Data-Collection for the Sloan Digital Sky Survey: a Network-Flow Heuristic

The goal of the Sloan Digital Sky Survey is ``to map in detail one-quarter of the entire sky, determining the positions and absolute brightnesses of more than 100 million celestial objects''. The survey will be performed by taking ``snapshots'' through a large telescope. Each snapshot can capture up to 600 objects from a small circle of the sky. This paper describes the design and implementation of the algorithm that is being used to determine the snapshots so as to minimize their number. The problem is NP-hard in general; the algorithm described is a heuristic, based on Lagriangian-relaxation and min-cost network flow. It gets within 5-15% of a naive lower bound, whereas using a ``uniform'' cover only gets within 25-35%.Comment: proceedings version appeared in ACM-SIAM Symposium on Discrete Algorithms (1998

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

The Visvalingam algorithm: metrics, measures and heuristics

This paper provides the background necessary for a clear understanding of forthcoming papers relating to the Visvalingam algorithm for line generalisation, for example on the testing and usage of its implementations. It distinguishes the algorithm from implementation-specific issues to explain why it is possible to get inconsistent but equally valid output from different implementations. By tracing relevant developments within the now-disbanded Cartographic Information Systems Research Group (CISRG) of the University of Hull, it explains why a) a partial metric-driven implementation was, and still is, sufficient for many projects but not for others; b) why the Effective Area (EA) is a measure derived from a metric; c) why this measure (EA) may serve as a heuristic indicator for in-line feature segmentation and model-based generalisation; and, d) how metrics may be combined to change the order of point elimination. The issues discussed in this paper also apply to the use of other metrics. It is hoped that the background and guidance provided in this paper will enable others to participate in further research based on the algorithm