CDM analysis

The C Data Manager (CDM) is an advanced tool for creating an object-oriented database and for processing queries related to objects stored in that database. The CDM source code was purchased and will be modified over the course of the Arachnid project. In this report, the modified CDM is referred to as MCDM. Using MCDM, a detailed series of experiments was designed and conducted on a Sun Sparcstation. The primary results and analysis of the CDM experiment are provided in this report. The experiments involved creating the Long-form Faint Source Catalog (LFSC) database and then analyzing it with respect to following: (1) the relationships between the volume of data and the time required to create a database; (2) the storage requirements of the database files; and (3) the properties of query algorithms. The effort focused on defining, implementing, and analyzing seven experimental scenarios: (1) find all sources by right ascension--RA; (2) find all sources by declination--DEC; (3) find all sources in the right ascension interval--RA1, RA2; (4) find all sources in the declination interval--DEC1, DEC2; (5) find all sources in the rectangle defined by--RA1, RA2, DEC1, DEC2; (6) find all sources that meet certain compound conditions; and (7) analyze a variety of query algorithms. Throughout this document, the numerical results obtained from these scenarios are reported; conclusions are presented at the end of the document

Disaster knowledge factors in managing disasters successfully

The number of reported natural disasters has increased steadily over the past century and risen very sharply during the past decade. These bring about the loss of lives, property, employment and damage to the physical infrastructure and the environment. Disaster management efforts aim to reduce or avoid the potential losses from hazards, assure prompt and appropriate assistance to victims of disaster, and achieve rapid and effective recovery. While knowledge management can enhance the process of disaster management, there is a perceived gap in information coordination and sharing within the context of disaster management. Identifying key success factors will be an enabler to manage the disasters successfully. In this context, this study aim to identify and map key knowledge success factors for managing disasters successfully through capturing the good practices and lessons learned. The objective of this paper is to present the literature findings on factors which support successful disaster management. Accordingly the identified factors were classified into eight main categories as technological, social, legal, environmental, economical, functional, institutional and political