I. devices ( 2 pages )- Human factors

I. Abstract ( in progress )Virtual reality (VR) technology has been existed for decades, however, people have not been familiar with this term until recent years. Nowadays it is not limited in universities and laboratories anymore, but day by day influencing in various daily life areas such as entertainment, medical, engineering, communication and so much more. The raise of VR without a doubt has a significant contribution of hardware devices improvement. Giant technology corporations such as Facebook, HTC, Google, Samsung also have big concerns in this field and define virtual reality technology as a strategic step, competitive aspects integrated in their products and services. These days there is a lot of VR devices on the market from different provider with variety of designs, features, versions and support different aspects in VR development. This thesis gives an overview of  problems in VR application development, regarding VR hardware devices and presents a taxonomy for current VR devices as well as an accompanying decision support platform.1 Introduction ( in progress )1.1 Heading of the subchapter1.2 Heading of the subchapter2 State of the art ( in progress )2.1 Virtual Reality ( 1 pages)- What is virtual reality ?  The term virtual reality has been understood a bit differently from people’s point of view in this new technology. However, the finest definition would be from Devendra (2016) “Virtual Reality (VR) can be seen as an artificial environment which is created using hardware and software presented to the user in such a way that they come to believe that it’s a real environment.”- Main elements of virtual reality system ( 4 elements )- VR devices2.2 Human Action Concept ( 2 pages )- The general classification method to construct the taxonomy is by applying the concept of interaction defined by Norman (2002). He introduced the action cycle which describes two aspects of every human task interact with surround world, execution and evaluation. Gabbard (1997) applied this method to generate his taxonomy of usability characteristics in virtual environments.2.3 Human Senses and its relation to VR devices ( 2 pages )- Human factors and how they affect design of VR devices 2.4 Graph database & Decision support Systems ( 3 pages )2.4.1 Graph databaseRelational database has been existed for decades and provided solutions for almost data storing problems. However,  since nowadays business changes frequently and especially the highly demands of connected data, this traditional database shows its limitations. Indeed, relational database defines data model in tabular structure with fixed schema, therefore it takes a lot of effort even with small change in data attributes to adapt with new business requirements. Relational database does not store relationship between data entities, it only connects them by join command using foreign keys. This is painful because when business get complex and the amount of data increases to high volume, the join command becomes expensive and massive to query data that meet requirements, following by high memory consumption and poorly performance.  Graph database overcomes limitations and constraint of relational database, it provides new schema-less data model for storing and handling connected data in which defines data entities as nodes and relationships to link them.  Nodes are grouped by one or more labels that help defining which types a node belong to. Nodes have properties, presented as key-value pairs according to Robinson, Webber, and Eifrem (2015). Properties of a node can be understood as attributes of a table in relational database. Each node can be independent or connected with other nodes by relationships. They define relation between start node and end node, and also contains properties of that relation. Figure 1 present a simple graph data structure, in which users are stored as nodes and connected by relationship “Child of”. Firgure 1. A sample of graph database modelGraph database, for example, Neo4j, provide highlight advantages in storing connected data, which are flexibility and performance. The schema-less construction allows changing and modifying data attributes easily, which is perfectly suitable for agile development where the requirements is updating frequently. Nodes are indexed by relationships, which help data to be accessed incredibly fast by applied graph theory strength, traversal,  and beside remains the performance stable even when data volume increase.One of most popular graph database at the moment, Neo4j, which has been applied in many big corporations such as Walmart, Microsoft, IBM, Ebay and Airbnb. Neo4j provides Cypher, a query language which handles graph database operation. Cypher syntax is designed friendly in a simple expression, however, it is able to handle complicated queries.MATCH (child:person)-:Child of->(parent) WHERE child.name = ‘John’ RETURN child, parentThe above command is one simple example of  Cypher query, applied to graph database in figure 1. It selects all nodes which have type of person and has attribute name as ‘John’, and nodes which have relationship “Child of” with Phillip. The result is presented in table 1.+————————————————————————+| “child” | “parent” +————————————————————————+|{“name”:”John”, |{“name”:”Birgit”,     ||{“from”:”Germany”, |{“from”:”Germany”,     ||{“age”:”24″, |{“age”:”56″,         | Table 1. Result from…2.4.2 Decision support systemDecision support system (DSS) is computer-based system which aims to help user in decision making regarding to a specific problem or to evaluate choices.A DSS contains of three main components as defined by Marek and Roger 2002, which are database management system, model-base management system and dialog generation.- Database management system stores and manage huge amount of relevant data of particular area in a well structure, which provides the sources for model-base management system.- Model-base management system retrieves data from database and present useful information which support the decision making process.- Dialog generation and management system is the primary interface that user interact with decision support system. The interface need to be friendly with easy experience in order to support decision maker interact with the system effectively.DSS can be applied in may fields such as human resources management, finance, marketing, international business, information systems and many other areas in which need the support of decision making. However, no matter which fields a DSS covers, Sprague (1980) list out these characteristics they all have:- They tend to be aimed at the less well structured, underspecified problems that upper level managers typically face.- They attempt to combine the use of modes or analytic techniques with traditional data access and retrieval functions.- They specifically focus on features which make them easy to use by non-computer people in an interactive mode.- They emphasize flexibility and adaptability to accommodate changes in the environment and the decision making approach of the user.Write more about current decision support system, what to improve, what can learn from therehttp://www.sciencedirect.com/science/article/pii/092575359400068Ehttp://ieeexplore.ieee.org/document/253877/http://ieeexplore.ieee.org/abstract/document/324254/2.5 Related WorkThere are various number of research and published books contributed to user interface and human devices interaction in virtual reality. The very recent book from Augkstakalnis (2017) presents the mechanism of human senses and the technologies developed for them accordingly. Augkstakalnis listed three main elements  in which a person interact with surround world, including sight, hearing and feeling.A very good resource covers many detailed aspects of 3D user interfaces from Laviola, Kruijff, McMahan, Bowman and Poupyrev (2017). By introducing human factors and human devices interaction in 3D environment, they presented supported input and output hardware technologies in VR.In Hale and Stanney (2015), completed understandings in virtual environments are given from basic definitions and principles of virtual reality to details of hardware technologies. Eight main areas of VR hardware technologies are described, including vision, auditory, haptic display, olfactory, body motion, eye tracking, gesture recognition and locomotion.Many works have been implemented to categorize and order terms and concepts in different parts of VR. Bowman and Hodges (1999) established a taxonomy of travel techniques in immersive virtual environment and another taxonomy of selection/manipulation. Both taxonomies in order to support generating their design and evaluation framework of VR interaction techniques.An taxonomy constructed by Muhanna (2015), which classifies different virtual reality systems. This taxonomy used two main concepts, technology type and level of mental immersion, focuses on types of completed VR system. Besides the taxonomy, he suggested five key elements that contribute to VR experience, a virtual world, immersion, feedback, interactivity and participants.Gabbard (1997) also proposed a taxonomy of usability characteristics, presented an overview of interaction styles in virtual environments. It followed the basic concept of human-computer interaction proposed by Norman (1990) and apply in virtual environment for classification, as show in figure n. Firgure n. Interaction process in VRJaneková, Janek and Rudy (2014) gave a summary of VR devices, focus on input devices output devices and their application area.Anthes, Hernádez and Wiedemann (2016) recently introduced an overview of current status of VR development and a taxonomy of VR hardware covered many types of VR devices. They also constructed their taxonomy base on the similar concept which Gabbard used on his taxonomy, made their first step of classification by dividing two main types of devices, input and output. The taxonomy presented from their research is a huge inspiration and contribute significantly in implementing this thesis, to expand and categorize all VR devices available on the market in a more appropriate way. In term of decision support system, many books and papers have described methods for development. Druzdzel and Flynn (2002) divide a decision support system into three main components, database management system, model-base management system and dialog generation and management system . Fillip, Zamfirescu and Ciurea (2017) also agree on mentioned point defining a standard general architecture of a decision support system, contains of three sub elements, which are the language subsystem, the presentation subsystem and the knowledge subsystem. Sprague (1980) explain and give understandings of decision support system and its characteristics. A framework to develop a decision support system is introduced and approach for modeling a good architecture platform is also presented.Other sources were retrieved from tech blogs and forums. Information and specification of VR devices are from official websites of producers and many product catalogue websites.References (must in alphabet order)Linkshttps://neo4j.com/why-graph-databases/https://neo4j.com/customers/https://neo4j.com/docs/developer-manual/current/cypher/Literature  8 Adolfo Crespo Marquez and Carol Blanchar. “A decision support system (DSS) for evaluating operationgs investments in high-technology business”, pp 2. 12 Handbook of Virtual environments12 Practical Augmented Reality12 3D user interface : Theory and practice12 State of the Art12 Virtual reality and the Cave: : taxonomy, interaction challenges and research directions12 Handbook of Virtual environments Bibliography1 Alice Redmond-Neal. (2013) Starting A Taxonomy Project. Online. https://www.sla.org/wp-content/uploads/2013/05/StartingTaxProject_Redmond-Neal.pdf2 Aleksa Vukotic and Nicki Watt, “Neo4j in Action,” p. 81, 2015.3 William E Evans and Mary V Relling, “Pharmacogenomics: Translating functional genomics into rational therapeutics,” Science 286.5439, pp. 487-491, 1999.4 Devendra Mishra, “Virtual Reality,” ISSN 2278 – 2540, pp. 50-53, 2016.5 Doug A. Bowman and Larry F. Hodges, “Formalizing the Design, Evaluation, and Application of Interaction Techniques for Immersive Virtual Environments,” Journal of Visual Languages and Computing, pp. 41-42, 1999.6 Rik Van Bruggen, “Learning Neo4j,” Packt Publishing, pp. 34-38, 2014.7 Ian Robinson, Jim Webber, and Email Eifrem, “Graph Databases: New Opportunities For Connected Data,” Neo Technology, pp. 35-58, 2015.8 Neo4j, “Overcoming SQL Strain and SQL Pain – How Utilizing a Graph Database Drives Faster Time to Market, Faster Performance, and Business Agility,” Neo4j White Paper, p. 2, 2015.9 Marek J Druzdzel and Roger R Flynn, “Decision Support Systems,” Encyclopedia of Library and Information Science, pp. 6-7, 2002.10 M.C.Er, “Decision support systems: a summary, problems, and future trends,” Journal, vol. 4, pp. 356-357, 1998.11 Doug A Bowman and Larry F Hodges, “Formalizing the Design, Evaludation, and Application of Interaction Techniques for Immersive Virtual Environments,” Journal of Visual Languages and Computing, pp. 40-48, 1999.12 Ralph H Sprague, “A framework for the development of decision support systems,” MIS Quarterly, vol. 4, no. 4, p. 2, 1980.13 Florin Gheorghe Filip, Constantin-B?l? 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