A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> A review of prior, relevant literature is an essential feature of any academic project. An effective review creates a firm foundation for advancing knowledge. It facilitates theory development, closes areas where a plethora of research exists, and uncovers areas where research is needed. <s> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> The proliferation of mobile internet enabled devices is creating an extraordinary opportunity for a new mode of e-commerce. In Japan, in April 2003, there were more than 62 million users of mobile internet services. Mobile devices, typically used by a single person, provide an unprecedented platform for individualised services. Such services may build on the value propositions of time, place and individual context. One emerging area of mobile business is banking. This paper explores the state of the art of mobile (m-) banking in Japan. A brief discussion about the main characteristics of Japanese banking practices is accompanied by an overview of this country's mobile market. This is followed by a detailed analysis of the mobile internet services of three major Japanese banks Mizuho, Sumitomo Mitsui and UFJ and the development of a strategic framework for m-banking. The paper concludes with a discussion about the future of m-banking. <s> BIB002 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> The convergence of mobile communications and distributed networked computing has provided the foundation for the development of a new channel of electronic business: mobile business. Research into mobile business has begun to grow significantly over the last five years to the point where we now see dedicated journals and conferences focusing on this topic. This paper provides an assessment of the state of mobile business research. Drawing on over 230 research papers from key research outlets, we describe the emergence of this research area and characterize its current status. We also provide a critique of current research and some recommendations for future research into mobile business. <s> BIB003 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> BACKGROUND: A software engineering systematic map is a defined method to build a classification scheme and structure a software engineering field of interest. The analysis of results focuses on frequencies of publications for categories within the scheme. Thereby, the coverage of the research field can be determined. Different facets of the scheme can also be combined to answer more specific research questions. ::: ::: OBJECTIVE: We describe how to conduct a systematic mapping study in software engineering and provide guidelines. We also compare systematic maps and systematic reviews to clarify how to chose between them. This comparison leads to a set of guidelines for systematic maps. ::: ::: METHOD: We have defined a systematic mapping process and applied it to complete a systematic mapping study. Furthermore, we compare systematic maps with systematic reviews by systematically analyzing existing systematic reviews. ::: ::: RESULTS: We describe a process for software engineering systematic mapping studies and compare it to systematic reviews. Based on this, guidelines for conducting systematic maps are defined. ::: ::: CONCLUSIONS: Systematic maps and reviews are different in terms of goals, breadth, validity issues and implications. Thus, they should be used complementarily and require different methods (e.g., for analysis). <s> BIB004 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003. <s> BIB005 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization <s> BIB006 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> In this editorial, the co-editors-in-chief undertake a number of tasks related to International Journal of Management Reviews (IJMR). They begin by reviewing the objectives set out by Macpherson and Jones in their 2010 editorial (IJMR, 12, pp. 107–113). The benefits of publishing in IJMR for scholars at various stages of their careers are then discussed. The section outlining the progress of IJMR over the last four years sets out the main reasons why so many papers are desk rejected by the co-editors. The main criteria for writing an analytical literature review of the type that the editors aspire to publish in the Journal are then discussed. The objectives are not simply to reduce the number of desk rejects, but also to encourage authors to be more ambitious and innovative in their approaches to reviews of the literature. <s> BIB007 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Data Mapping <s> The inflection point in the development of some core technologies enabled the Autonomous Vehicles (AV). The unprecedented growth rate in Artificial Intelligence (AI) and Machine Learning (ML) capabilities, focusing only on AVs, is expected to shift the transportation paradigm and bring relevant benefits to the society, such as accidents reduction. However, recent AVs accidents resulted in life losses. This paper presents a viewpoint discussion based on findings from a preliminary exploratory literature review. It was identified an important misalignment between AI and Safety research communities regarding the impact of AI on the safety risks in AV. This paper promotes this discussion, raises concerns on the potential consequences and suggests research topics to reduce the differences between AI and system safety mindsets. <s> BIB008
The data mapping from the selected papers were executed after they had been completely reviewed and scrutinized. It was performed categorizing the 59 sample papers into 6 categories (CT. BIB008 BIB003 BIB007 BIB001  BIB004 to answer the 6 research questions (RQ.1-6), respectively. The categories defined were based on the corresponding research question: (CT.1) Impact, (CT.2) Topics, (CT.3) Techniques, (CT.4) Problem, (CT.5) Findings and (CT.6) Future Studies. The categorization process was based on the agreement of researchers working in this study. Different strategies were used to create the codes for each of the categories. For (CT.1) Impact, the code increase was used when the paper described AI as a factor of increasing the safety risk (negative impact on safety) and the code decrease was used when the paper presented AI as a factor of decreasing the safety risk (positive impact on safety). For (CT.2) Topics, (CT.5) Findings and (CT.6) Future Studies, the codifications were derived by the context domain of the study according to what was reported by their authors, as suggested by BIB002 . Lastly, for (CT.3) Techniques and (CT.4) Problem, similarly to other categories, the codes were based on what was reported by their authors BIB002 and, due to the wide range of techniques, subfields and misuses of terms, the terminologies were adapted and normalized according to field references  [13] BIB005 [15]  BIB006 .