtually every other Big Data book: identifiers, immutability, and introspection.

A thoughtful identifier system ensures that all of the data related to a particular data object will be attached to the correct object, through its identifier, and to no other object. It seems simple, and it is, but many Big Data resources assign identifiers promiscuously, with the end result that information related to a unique object is scattered throughout the resource, attached to other objects, and cannot be sensibly retrieved when needed. The concept of object identification is of such overriding importance that a Big Data resource can be usefully envisioned as a collection of unique identifiers to which complex data is attached. Data identifiers are discussed in Chapter 2.

Immutability is the principle that data collected in a Big Data resource is permanent, and can never be modified. At first thought, it would seem that immutability is a ridiculous and impossible constraint. In the real world, mistakes are made, information changes, and the methods for describing information changes. This is all true, but the astute Big Data manager knows how to accrue information into data objects without changing the pre-existing data. Methods for achieving this seemingly impossible trick is described in detail in Chapter 6.

Introspection is a term borrowed from object oriented programming, not often found in the Big Data literature. It refers to the ability of data objects to describe themselves when interrogated. With introspection, users of a Big Data resource can quickly determine the content of data objects and the hierarchical organization of data objects within the Big Data resource. Introspection allows users to see the types of data relationships that can be analyzed within the resource and clarifies how disparate resources can interact with one another. Introspection will be described in detail in Chapter 4.

I urge you to read more about my book. Google books has prepared a generous preview of the book contents. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

Jules J. Berman, Ph.D., M.D.tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining