Calm before the storm: the challenges of cloud computing in digital forensics

Cloud computing is a rapidly evolving information technology (IT) phenomenon. Rather than procure, deploy and manage a physical IT infrastructure to host their software applications, organizations are increasingly deploying their infrastructure into remote, virtualized environments, often hosted and managed by third parties. This development has significant implications for digital forensic investigators, equipment vendors, law enforcement, as well as corporate compliance and audit departments (among others). Much of digital forensic practice assumes careful control and management of IT assets (particularly data storage) during the conduct of an investigation. This paper summarises the key aspects of cloud computing and analyses how established digital forensic procedures will be invalidated in this new environment. Several new research challenges addressing this changing context are also identified and discussed

Towards the realisation of an integratated decision support environment for organisational decision making

Traditional decision support systems are based on the paradigm of a single decision maker working at a stand‐alone computer or terminal who has a specific decision to make with a specific goal in mind. Organizational decision support systems aim to support decision makers at all levels of an organization (from executive, middle management managers to operators), who have a variety of decisions to make, with different priorities, often in a distributed and dynamic environment. Such systems need to be designed and developed with extra functionality to meet the challenges such as collaborative working. This paper proposes an Integrated Decision Support Environment (IDSE) for organizational decision making. The IDSE distinguishes itself from traditional decision support systems in that it can flexibly configure and re‐configure its functions to support various decision applications. IDSE is an open software platform which allows its users to define their own decision processes and choose their own exiting decision tools to be integrated into the platform. The IDSE is designed and developed based on distributed client/server networking, with a multi‐tier integration framework for consistent information exchange and sharing, seamless process co‐ordination and synchronisation, and quick access to packaged and legacy systems. The prototype of the IDSE demonstrates good performance in agile response to fast changing decision situations

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

Computer technologies and institutional memory

NASA programs for manned space flight are in their 27th year. Scientists and engineers who worked continuously on the development of aerospace technology during that period are approaching retirement. The resulting loss to the organization will be considerable. Although this problem is general to the NASA community, the problem was explored in terms of the institutional memory and technical expertise of a single individual in the Man-Systems division. The main domain of the expert was spacecraft lighting, which became the subject area for analysis in these studies. The report starts with an analysis of the cumulative expertise and institutional memory of technical employees of organizations such as NASA. A set of solutions to this problem are examined and found inadequate. Two solutions were investigated at length: hypertext and expert systems. Illustrative examples were provided of hypertext and expert system representation of spacecraft lighting. These computer technologies can be used to ameliorate the problem of the loss of invaluable personnel

Determinants of Managerial Intensity in the Early Years of Organizations

This paper examines how founding conditions shape subsequent organizational evolution— specifically, the proliferation of management and administrative jobs. Analyzing quantitative and qualitative information on a sample of young technology start-ups in California’s Silicon Valley, we examine the enduring imprint of two aspects of firms’ founding conditions: the employment blueprints espoused by founders in creating new enterprises; and the social capital that existed among key early members of the firm—their social composition and social relations. We find that the initial gender mix in start-ups and the blueprint espoused by the founder influence the extent of managerial intensity that develops over time. In particular, firms whose founders espoused a bureaucratic model from the outset subsequently grew more administratively intense than otherwise-similar companies, particularly companies whose founders had initially championed a “commitment” model. Also, firms with a higher representation of women within the first year subsequently were slower to bureaucratize than otherwise-similar firms with a predominance of males. Our analyses thus provide compelling evidence of path-dependence in the evolution of organizational structures and underscore the importance of the “logics of organizing” that founders bring to new enterprises. Implications of these results for organizational theory and research are discussed

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft