**2. Problem background**

Traditional DSS models have had limited success supporting decision makers in their problem specific situations for disaster management. Specially, for enabling decision makers' context

An Emerging Decision Support Systems Technology for Disastrous Actions Management 103

(Louarn, 2007). This paradigm supports development of emerging technologies for knowledge interactions between software solutions and users (Miah, 2009). Under this framework, an appropriate perspective of socio-materiality is identified for solution design; in which decision maker's social and material characteristics (e.g. organisational policy, rules and management principles) within the changing context can be captured analytically. The fundamental nature of the socio-material practice perspective is important to capture, including themutually constituted view of the stakeholders/decision makers in their own work context (Wagner, Newell and Piccoli, 2010). In this sense, the DSS structures and internal processes for examples the analytics rules and routines associated with a best practice configuration can be outlined as different decision makers draw upon in their situated practices (Wagner et al. 2010; Orlikowski and Scott, 2008). Given the focus of this study upon understanding how the material aspects of technology are entangled with the social for obtaining appropriate decision making practices, this perspective is grounded in the notion that what people do is always locally defined and emergent (Orlikowski, 2000). This view offers potential benefits for such a BI technique oriented DSS solution design that

DSS based applications are well- known and extensively applied in disaster management. Various technologies are used for instances, in natural disaster management, such as flood control and forescasting, hazard management and pre and post activities management.

There are many technologies that have been used in DSS application development for the purpose of disaster situation management. Bessis, Asimakopoulou and Xhafa (2011) highlighted potential use of next generation emerging technologies in managing disaster situations. The technologies are namely: *grid computing* (an information sharing solution that allows integrated, collaborative use of distributed computing resources including servers (nodes), networks, databases, and scientific tools managed by multiple organisations); *web services* (an information sharing infrastructure to provide stateless, persistent services and resolves distributed computing issues); *cloud computing* (a service solution that is defined as incorporating virtualisation and utility computing notions as a type of parallel and distributed system); *pervasive computing* (also known as ubiquitous computing, a means to enable resource computation and utilisation in a mobile or environmentally-embedded manner); and *crowd computing* (a service platform and sometimes called 'crowd-sourced' mobile phone data from citizens) (Bessis et al. 2011).Our findings suggest that there are three main approaches that are used for decision support design in different purpose specific disaster managements, namely:

integrated dynamic model based, GIS based and cloud computing based approaches.

Asghar et al. (2006) described a dynamic integrated model for disaster management from their previous study on a single DSS design. The model handles specific decision-making needs through the use of combining more than one model. Literature in Asghar et al. (2006) suggested that different DSS systems are designed for various categories of disasters and most of the time DSSs are based on specific models and decision support needs. However, there are different needs of decision support in disaster management in that one single model may not be sufficient to cope with the problem situations (Asghar et al. 2006). Asghar's et al integrated model is decomposed into modular subroutines that are

will adapt to various policy and decision making context.

Table 1 illustrates various DSS design applications in previous studies.

**3. Emerging technologies of disaster DSS** 

functionally independent in the problem space.

specific reflection, a decision support framework is required for different stakeholders. Technological provisions for on-site or remote access solution concepts are not new in the use of DSS applications. Although some existing DSS solutions integrate GIS provisions, they lack appropriate process that are required to address concurrent data capturing for decision making and relevant decision sharing in users' context. Buzolic, Mladineo, and Knezic (2002) developed a DSS solution for disaster communications through decision processes in the phases of preparation, prevention and planning of a protection system from natural and other catastrophes, as well as in phases throughout interventions during an emergency situation in the telecommunications part. The DSS model is based on a GIS that only capture location specific necessary data. Using a combination of GIS and multi-criteria DSS approach, according to natural catastrophes such as floods, earthquakes, storms, and bushfires, data regarding the vulnerability of the telecommunications system are generated (Buzolic et al. 2002) in the system model. Similarly, Mirfenderesk (2009) described a DSS designed for mainly to assist in a post-disaster situation. However, this DSS model is used traditional technique for pre-disaster flood emergency planning. As a post-disaster measure it can identify vulnerable populations and assist in the evacuation of those at risk (Mirfenderesk, 2009). Rodríguez, Vitoriano and Montero (2009) developed a DSS for aiding the Humanitarian NGOs concerning the response to natural disasters. This DSS has been developed avoiding sophisticated methodologies that may exceed the infrastructural requirements and constraints of emergency management by NGOs. The used method is not technically novel enough to capture decision makers' context. A relatively simple two-level knowledge methodology is utilised in this approach that allows damage assessment of multiple disaster scenarios (Rodríguez et al. 2009). Apart from these DSS approaches, there some disaster management solution have been developed such as MIKE 11 (Designed by Danish Hydraulic Institute) (Kjelds and Müller, 2008) and SoKNOS (Service-Oriented ArchiteCtures Supporting Networks of Public Security- a service oriented architecture based solution design by the German federal government) (SoKNOS, 2011), but they are still in the embryonic or conception stage of design. None addresses the technical requirements of using business intelligence techniques. Therefore issues remain in designing a relevant decision process with support for policy making and operational levels for disaster impact assessments to meet emergency requirements for the safety of humans and other living beings in emergencies.

Drawing upon the above, in the proposed DSS solution, the architecture comprises several technological layers. The first layer collects disaster information from a range of different sensors or sources. This collected information is then processed through a business analytics method (Davenport and Harris, 2007) before storage in knowledge repositories. In the second layer, analysed results with context details are stored in knowledge repositories with their enhanced semantics. The *Protégé* based knowledge framework (Gennari et al. 2003) is employed for knowledge codifications through a controlled vocabulary principle. Finally, the third layer (called data fostering unit) handles the codified knowledge through different decision making policies or rules (for example, rules based decision making- Miah, Kerr and Gammack, 2009) in various decision making levels. Figure 1 illustrates more detail descriptions.

The digital eco-systems research paradigm provides a powerful and broad methodological foundation for such a DSS solution design for addressing the information needs of different stakeholders. A digital ecosystem enables a platform that facilitates self organizing digital system solutions aimed at creating a digital environment for stakeholders in organizations (Louarn, 2007). This paradigm supports development of emerging technologies for knowledge interactions between software solutions and users (Miah, 2009). Under this framework, an appropriate perspective of socio-materiality is identified for solution design; in which decision maker's social and material characteristics (e.g. organisational policy, rules and management principles) within the changing context can be captured analytically. The fundamental nature of the socio-material practice perspective is important to capture, including themutually constituted view of the stakeholders/decision makers in their own work context (Wagner, Newell and Piccoli, 2010). In this sense, the DSS structures and internal processes for examples the analytics rules and routines associated with a best practice configuration can be outlined as different decision makers draw upon in their situated practices (Wagner et al. 2010; Orlikowski and Scott, 2008). Given the focus of this study upon understanding how the material aspects of technology are entangled with the social for obtaining appropriate decision making practices, this perspective is grounded in the notion that what people do is always locally defined and emergent (Orlikowski, 2000). This view offers potential benefits for such a BI technique oriented DSS solution design that will adapt to various policy and decision making context.
