In creating prequels, we wanted to tackle technological gaps not addressed in the Original Trilogy. While slicers, cyes, and sensationaries are not shown in those films, we found it unlikely that a galaxy with hyperspace travel, Death Stars, Lobot's cranial implants, and mechanical hands wouldn't have their equivalents. So we decided these items were "present," just not shown to the movie viewer, giving us the liberty to explore these technologies in our stories.
One novel realm we've mentioned and explored in chapters with Til'trius is the dataverse (or d-verse). Just what is this place that Til'trius and others work in? It's not a virtual reality simulator, not a Matrix, or place where hypercomplex video games would be played. If not, what is the dataverse's purpose?
We imagine that in a futuristic world there could be a problem of having too much information, which is irrelevant, unintegrated, and/or difficult to understand. The challenge would not be in gathering information, but in our inability to know what to ignore and what to synthesize.
The dataverse then is a virtual reality realm where unwieldy data is massaged -- "rubik's-cubed" -- until it becomes useful. Guests of the dataverse, known as "seekers" employ tools to help them compile, sort, integrate, and best present data they possess.
There are four basic types of resources seekers use in the dataverse to achieve the goal of gaining actionable information in a timely fashion:
(a) Bridgers -- these link digital databases and records, both public and private, together for the largest sum of data to explore. They find data leaks in networks, and run complex algorithms to crack through weaker security systems. The better a seeker's bridgers, the better the "sum total" of data he possesses.
(b) Crawlers -- these employ seeker-defined parameters to mine for the most relevant sources of information in the dataset provided by bridgers. Beyond the typical parameters, seekers can indicate for what purpose the desired intelligence is to be used, helping the crawlers more effectively rank the discovered sources.
(c) Weavers -- Digesting the results of the crawlers, weavers extract, compile, and assemble the data, categorizing it and producing relational patterns and reports. They also mark the data for the use of skins.
(d) Skins -- This final level of amalgamation reads the weaver's patterns and markers, projecting rich sensory representations into the seeker's mind, a perpetual illusion of data made flesh. The seeker typically only experiences this end result in his mind, the other processes occurring "under the hood." Different skins can be programmed to activate when appropriate markers set by weavers are encountered. However, not all preset skins are good for displaying information, so the seeker can find himself manually assigning different skins to unknit and reknit the fabric of code to a more comprehensible form.
While the tools are the same for all seekers, not all deploy them competently or use the highest quality of datamining applications. Many seekers develop their own custom applications (especially skins), or tweek configurations to best accomodate their own subjective approach to data. We hope you'll enjoy find the dataverse a fun addition to the Star Wars universe.
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