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Every ANSI SQL Processor Conatins a Powerful Hierarachical Data Processor

Michael M David

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Hierarchical structures have an inherent ability for significant data value increases beyond the data collected.  This will be shown to exist in hierarchical structures and even more powerfully in their natural hierarchical processing capabilities. These will demonstrate flexible and efficient ways to increase data value automatically and will be discussed in this article. SQL will be used to perform a wide range of hierarchical processing operations that easily demonstrate these increasing data value capabilities. Basic Hierarchical Data Modeling The SQL view definition in Figure 1 below uses a sequence of standard SQL LEFT Outer Joins to model the shown hierarchical structure. This hierarchical view is modeled at the node level to define basic physical structures like IMS or XML. It defines logical hierarchical structures using flat data like relational. The LEFT... (more)

SQL Transparent Hierarchical Processing of Relational, XML and IMS Data

Current SQL support of relational, XML and hierarchical legacy data such as IMS is driven by flattening the hierarchical data in order to integrate it naturally with relational (flat) data so that it can be processed relationally. Unfortunately, this strips out the natural semantics in hierarchical data which has the capability to dynamically increase the value of the data being processed and to perform powerful hierarchical operations. The SQL-92 standard introduced the LEFT Outer Join which offers a powerful alternative to standard relational processing that can be used to perfo... (more)

SQL Peer-to-Peer Dynamic Structured Data Processing Collaboration

Unstructured and XML semi-structured data is now used more than structured data. Unstructured data is useful because of its fuzzy processing applied to this more common ubiquitous data.  But fixed structured data still keeps businesses running day in and day out, which requires consistent predictable highly principled processing for correct results. This means structured data cannot be replaced by unstructured or semi-structured data.  For this reason, it would be very useful to have a general purpose peer-to-peer collaboration capability that can utilize highly principled hierar... (more)

A Complementary Query Language to Google’s Dart

Dart is a new structured data programming language from Google. While unstructured data has become extremely useful, structured data is still extremely important because it keeps businesses running day in and day out. Programming languages still need to be coded by hand and most Google users are not programmers. To fill this large gap for most Google users who have no programming experience, a structured data query language would be very useful. Query languages operate by what data or information is wanted and not how to access or derive it. No programming is necessary to use. Th... (more)

Advanced ANSI SQL Native XML Integration-Part 1

This two-part article will change your view and understanding of standard SQL and its ability to integrate naturally and fully with native XML. The perceived problem with achieving full SQL-based integration of XML is that relational data is flat while XML data is hierarchical, producing a huge impediment to a seamless solution. This belief has prevented a full integration solution, resulting in SQL vendors resorting to nonstandard SQL and external code, whose solutions fall far short of full XML integration. The usual method of integration used by SQL vendors is to shred or fla... (more)