The word ubiquitous is a key to understanding Korean and Japanese information technology (IT). An example: U-city (U as in ubiquitous) is a concept heavily promoted in Korea. All the major Korean cities strive to earn the U-city label. Ubiquitous, according to an English dictionary, means found or seeming to be found everywhere. How can a city be found everywhere? The very ambition to be found everywhere may seem mysterious, or even suspect to a Westerner.

However, to Koreans and the Japanese ubiquitous has a different meaning. The double semantics of this word is little known. Since I couldn’t find any previous work on this subject I recently wrote an article about it, now published in the proceedings of the ICISA 2010 conference.
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A previous post promised a head-to-head no-mercy Neo4j vs. relational showdown. It also provided Groovy programs to store and retrieve file system data in a Neo4j graph database. Neo4j was recently released in a 1.0 version (see the Neo4j site).

Now it’s time for the relational combatant to enter the scene: Apache Derby.
We will write another Groovy program to store file system data, this time using a Derby relational database. To make the task more interesting we will try to earn a “nosql” medal by not using SQL.
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Neo4j is a graph database, recently released in a 1.0 version (see the Neo4j site). A previous post showed a trivial example of using Neo4j from Groovy.

This post contains an example somewhat closer to real life. It is also the first entry in a Neo vs. relational head-to-head no-mercy showdown.
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