Hiveware® description

Hiveware is a new kind of coöperative software development and execution platform based on natural language formation. Hiveware belongs to the Social networking and User Generated Content categories. Hiveware® is related to ontology and semantic mapping, with one big distinction: Hiveware® deals with future, and not historical information. The technical description of Hiveware is synchronous groupware with evolving overlay. The Hiveware technology is a cognitive technology. Instead of trying to duplicate the human thinking capability, Hiveware augments and extends human thinking with the use of the digital computer. This approach lets humans and computers work coöperatively instead of competively together with digital-thinking supplementing and complementing bio-thinking.

Hiveware® stands for Hyperstructured Interactive Virtual Environment softWare and integrates the development and running of software, as well as who and where content contributions are made. Like natural language, all Hiveware® activity is subjugated an evolving structure that both guides and coördinates the individual contributor.

Today, software development and execution is stuck in the client/server model. Typically, a user first thinks of a question or a query. He or she then steps up to the computer and implicitly asks it or requests a result of some sort (e.g., Are there national parks in Russia? or give me the movie “Gone with the Wind”). The server software, concentrated in a few company’s enormous server farms, is implicitly charged with determining that answer and returning the result in real-time solely based on the user’s naked query. That is not the way natural language works. For natural language all utterances and text are expressed from a context of fellow language contributors, which is called context. Computers can’t understand that context, which has been an unsolved Computer Science problem for 50 years. Querying a server with this or that question avoids the issue of context. Consequently, Computer Science, imprisoned under the current model, has not progressed.

Hiveware addresses the context issue head-on by associating desktop and mobile computers in an elaborate peer-to-peer network (P2P) that exactly matches the real conceptual structure of the involved contributors, or Hiveware-to-Hiveware(H2H). Making this possible is SGML, or Standard Generalized Markup Language, an ISO standard from 1986. SGML makes the subtle leap between human language and machine process-able code. The result is Hiveware®, the first credible implementation of SGML.

Hiveware® will change the following technologies:

·        data mining – for years leading edge software companies have engaged their top personnel in contriving ways to mine after-the-fact information from stagnent data sources, be they books, articles, newspapers or web pages. The number of web crawlers is already legion. The problem with data mining is the activity is based on a false Language Psychological premise: that one can detect with certainty what someone else meant when he wrote or said something about the world. Meaning is something that exists in the mind of the meaning creator, that is, the author of the expression. To capture what the true meaning is, you have to ask the author. Data mining is just guessing. Hiveware® maintains in hives connections to the authors of their expressions and thus preserves contextual meaning.

·        categorization – it is unnatural for humans to step out of their context and describe what they are talking about. But none the less, that is what is necessary to create communcable metadata. We do it, however, when it feels natural. We don’t do it when it doesn’t, for example, when we should name and organize our digital photos or file system. Writing an outline for a term paper is categorizing. Developing templates for Microsoft Word is categorizing. Creating ontologies which the DoD is prone to do is categorizing. Common for all these textual activities is the resulting schema is static. The problem with static categories is, change is difficult and tends not to get done. Natural language changes all the time, however, and therefore the categories we deal with are changing, or evolving as Hiveware® calls it, are changing all the time. For example, Hiveware® for Word will have the capability of altering the SGML structure, a.k.a., Outlining, which will allow the users to let their joint document migrate structurally as needed. Categorizing is the art of creating meta-structure. The beauty of Hiveware® is that this meta-structure, just like natural language, can be accomplished synchronously and seamlessly with the creation of every day content.

·        cut-and-paste practice – most industries today use the cut-and-paste method of working in groups and many use Word 2003-vintage software to do it. Professionals work in groups, make their contributions and the most senior member of the group or the one with the most Word skills defaults to the one who cut-and-pastes the drafts together. Each draft disrupts the group’s author participants as they were used to the old draft or had even printed it off with comments on the pages. Hiveware® lets the group’s members move together as changes are made and they never have to leave the document or replace it with the next draft.

·        windows operating systems – Windows is stil using the kitchen-sink approach regarding its architecture. Added functionality still comes in the form of applications on top of the operating system and the op sys itself is more and more elaborate and vulnerable. Counter-security measures have even hobbled whole versions, like Vista. Seen from the Hiveware® perspective, there is no magic line between the op sys and dll functionality. Theoretically, each PC site only needs the ability to connect to the outside world, a CPU and some RAM. It doesn’t even need a hard disk. Once populated with the basic TCP/IP transporter mechanism (a.k.a, the Hiveware® engine), it can begin populating itself with the necessary and sufficient op sys tools it needs to do that hive’s job. And no more. The “op sys” for any particular hive node becomes the union of whatever the traditional op sys functions needed to run that site’s hives. Take, for example, VMWare and Citrix: these technologies have the singular purpose of transporting a user virtually to the location of a remote PC’s desktop. How ironic and crude that the PC’s op sys left behind has been reduced to teletype (TTY) functionality, not to mention the enormous power of its CPU. Hiveware® simply replicates the data being work on by groups and computer-assists in controlling who and what can be changed in a mutually-exclusive manner and along the categorization boundaries of natural language grammar as made tractible by SGML. All PC’s CPUs and RAM are fully used without hobbling the op sys or transporting the user elsewhere.

·        context searching – web search today is data-metric, that is, statistical. It data mines and surmises meaning. Google spends millions of dollars refining its search engine to do just that and does it better than any other search engine. But it is not context searching, which is the holy grail of search. For example, Google still can’t discern if a search for bill has to do with a restaurant, a governmental body or a part of a bird. Hiveware® does this automatically. Eventually as the number of hives grows, a context search for bill would begin by the searcher travelling like a web crawler down and around the permitting hives until he gets to the meaning group and authors that, on an ongoing basis, defines what he is looking for.

·        load balancing – solving a balancing task would be different from creating a Word template/outline change. The concept would be identified, and a code behavior would be developed for the task. Let’s say a music group has a new video and song they want to send out to their subscribers all over the world. The grammar might be, MusicalGroup: ( NewMusic Video | NewTune ), PushToCloserNetwork*. The next piece of grammar might be: PushToCloserNetwork : SendToMusicGroupSubscriber | RelayContentToCloserNetwork. The point is, the task of the distributed hive would be to balance and distribute the content to the subscribers.

·         server farms gone – Hiveare doesn’t need server farms because it harnesses all of one’s device’s CPU and memory capacity in tandem. Replication of one’s data among one’s devices is the most secure method of backup known today. Hiveware turns the concept of data storage on its head where filling up all of one’s storage that one has already purchased is the norm.