Decision Support Systems - Report Example

Decision Support Systems (DSS) Student Number: Date: Contents Introduction 1 1.0 A Decision Support System (DSS) 2 1.1. DSS definition and Description 2 1.2 Classification and Types of DSSs 4 1.3 Evaluation of an appropriate DSS 5 1.4 Applications of DSSs 6 2.0 Wireless Information System for Emergency Response (WISER) 7 2.1 Description of the System 7 2.2 Features and Capabilities of the WISER Software 7 2.3 Platforms to Run the System 9 2.4 How to Download and Install WISER Software on Windows PC 9 3.0 Spatio-Temporal Epidemiological Modeler (STEM) 11 3.1 Description of STEM 11 3.2 Features and Capabilities of STEM Software 11 3.3 Platforms that can run STEM Software 13 4.0 HotSPot Health Physics Codes 14 4.1 Description of the Software 14 4.2 HotSpot Software Tools 16 4.3 HotSpot Radionuclide Library 17 4.4 Platforms that Support HotSpot Software 17 General Conclusions 17 References 18 Appendices 20 Appendix 1: How to Download and Install WISER Software on Windows PC 20 Appendix 2: How to Download and Install STEM Software on Windows PC 24 Appendix 3: How to Download and Install HotSpot Software on Windows PC 28 Introduction I am a student taking a master course in Chemistry and this work provides a general introduction covering four systems/softwares. These are the Decision Support Systems (DSS) and softwares for the prevision and management of Chemical, biological, radiological, nuclear and explosive CBRNe events. The softwares discussed in this paper include Wireless Information System for Emergency Responders (WISER) software, Spatiotemporal Epidemiological Modeler (STEM) software, and Health Physics Codes for the PC (HotSpot). There are scenarios to work with for each of these softwares. A decision support system is an information system that is based on a computer-run program, used to analyze a business or organizational data and assist in making a decision. WISER is a resource that helps emergency responders in incidents involving hazardous materials. STEM is a software that is used by scientists and those who work in the public health sector to develop and use spatial and temporal models of emerging diseases that may be infectious to populations. The HotSpot Health Physics codes provide a quick means for approximating the effects associated with radiation emitted from a radioactive substance. This work covers a general introduction to these four systems, from their description, features and capabilities, their applications, where to find them, platforms and system requirements, and how you can download them, install and launch as a user. 1.0 A Decision Support System (DSS) 1.1. DSS definition and Description A Decision Support System (DSS) is a computerized information system that can support organizational or business decision making activities, resulting in sorting, ranking or making the best choice from alternatives. Most DSSs focus on making a decision from a group of alternatives characterized on attributes or multiple criteria. DSS systems can either be human-powered, fully computerized, or a combination of the two. A properly designed DSS is an interactive software system that can help a decision maker to compile the most useful information from documents, raw data, business models, and/or personal knowledge to identify unstructured and semi-structured problems and find solutions for the problems, and make sound decisions (Janakiraman & Sarukesi, 2008). While academia may perceive DSS as a decision-making support tool, DSS users may see the system as a tool to achieve organizational processes. DSSs provide platforms for data storage and retrieval, enhancing traditional information access functions with support for model development and model-based reasoning. DSSs are typically used for tactical and strategic decisions required of upper level management. The decisions made are characterized by reasonably low frequency consequences but with high potential – generously paying off in the long run for the time taken through the process of thinking and modelling. Thus, they support modeling, framing, and problem solving. A typical DSS gathers and presents the following type of information: Comparative figures of sales between two periods; Information asset inventories (including legacy and relational data sources, data warehouses, cubes, and data marts); Revenue figures projected based on product sales assumptions; The consequences of different alternatives, provided a description of the past experience. Typically, DSSs are applied in areas such as business management and planning, health care, militaries, and in any other area where management is likely to encounter a situation that requires a complex decision-making. There are three important components of DSSs; Data base management system, Model-base management system, and Dialog generation and management system. Database management system (DBMS) – This serves as a data bank for Decision support systems. A DBMS stores data in large quantities – data that is relevant to a particular class of problems for which a a particular DSS has been designed to solve. It provides a logical data structures which are meant to interact with users, separating users from the physical data structures and processing. DBMSs are also capable of informing a user of available types of data and how they can gain access to these types of data (Druzdzel & Flynn, 2002). Model-base management system (MBMS) – An MBMS serves a role that is analogous to a DBMS. The primary function of MBMSs is to provide independence between the models used in a DSS from specific applications that use these models. MBMSs work by transforming data from DBMSs into an information that can be used in making a decision. Given that a DSS user copes with a variety of unstructured problems, it is required of an MBMS to be capable of assisting users in model development (Druzdzel & Flynn, 2002). Datalog generation and management system (DGMS) – Also refered to as user interface, DGMSs provide an insight interaction between DSSs and users. Since DGMS users are mostly untrained managers, DSSs have to be equiped with interfaces that are intuitive and easy to use. The main function of a DGMS is to improve the DSS system user ability to utilize all the benefits of the system. Intuitive interface and easy-to-use interfaces assist in model building, easy intraction with the built model, e.g gaining insight and recommendations (Druzdzel & Flynn, 2002). The components discussed above are found in a typical DSS architecture and have a fundamental role to play in a variety of DSSs. The interaction among these components is illustrated in figure 1. Essentally, a DGMS provides a platform over which a user interacts with the DSS and communicates with both the DBMS and MBMS. These inturn screen the user interface from the physical information provided by the model base and implementation of the database (Manos & Basil, 2010). Figure 1: Architectural diagram of a DSS The outcome of a DSS can be an interpretation, recommendation, or prediction of a situation under analysis, such as water quality, food safety or crop treatment. Some DSSs may become very complex with several interacting components. They can also be real-time and embedded systems, or even off-line systems. 1.2 Classification and Types of DSSs DSSs can be categorized into many different classes. The five common types are the following: i. Data-driven DSS – This type of DSS is targeted at managers, product/service suppliers, and staff. It used to query a data warehouse or database to obtain particular answers for particular purposes. It is usually deployed via a client/server link, main frame system or the web. An example is a computer database with a query system to check. ii. Communication-driven DSS – This type Of DSS is focused on internal teams and partners. Its primary purposes are to aid in conducting a meeting, or help users to collaborate. Communication-driven DSS is deployed using a client server or the web technology. Examples are instant messenger and chat softwares, and online collaboration or meeting systems. iii. Document-driven DSS – Document-driven DSSs target a wide range of user groups. Its primary function is to search the web and find documents on a given set of search terms/keywords. These DSSs are set-up via a client/server system or the web. iv. Knowledge-driven DSS – This type of DSS is a catch-all type that covers a wide range of systems for users in an organizational setting, but it may also include other users who interact with the organization e.g. the consumers. The marketing departments can use this type of DSS to obtain management advice or make a choice for products/services (Janakiraman & Sarukesi, 2008). The technologies used to deploy this DSS include the web, client/server systems and softwares that are installed and run on stand-alone PCs. v. Model-driven DSS – This type of DSSs involve a complex system that aid in analyzing decisions or making a choice between multiple alternatives. They are normally used by staff and managers of an organization, or other people who interact with the organization for several purposes – including decision analysis and scheduling, depending on the set-up of the model. It is deployed via the web, client/server systems, or software/hardware installation in stand-alone PCs (Manos & Basil, 2010). 1.3 Evaluation of an appropriate DSS Design and development of a DSS involves a process that requires a consideration of all business processes that will be undertaken in an organization. Business managers, staff and clients need to be familiar with the DSS in order to be in a position to accurately communicate their needs during the formulation of the system deliverables, capabilities, and the decisions supported by the DSS. To evaluate, design and implement a DSS, the following methodology can be used: a. Strategic focus of the firm – Before a DSS is deployed, the firm wishing to use the DSS should clearly provide its strategic focus in the industry in which it operates. The strategic focus should emphasize on the firm’s objectives and the direction in which it needs to move. This is fundamental in providing information requirements as well as the scalability of the DSS to be designed (Manos & Basil, 2010). b. Creating a tem for DSS evaluation – This team is created using a multi-disciplinary approach – with people from disciplinary backgrounds such as accounting, networking, communications, database etc. The purpose of this team is to do initial analysis and provide a blueprint for the rest of project design and development. Optimal performance of such a team is achieved through team performance and group dynamics aspects. c. Defining requirements – The more the design team understands about categories of DSS, the better position they are in getting fine-tuned specifications of the DSS. This is where a multi-disciplinary team and stakeholders involved in the implementation play an important role. The design team should engage all people who will be affected by the DSS to help them cover all the needs of the users. d. System design and product evaluation – This process involves clearly specifying deliverables, a preliminary evaluation of available alternatives and vendors. It also includes functional screening, performance evaluation as well as a detailed review based on the specifications identified. Most organizations design and adopt a DSS that can best suits their needs (Druzdzel & Flynn, 2002). e. Negotiation with vendors – Vendors have to be reviewed in terms of product/service offerings, financial stability, reputation and experience in similar systems. Other issues that need to be clarified here include: price of package, timeframe of deliverables etc.  1.4 Applications of DSSs As world technology continues to advance, analysis of data is no longer restricted to large mainframes. A DSS application can be loaded on most PCs, mobile devices and other computer systems. The flexibility of this application has become very beneficial for users who travel frequently. It provides them with opportunity to stay informed all times. This in turn puts them in a position to make the most appropriate decisions for their organizations or businesses. Clinical DSSs are useful in medical diagnosis. Another growing area where DSSs find their applications is in agricultural production and marketing for sustainable development. DSSs are also used in forest management programmes where problems of long planning and spatial dimension of planning need specific requirements. 2.0 Wireless Information System for Emergency Response (WISER) 2.1 Description of the System Wireless Information System for Emergency Response (WISER) is a system designed by the United States National Library of Medicine (NLM) to help first responders in incidents involving hazardous material. The system provides a wide range of information on hazardous materials, including material identification support, human health information, physical characteristics, and containment and suppression advice. At first, the system iteration provided users with a database of 44 hazardous chemical substances in a hand-held device. Currently, the system provides a greater functionality with a database of dangerous chemicals exceeding 400. Both functionality and database items are always increasing over time (U.S. National Library of Medicine, 2017). 2.2 Features and Capabilities of the WISER Software WISER encounters the most number of entries from the Hazardous Substance Data Bank (HSDB). The system simplifies the data received with an interface to aid in identifying unknown substances. Different means are available for searching the identity of known substances. These include CAS number, UN number, and Science Transportation Commodity Code (STCC) number. WISER provides health effects of a substance, as well as an overview of occupational exposure levels and treatments. The system serves an emergency responder without networking or reach-back requirement. It also provides other additional tools such as Weapon of Mass Destruction Guidebook and Emergency Response Guidebook (ERG) (Lawrence Livermore Natinal Laboratory, 2016). The lack of reliance on a network connectivity is a key feature of this software. The WISER system was designed to function independent of external communications that may break down or be temporarily down during a major crisis. Another important feature is the Emergency Response Guidebook from its Transportation Department. This guidebook is important when it comes to providing vital information that comes in handy on protective measures required for a wide range of hazardous materials. The NLM has been able to integrate the information from the latest versions of ERG into WISER (Noll, et al., 2012). The NLM has been able to tap throughout the government database, allowing WISER to work beyond its initial subject matter. As a result of recent input from emergency responders and other stakeholders, the NLM has been able to add more detailed and peer-reviewed biological and radiological substances. All the information for the added substances was provided by non-HSDB databases. For example, the Centers for Disease Control (CDC) and Prevention Category A substance list provided the biological information for biological agents. This list of biological substances are considered by CDC to be the most hazardous pathogens – such as plague, viral hemorrhagic fevers, anthrax and small pox (Ackerman, 2008). The addition of the GIS and mobile supports on WISER provides for protective distance overlays within the map of an incident and providing responders with the required information when and where it is needed. NLM experts translated textual information related to the hazardous substances from other databases since these databases were designed for experts (toxicologists) and it was important to translate it into a format that would be understood by a range of WISER users. The outcome is that the system users can not only check a wide range of information on a given hazardous substance, but can also query the WISER database to assist in identifying an unknown substance. For example, in a case where a loaded track overturns and spills its contents and the driver is unconscious, an emergency responder can be able to determine the identity of the chemical spill by entering information about the properties (such as its state, color, odor and pH-level) of the spilled substance into the system. At the end, the system user will have a short list of suspect substances, or even get a specific identification. The system then provides a description of hazards associated with the identified substance and warns against action – such as use of firefighting materials or chemical detergents that could make the situation turn to worse (Noll, et al., 2012). WISER also provides its users with a means of determining if one has been exposed to hazardous materials by listing the symptoms likely to be observed in people exposed to a particular hazardous substance. This is important for emergency medical services. Variables such as neurological states, skin condition, respiratory or cardiovascular rates and body temperature form part of the list of variables that the system incorporates to establish the hazardous materials a patient has been exposed to and provide a recommendation for appropriate emergency treatment (Ackerman, 2008). WISER can provide vast information about a substance if the emergency responder is in a position to identify the substance at the scene – ranging from facts and requirements to health effects and treatment for exposure. User profiles allow WISER users to specify their role at the scene of an event as: first responder, EMS specialist, HAZMAT specialist, preparedness planner or hospital provider. The user interface is customized so that there is easy access to relevant information based on the role played by the application user. Experts at the NLM continuously strive to upgrade WISER, with developments having made the software compatible with smartphones. There is also the Pocket PC version that uses a stylus to navigate through the system. A smart phone user is able to navigate through the WISER menu using the phone’s existing buttons. Future plans for WISER and its developers is to add more hazardous substances to the system’s database. The team is also striving to ensure that emergency responders get a greater assistance in getting the identity of unknown substances. The NLM researchers are also adding artificial intelligence to the system that would learn from the data input in a material identity search. Thus, steering the WISER user toward offering the best information in a faster way (Noll, et al., 2012). 2.3 Platforms to Run the System WISER is downloadable on Microsoft Windows PCs and smart mobile devices, as well as on Palm OS PDAs. WebWISER is a browser-based application for both PC and PDA based browsers. 2.4 How to Download and Install WISER Software on Windows PC Hardware requirements for the Software: The PC needs to be run on Windows XP Service Pack 2 or later. RAM: At least 128 MB. Hard disk space: At least 22 MB of free hard disk space is required. If Microsoft.NET Framework is to be installed, a grater hard disk space will be required. To download and install the system, the steps described in appendix 1 are followed. 3.0 Spatio-Temporal Epidemiological Modeler (STEM) 3.1 Description of STEM The Spatiotemporal Epidemiological Modeler (STEM) is a software that was designed to assist scientists and public health officials develop and use spatial and temporal models of any emerging infectious diseases. This tool was originally released by IBM Research and available under the Eclipse Foundation. The models created can help in understanding and preventing the spread of infectious diseases. STEM relies on mathematical models based on different equations to simulate the process of development and evolution of an infection in space and time. The software utilizes a component software architecture that is based on the OSGi standard. OSGi is a Java framework used to develop and deploy modular software programs as well as libraries (The Eclipse Foundation, 2017). The Eclipse Equinox is an implementation platform for the OSGi standard. The use of a component software architecture ensures that all the elements needed for modeling a disease – including the data and codes are available as independent software building extensions or blocks that can be exchanged freely (shared, reused, replaced or extended). These building plug-ins or blocks are referred to as eclipse “plug-ins” and contain global/denominator data that is used for administrative regions that may be have interest. Standard codes are used to index the regions of interest. The global data contained in the plug-ins include population data, demographics, transport data, geographic data, and also basic disease models. These data includes populations, common borders, road networks, airport links, rainfall and temperature (The Eclipse Foundation, 2017). 3.2 Features and Capabilities of STEM Software The software comes preconfigured with a large amount of denominator data for the whole world. Through using STEM and extending its software data and models, it becomes possible for one to prototype or create new models and test them more rapidly for emerging infectious diseases. The software also provides its users with tools to assist them compare and validate their models. The framework used to build the models provide a simple graphical user interface and has the capability of automatically generating model codes that are hot-injected into STEM during runtime. It does not require a user to have knowledge of Java or Eclipse. The software’s code generator allows its users to develop models affected by variations in climate data (The Eclipse Foundation, 2017). The goal of STEM as an open source project is to provide support and encouragement to communities of scientists who use the software as a tool for developing simulations of infectious disease spread, and also contribute back to the project. By providing descriptive metadata as plug-ins or blocks, STEM make it possible for new collaboration avenues. For example, scientists involved in the study of bird migrations can contribute data that can be of significant use to epidemiologists involved in the study of avian influenza. On the other hand, economics involved in the study of workforce productivity may contribute data to STEM that may turn out to be useful to public health officials involved in the study of impacts of pandemic influenza on the economy. Another important feature of STEM is its ability to perform simulation for intervention strategies such as mass vaccinations and school closures. This is important for epidemiologists as it allows them to propose solutions for containing the infectious disease to the concerned authorities (Chen, et al., 2010). As an application built on Eclipse, STEM also supports a collaborative community contribution to an expanding data library needed to model both existing and emerging infectious diseases. STEM has recently extended its support for modeling foodborne, zoonotic and vector borne disease. Disease models that come with the software include epidemiological compartment models. Making a common collaborative platform available and providing extensible, re-usable and flexible components makes STEM to have a greater understanding of phenomenal features affecting public health, potentially impacting on the environment, social and economic life (Chen, et al., 2010). By simply switching between solver plug-ins, STEM model can either be run deterministically or stochastically. Users can make a choice between different numerical solvers of ordinary differential equations (ODE) provided by STEM. The results of a simulation can be output using pluggable bloggers, map bloggers and video bloggers (The Eclipse Foundation, 2017). STEM can be applied in the study of very complex models and has the capability to run global scale simulations. Whether an epidemic involves animals or humans, STEM can be used to simulate the situation. However, it is only restricted to a physical spatial model and is therefore, cannot be used to simulate socio-technological epidemics. This is because STEM does not have a support for developing individual logical networks for transmitting information. 3.3 Platforms that can run STEM Software STEM can be run on Windows, Mac and Linux. For Windows, the 32-bit version of STEM can run on both 64-bit and 32-bit OS. Please see appendix 2 on how to download and install STEM software on Windows PC. 4.0 HotSPot Health Physics Codes 4.1 Description of the Software The HotSpot Health Physics codes program was developed by Lawrence Livermore National Laboratory (LLNL) to provide Health Physics emergency response personnel and other emergency planners with a portable, fast calculational set of tools that can be used to evaluate accidents involving radioactive substances. This software tool is also useful in carrying out safety analysis of the U.S Department of Energy (DOE) facilities that deal with nuclear material. HotSpot was developed following the Gaussian Plume Model. HotSpot codes are designed to provide a 1st order approximation of effects of radiation that is associated with the release of radioactive materials into the atmosphere. The atmospheric dispersion models for HotSpot are designed for short term release durations ( Read More

There are three important components of DSSs; Database management system, Model-based management system, and Dialog generation and management system.
Database management system (DBMS) – This serves as a data bank for Decision support systems. A DBMS stores data in large quantities – data that is relevant to a particular class of problems for which a particular DSS has been designed to solve. It provides logical data structures which are meant to interact with users, separating users from the physical data structures and processing. DBMSs are also capable of informing a user of available types of data and how they can gain access to these types of data (Druzdzel & Flynn, 2002).
Model-base management system (MBMS) – An MBMS serves a role that is analogous to a DBMS. The primary function of MBMSs is to provide independence between the models used in a DSS from specific applications that use these models. MBMSs work by transforming data from DBMSs into information that can be used in making a decision. Given that a DSS user copes with a variety of unstructured problems, it is required of an MBMS to be capable of assisting users in model development (Druzdzel & Flynn, 2002).
Datalog generation and management system (DGMS) – Also referred to as user interface, DGMSs provide an insight interaction between DSSs and users. Since DGMS users are mostly untrained managers, DSSs have to be equipped with interfaces that are intuitive and easy to use. The main function of a DGMS is to improve the DSS system user's ability to utilize all the benefits of the system. Intuitive interface and easy-to-use interfaces assist in model building, easy interaction with the built model, e.g gaining insight and recommendations (Druzdzel & Flynn, 2002).

The components discussed above are found in a typical DSS architecture and have a fundamental role to play in a variety of DSSs. The interaction among these components is illustrated in figure 1. Essentially, a DGMS provides a platform over which a user interacts with the DSS and communicates with both the DBMS and MBMS. These in turn screen the user interface from the physical information provided by the model base and implementation of the database (Manos & Basil, 2010).

The outcome of a DSS can be an interpretation, recommendation, or prediction of a situation under analysis, such as water quality, food safety, or crop treatment. Some DSSs may become very complex with several interacting components. They can also be real-time and embedded systems or even offline systems.
Data-driven DSS – This type of DSS is targeted at managers, product/service suppliers, and staff. It is used to query a data warehouse or database to obtain particular answers for particular purposes. It is usually deployed via a client/server link, mainframe system, or the web. An example is a computer database with a query system to check.
Communication-driven DSS – This type Of DSS is focused on internal teams and partners. Its primary purposes are to aid in conducting a meeting or help users to collaborate. Communication-driven DSS is deployed using a client-server or web technology. Examples are instant messenger and chat software, and online collaboration or meeting systems.
Document-driven DSS – Document-driven DSSs target a wide range of user groups. Its primary function is to search the web and find documents on a given set of search terms/keywords. These dresses are set up via a client/server system or the web.
Knowledge-driven DSS – This type of DSS is a catch-all type that covers a wide range of systems for users in an organizational setting, but it may also include other users who interact with the organization e.g. the consumers. The marketing departments can use this type of DSS to obtain management advice or make a choice for products/services (Janakiraman & Sarukesi, 2008). The technologies used to deploy this DSS include the web, client/server systems, and software that are installed and run on stand-alone PCs.

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