Querying Recursive Structures Without Recursive Queries Yi-Ping Phoebe Chen *, & and Robert M. Colomb * Abstract In this paper, we present generic DataBase Model Tools for L-Systems (DBM-L). DBM-L allows representation of L-Systems objects as database structures by a generic automatic translation process. This research contributes the idea of pre-computing recursive structures data into derived attributes using compiler generation. In this work we supplied a method to allow a correspondence biolo- gist’s terms and compiler generated terms and to apply a biological friendly computing environment. We also present a visualization tool (VT) to representing the differ- ent data structures. The whole DBM-L provides a valuable package for biological scientific database management. 1. Introduction Keeping track of and querying data generated by scientific experiments, simulations, and measurements requires database management facilities [2]. One of the most important advantages of database systems is that the underlying mathematics is rich enough to specify very complex operations with a small number of statements in the database language [3]. The ability to store and query information simply is important from a software engineering point of view, since the cost of developing an application is closely related to the number of programming language constructs needed to implement it. In 1968 a biologist, Aristid Lindenmayer, introduced a new type of string rewriting mechanism, subsequently termed L-systems. L-systems are context-sensitive parallel grammars. The main difference between Chomsky grammars [9] and L-systems lies in the method of applying productions. Chomsky grammar productions are applied sequentially one at a time. L-system productions however, are applied in parallel and simultaneously replace all letters in a given word [10]. Particular plant parts in particular states are represented by symbols and associated parameters. The process of transformation is expressed as rules or productions [11]. The Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) research program traced a process for measuring plant structure over time, deriving statistics for development and growth from measurements, and modelling morphology as a set of growth rules expressed in the L-system formalism [7]. At any point during development, a virtual plant’s architecture is defined by a string of symbols representing its constituent parts. The string can be visualised as a schematic or as realistic computer graphic images seen from any angle [12]. The major motivation for this paper is data analysis. Tracking the data collection is a most important task for an analytical scientist. For example, a scientist may choose to run a series of small field scale simulations stepping a number of values through a few parameters (5 parameters with 5 values each is 3125 (5 5 runs) for example). If each run has 100 plants and 100 time-steps and if each plant has 100 parts, then each run generates about 10 6 data values. The total experiment generates 3.125 x 10 9 values in a complex organisation. If such a simulation were performed, then it would seem reasonable that the investigators would spend considerable time looking at the results in a variety of ways. For example: -selecting a subset of values for visualisation -performing aggregations (counts, totals, averages) on selected subsets. From this example we get an indication of the size of data sets we will be dealing with. In fact, there could be multiple sets of data derived by different procedures from other sets. * Department of Computer Science and Electrical Engineering, The University of Queensland, QLD 4072, Australia email: {yiping, colomb}@csee.uq.edu.au & Centre of Cooperative Information Systems, School of Information Sytsems, Faculty of Information Technology, Queensland University of Technology, QLD 4001, Australia email: chenp@fit.qut.edu.au