**5. Proposed DSS framework**

106 Emerging Informatics – Innovative Concepts and Applications

*Management control Timeframe = 6months to 1 years*

Pre-event such as Development of resources Development of reporting formats

Post-event such as Treatment of injured Deployment of relief

Pre-event such as Development of procedures for conducting post event damage and loss assessment

Post event such as Declaration of state of

Pre-event such as Replacement for loss of

Personnel and equipment

effects such as epidemics Search and rescue

Post-event such as Coping with secondary

Set up triage/reception

emergency

centers

key

achieve the objective of developing reliable system behaviour, the technology should demonstrate robust behaviour that helps achieve reliability through its innovative mechanisms. The integral data processing requires the technical development that combines internal and external data readable to decision makers. These objectives are useful in motivating further technology development for disaster management; however, this project is still in its conceptual stage. From this perspective this study adopts the principle "*technology should demonstrate robust behaviour that helps achieve reliability*" to outline a business intelligence

technique based DSS model in this chapter that guides a conceptual solution design.

forces

*Strategic planning Timeframe = more than a year* 

Pre-event such as securing budgetary and legislative support for programs

Post-event such as allocation of scarce

regulatory standards

Pre-event as such enforcement of zoning and similar

Tests and exercises

Post event such as administration of disaster relief Determination of priorities of needs

considered in legal of unexpected findings (e.g. geological fault under a nuclear

Post-event such as Decision to relocate

resources Development of zoning and

standards

Pre-event Actions to be

facility)

populace Major recovery expenditures

*Type Operation control* 

Pre-event such as Inspection

Post-event such as Damage assessment Epidemiological surveillance

Pre-event such as warning and alerting Meteorological data

Post event such as Notification of responsible officials Evacuation plan implementation

Pre-event such as Unanticipated personnel

Exacerbating events

Post-event such as equipment malfunction Impacts not foreseen

Table 2. Activities under disaster management scenario

(adapted from Wallace & De Balogh, 1985)

problems

assessment

Inventory of resources

Structured tasks

Structured tasks

Semi structured

Semi structured

Unstructured

Unstructured

tasks

tasks

tasks

tasks

*Timeframe = 6 months* 

Recent research in business intelligence (BI) suggest that the approach can be supportive for any decision support solutions including its underlying architectures, tools, databases, applications, and methodologies (Raisinghani 2004). Negash (2004) provides a definition of BI as follows:

*"combine data gathering, data storage, and knowledge management with analytical tools to present complex internal and competitive information to planners and decision makers"* 

Turban et al. (2008) describes BI's main purposes as enabling interactive and easy access to data, its manipulation and transformation to provide business managers the required decision support. This implies that the approach can add value to decision makers in a disaster management situation where they need to process and transform diverse data. As such, the BI approach is now widely adopted in the world of application design for decision making aid (Watson and Wixom 2007). Drawing from this, this study outlines a BI based conceptual solution in which diverse data are retrieved and manipulated through an analytics filter at the initial stage. Figure 1 illustrates the proposed solution model. The first layer ranges from data sources to knowledge repository; the second from knowledge repository to data fostering; and the third extends from data fostering unit to user group access. At the first layer, the aim is to create meaningful data for storing in the knowledge repository.

Fig. 1. Conceptual solution model of the proposed decision support systems

After storing the data in the knowledge repository, using an ontology model defined in protégé II (Gennari et al. 2003), the data fostering unit (second layer) use BI rules which will be generated from contextual knowledge to transform the meaningful information to different decision makers at the third layer. It is suggested that BI heavily relies on advanced data collection, extraction, and analysis technologies (Watson and Wixom 2007). In the proposed process, business analytics ensures advanced data collections from a range of data sources with context specific detail knowledge. To extract the data, BI rules ensure appropriate contextual information generation of the stored data to improve decision making. In the data fostering unit, (third layer) the user group can select options relevant to their context, for example by defining parameters for the BI rules. During the events of the disaster, the user is able to send and retrieve information through the use of handheld

An Emerging Decision Support Systems Technology for Disastrous Actions Management 109

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devices such as mobile and PDA. The data fostering unit can even transmit urgent message and alerts to mobile phones in the form of SMS.
