Sharing structured data

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Article

SQL Peer-to-Peer Dynamic Structured Data Processing Collaboration

Using automatic metadata maintenance

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 hierarchical data processing and its flexible and advanced structured processing to support dynamically structured data and its dynamic structured processing.  This flexible dynamic structured processing can change the structure of the data as necessary for the required processing while preserving the relational and hierarchical data principles and semantics of the data to derive correct structured data results even after structure transformations.

This processing will perform freely across remote unrelated peer locations anytime and transparently support unpredictable structured data and data type changes automatically for immediate processing.  Such an automatic peer-to-peer dynamic structured data collaboration is depicted below with its dynamic working hierarchical data structure being modified at each peer site that are labeled P1 to P4 in the diagram directly below.  Its operation is described below the diagram.


Diagram Description

In the diagram above, peer locations: P1, P2, P3, and P4 located anywhere need to collaborate and share their structured data in order to produce a needed result. This process will require unpredictably changing the data structure and data types as it becomes necessary to achieve the desired need and result.  Peer 1 starts the collaboration process by inputting three relational tables, A, B, and C, and models them into a hierarchical structure sending it off to Peer 2 for further processing. Concurrently overlapping with peer 2's processing, Peer 1 also inputs a XML linear hierarchical structure, XYZ, and transforms it into a nonlinear multipath hierarchical structure sending it off to Peer 3 for further processing.

Peer 2 and peer 3 are now performing independently and concurrently. Each is: retrieving their structure input from peer 1, inputting additional relational table data from their different peer home locations, and joining this data to their working data structures. On completion, peer 2 and peer 3 both send their modified data structures off to common peer 4 for further processing.

Peer 4 accepts the modified data structures from both peer 2 and peer 3 which operated concurrently. It hierarchically joins them together using a matching data item value between nodes B and X (B.b=X.x). Peer 4 then eliminates unneeded data items from the joined result using SQL's dynamic SELECT operation to select data items for output  from nodes A, B, E, Y and W.  This SQL query looks like: SELECT A.a, B.b, E.e, Y.y, W.w FROM P2View LEFT JOIN P3View ON B.b=X.x.  This slices out all nodes (C, D, Z ,X, V) that were not referenced by the SELECT statement. This automatically aggregates the necessary data nicely as shown in the diagram above. This process is known as projection in relational processing and node promotion in hierarchical processing. The LEFT JOIN operation hierarchically places P2's structure over P3's structure connected by the ON clause specification of: B.b=X.x. This newly combined hierarchical structure in peer 4 is sent back to Peer 1 for immediate review and processing where the hierarchical data can be selectively output in different formats each with different data selections as shown in the above diagram.

During this entire peer-to-peer collaboration process, the changing data structures and data types are automatically maintained and utilized transparently for the user as needed. The user at each receiving peer can also view the current active structure and its data types. But knowledge of the structure is not necessary for the user to specify in the query because the maintained structure is automatically known and used inherently by the query processor.  Different working data structure versions can also be saved and restored at each peer by the user.

Integrating SQL With Peer-to-Peer Structured Data Collaboration

The problem with performing the above type of dynamic processing is that structured data processing has been limited to fixed static structure processing because dynamically generated structured data cannot be handled today.  Sharing structured data today is performed with shared metadata.  The metadata remains the same, so the structure must remain static. But with dynamic structured processing, the data structure can be dynamically modified as needed to support the required structured operation as shown and described above. This requires automatic metadata maintenance which has not previously been supported by the industry for structured data processing.

An advanced ANSI SQL transparent hierarchical processor prototype, SQLfX (www.adatinc.com/demo.html), has been developed that can support the required dynamic and flexible structured data processing necessary for collaboration. In addition, it uses SQL's inherent hierarchical data processing capabilities that naturally support full multipath dynamic hierarchical data structures. This allows the most complex hierarchical operations to be performed in order to always meet the need at the required time and peer locations with the SQLfX processor. It already operates structure-aware because of its dynamic processing which is also necessary for the required automatic metadata maintenance to occur automatically at each peer because of the dynamically changing data structure and data items.

The ANSI SQL SQLfX flexible dynamic hierarchical processing technology can be enhanced to integrate with peer-to-peer structured data processing collaboration that eliminates the user control necessary for the dynamically changing metadata. The automatic metadata maintenance supplies the updated current metadata that accompanies the data when transmitted between peers. This allows amazingly fast on the fly advanced hierarchical structured data processing collaboration. This enables previously unknown structure results delivered to any peer to be immediate processed automatically by a SQLfX SQL processor located at the peer location.

SQLfX SQL controls the peer-to-peer processing sending and receiving of data structures using new SQL InFile and OutFile keywords added by SQLfX for this purpose. Password and data encryption can also be supported for data security. Further dynamic processing and data structure modification can be performed at each peer visited in any order including in parallel as shown in the diagram above. This opens up the new capability of dynamic structured data processing and its automatic and transparent metadata handling.

SQL Hierarchical Processing Capabilities for Structured Data Collaboration
SQLfX is a powerful new ANSI SQL transparent multipath hierarchical processor that dynamically processes heterogeneous logical flat data like relational and physical hierarchical data such as XML initially. This SQLfX full dynamic hierarchical data processing enables logical and physical structures to be hierarchically joined and modeled dynamically. This hierarchical processing significantly increases the power of the data structure and the queries applied to it.  This is extremely flexible and powerful and is automatically performed without user hierarchical navigation. This operation naturally utilizes the hierarchical semantic information between the pathways to process powerful multipath queries. This can freely reference multipath queries that can for example select data from one path based on data in another path. This unlimited dynamic processing requires special automatic processing known as Lowest Common Ancestor (LCA) processing which enables any conceivable valid multipath query to be processed automatically.  This capability is supported in SQLfX naturally and is missing in other data processors such as XQuery.

An additional valuable benefit of using hierarchical structures is that they are great at naturally organizing and reusing data. Their ability to freely create and grow logical hierarchical multipath structures dynamically also has another overlooked powerful benefit.  It continually increases the data value of the data nonlinearly through automatic data reuse and sharing of the data at higher levels with the multiple lower levels in a pyramid fashion. In addition, the dynamic joining of these hierarchical structures can dynamically increase their data value and querying power many times.  Another powerful advantage are logical hierarchical data structures that are assembled on the fly when creating new structures such as when structures are joined and exist only when and while they are being used. These logical structures add flexibility to hierarchical structures and efficiency to their new use.

Hierarchical structures can also be hierarchically data filtered in their entirety following hierarchical semantics using SQL's WHERE clause to filter the data by data value to only the precise desired data result. This is a complex and powerful operation because data filtering applied to any node data item in a hierarchical structure affects all other nodes of the structure. This is because every node in a hierarchical structure is related to every other node in the data structure. This is demonstrated in Diagram 2 below where filtering node E winds up affecting all other nodes sometimes indirectly through a cousin relationship such as node B. In this example, all nodes with a data occurrence related to data item E equal to 25 are filtered out. The data filtering flow is represented by the arrows.  This is a powerful concept giving multipath processing and WHERE clause hierarchical data processing significant power. This global hierarchical structure filtering is particularly useful when combined with transferring these powerful multipath structures between peers in peer-to-peer processing and the entire structure needs to be filtered for some data value condition. This can be a complex condition involving multiple paths.

SQLfX SQL also supports a very advanced dynamic any-to-any data structure transformation and a data structure virtualization capability. This allows all hierarchically data transformations to be performed semantically correct at a high SQL processing level. With multipath hierarchical processing and any-to-any structure transformations, a variety of hypothetical, experimental, research, exploratory, and problem solving queries can be carried out immediately in an unrestricted fashion further enhanced by powerful real time hierarchical processing collaboration.

Conclusion

All of the powerful and flexible capabilities mentioned in this article make multipath hierarchical structures and their hierarchical processing the perfect opportunity for this dynamic structured data processing collaboration. The universally known SQL interface makes it a perfect API and this is backed by this new relational hierarchical processing technology.  Single one-way data transmissions will also always be available to send to anyone any time because a receive-only version of SQLfX peer-to-peer will be freely available to download and use to automatically view and utilize the one-way transmitted data structure. Additional information on SQLfX's advanced hierarchical processing capabilities and operation can be found at www.adatinc.com. Persons and Companies wanting more information or help on SQL  peer-to-peer dynamic structured data processing collaboration can contact [email protected].

More Stories By Michael M David

Michael M. David is founder and CTO of Advanced Data Access Technologies, Inc. He has been a staff scientist and lead XML architect for NCR/Teradata and their representative to the SQLX Group. He has researched, designed and developed commercial query languages for heterogeneous hierarchical and relational databases for over twenty years. He has authored the book "Advanced ANSI SQL Data Modeling and Structure Processing" Published by Artech House Publishers and many papers and articles on database topics. His research and findings have shown that Hierarchical Data Processing is a subset of Relational Processing and how to utilize this advanced inherent capability in ANSI SQL. Additionally, his research has shown that advanced multipath (LCA) processing is also naturally supported and performed automatically in ANSI SQL, and advanced hierarchical processing operations are also possible. These advanced capabilities can be performed and explained in the ANSI SQL Transparent XML Hierarchical Processor at his site at: www.adatinc.com/demo.html.