Evolvable Databases

B Nick Rossiter, Informatics, Northumbria University NE1 8ST, UK,nick.rossiter@unn.ac.uk
http://computing.unn.ac.uk/staff/CGNR1/M A Heather, Sutherland Building, Northumbria University NE1 8ST,
 m.heather@unn.ac.uk
Bioinformatics may entail special and new types of data as well as exploit new techniques like self-organisation but these are all part of the world-wide 21st century topic of information systems. Classical information systems are represented by databases where real-world structures are captured in a machine-friendly fashion for storing in persistent form for searching and updating.  The new generation of information systems may use natural computing as a tool for optimisation, scheduling and the design. But the outcome is still a complex piece of data to be represented by computer. Commercial solutions are already available which extend current database methods to include the rule-based approach, data warehousing, search optimisers, federalism and ontologies (Buckingham, 2004). However these all involve some quite extensive degree of reductionism and therefore loss of information. 

A task of current bioinformatics research is to develop the next generation, which can hold data in its natural structure. That is adapting the processor to match the data not the other way around as present on conventional computers. The one method that seems to have any chance of achieving this transformation is perhaps natural computing itself.  Data arriving unprocessed from automated censors is sometimes referred to as unstructured data but really like natural language the data contains its own meta-data wrapped up within it and is really highly structured (Rossiter 2003). There are a number of techniques to exploit from neural net emulation of the brain (Heather & Rossiter 1991) to the exploitation of quantum mechanical processes on a quantum computer and from genetic algorithms to DNA sticker models that can be applied to massive quantities of data captured in this way from nature. This data can be accumulated and because of its inherent natural structure can be expected to self-organise and can therefore be classed as a vast evolvable database. The advantage of natural computing is that it can achieve massively parallel processing on such a database. 

Rosen (1985,1991) has identified life as a reactive system, which may be formulated by anticipatory systems distinguishable as either weak or strong (Dubois 1998). Current information systems are weak (Rossiter & Heather 2003, 2004) but those in natural computing may be strong and therefore lossless. These can be explored in the very recently advanced star-categories. 

This proposal looks at databases as classically conceived as theoretical structures and applies the same type of reasoning to modern information systems for bioinformatics.  The theory shows that the environment in terms of context plays a much greater phenotypic role. While natural logic goes beyond classical Boolean logic to that of the logic of a topos (Johnstone 2002) as genotypic self-organisation. 

The strong theoretical basis of databases means that the formal representation of data and self-organising process has significance for the field of self-organisation as a whole. At the other end of the spectrum there are the actual applications to be found in bioinformatics. So that because the processes are natural, the methods can underpin the most general of information systems that can be used to store, search and update without any reductionism that results in loss of information and cuts off arbitrarily some possible uses of the data which may emerge in the future. 

This proposal should both advance the theoretical basis of self-organisation as well as provide a way forward for suitable new information systems. 

References:

Buckingham, Steve, Data's future shock, Nature 428 p.774-777 (2004). 

Dubois, D M, Introduction to Computing Anticipatory Systems, Int J Comp Anticipatory Sys 2 3-14 (1998).

Heather, M A, & Rossiter, B N, Semantic Pattern Matching in NetBases, Neurocomputing, 2 173-176 (1991).  

Johnstone, P T, Sketches of an Elephant, A Topos Theory Compendium, Oxford Logic Guides 2 volumes, 43 Clarendon (2002).

Rosen, Robert, Anticipatory Systems Philosophical, Mathematical and Methodological Foundations, Pergamon (1985). 

Rosen, Robert, Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life, Columbia (1991). 

Rossiter, B Nick, & Heather, M A, Anticipation in Constructing and Interrogating Natural Information Systems: from Classical through Nano to Quantum Computing, in: Symposium 7: Biological Systems: Genetics, Evolution, Ecology, Cognitive Models, CASYS'03, Sixth International Conference on Computing Anticipatory Systems, Liège, Belgium, August 11-16, 7:1-2 (2003). 

Rossiter, B Nick, & Heather, M A, Data Structures in Natural Computing: Databases as Weak or Strong Anticipatory Systems, CASYS'03, Sixth International Conference on Computing Anticipatory Systems, Liège, Belgium, American Institute of Physics Conference Proceedings 14pp (2004).

Rossiter, N, From Classical to Quantum Databases with Applied Pullbacks, 78th Meeting Peripatetic Seminar on Sheaves and Logic, Institut de Recherche Mathématique Avancée, Strasbourg University 15-16 February (2003).