A framework for the forensic investigation of unstructured email relationship data

Our continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. Moreover, email investigations may involve many hundreds of actors and thousands of messages. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation

Measuring and improving Agile Processes in a small-size software development company

Context: Agile software development has become commonplace in software development companies due to the numerous benefits it provides. However, conducting Agile projects is demanding in Small and Medium Enterprises (SMEs), because projects start and end quickly, but still have to fulfil customers' quality requirements. Objective: This paper aims at reporting a practical experience on the use of metrics related to the software development process as a means supporting SMEs in the development of software following an Agile methodology. Method: We followed Action-Research principles in a Polish small-size software development company. We developed and executed a study protocol suited to the needs of the company, using a pilot case. Results: A catalogue of Agile development process metrics practically validated in the context of a small-size software development company, adopted by the company in their Agile projects. Conclusions: Practitioners may adopt these metrics in their Agile projects, especially if working in an SME, and customise them to their own needs and tools. Academics may use the findings as a baseline for new research work, including new empirical studies.The authors would like to thank all the members of the QRapids H2020 project consortium.Peer ReviewedPostprint (published version

A Model-driven Visual Analytic Framework for Local Pattern Analysis

The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the process of discovering useful information is becoming more challenging nowadays due to the growing data scale. Particularly, the human cognitive capabilities remain constant whereas the scale and complexity of data are not. Meanwhile, visual analytics largely relies on human analytic in the loop which imposes challenge to traditional human-driven workflow. It is almost impossible to show every aspect of details to the user while diving into local region of the data to explain phenomenons hidden in the data. For example, while exploring the data subsets, it is always important to determine which partitions of data contain more important information. Also, determining the subset of features is vital before further doing other analysis. Furthermore, modeling on these subsets of data locally can yield great finding but also introduces bias. In this work, a model driven visual analytic framework is proposed to help identify interesting local patterns from the above three aspects. This dissertation work aims to tackle these subproblems in the following three topics: model-driven data exploration, model-driven feature analysis and local model diagnosis. First, the model-driven data exploration focus on the problem of modeling subset of data to identify the co-movement of time-series data within certain subset time partitions, which is an important application in a number of domains such as medical science, finance, business and engineering. Second, the model-driven feature analysis is to discover the important subset of interesting features while analyzing local feature similarities. Within the financial risk dataset collected by domain expert, we discover that the feature correlation among different data partitions (i.e., small and large companies) are very different. Third, local model diagnosis provides a tool to identify interesting local regression models at local regions of the data space which makes it possible for the analysts to model the whole data space with a set of local models while knowing the strength and weakness of them. The three tools provide an integrated solution for identifying interesting patterns within local subsets of data

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Patent Overlay Mapping: Visualizing Technological Distance

This paper presents a new global patent map that represents all technological categories and a method to locate patent data of individual organizations and technological fields on the global map. This overlay map technique may support competitive intelligence and policy decision making. The global patent map is based on similarities in citing-to-cited relationships between categories of the International Patent Classification (IPC) of European Patent Office (EPO) patents from 2000 to 2006. This patent data set, extracted from the PATSTAT database, includes 760,000 patent records in 466 IPC-based categories. We compare the global patent maps derived from this categorization to related efforts of other global patent maps. The paper overlays the nanotechnology-related patenting activities of two companies and two different nanotechnology subfields on the global patent map. The exercise shows the potential of patent overlay maps to visualize technological areas and potentially support decision making. Furthermore, this study shows that IPC categories that are similar to one another based on citing-to-cited patterns (and thus close in the global patent map) are not necessarily in the same hierarchical IPC branch, thereby revealing new relationships between technologies that are classified as pertaining to different (and sometimes distant) subject areas in the IPC scheme.We thank Kevin Boyack, Loet Leydesdorff, and Antoine Schoen for open and fruitful discussions about this paper. This research was undertaken largely at Georgia Tech drawing on support from the U.S. National Science Foundation (NSF) through the Center for Nanotechnology in Society (Arizona State University; Award No. 0531194); and NSF Award No. 1064146 ("Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight"). Part of this research was also undertaken in collaboration with the Center for Nanotechnology in Society, University of California Santa Barbara (NSF Awards No. 0938099 and No. 0531184). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.Kay L.; Newman, N.; Youtie, J.; Porter A.L.; Rafols García, I. (2014). Patent Overlay Mapping: Visualizing Technological Distance. Journal of the American Society for Information Science and Technology. 65(12):2432-2443. doi:10.1002/asi.23146S243224436512Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M. A., & Balakireva, L. (2009). Clickstream Data Yields High-Resolution Maps of Science. PLoS ONE, 4(3), e4803. doi:10.1371/journal.pone.0004803Boyack, K. W., Börner, K., & Klavans, R. (2008). Mapping the structure and evolution of chemistry research. Scientometrics, 79(1), 45-60. doi:10.1007/s11192-009-0403-5Boyack, K. W., & Klavans, R. (2008). Measuring science–technology interaction using rare inventor–author names. Journal of Informetrics, 2(3), 173-182. doi:10.1016/j.joi.2008.03.001Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351-374. doi:10.1007/s11192-005-0255-6Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69-87. doi:10.1016/s0048-7333(02)00004-5Chen, C. (2003). Mapping Scientific Frontiers: The Quest for Knowledge Visualization. doi:10.1007/978-1-4471-0051-5Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from National Systems and «Mode 2» to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109-123. doi:10.1016/s0048-7333(99)00055-4Franz , J.S. 2009 Constructing technological distances from US patent dataHinze , S. Reiss , T. Schmoch , U. 1997 Statistical analysis on the distance between fields of technology http://www.isi.fraunhofer.de/isi-media/docs/isi-publ/1997/isi97b81/technology-fields-diastance.pdf?WSESSIONID=5712ff2ca5ffcf0d9590afc8ef7e1486Janssens, F., Zhang, L., Moor, B. D., & Glänzel, W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing & Management, 45(6), 683-702. doi:10.1016/j.ipm.2009.06.003Kauffman, S., Lobo, J., & Macready, W. G. (2000). Optimal search on a technology landscape. Journal of Economic Behavior & Organization, 43(2), 141-166. doi:10.1016/s0167-2681(00)00114-1Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60(3), 455-476. doi:10.1002/asi.20991Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348-362. doi:10.1002/asi.20967Moya-Anegón, Sci. G. F. de, Vargas-Quesada, B., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., Munoz-Fernández, F. J., & Herrero-Solana, V. (2007). Visualizing the marrow of science. Journal of the American Society for Information Science and Technology, 58(14), 2167-2179. doi:10.1002/asi.20683Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Munoz-Fernández, F. J. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129-145. doi:10.1023/b:scie.0000037368.31217.34Porter, A. L., & Youtie, J. (2009). Where does nanotechnology belong in the map of science? Nature Nanotechnology, 4(9), 534-536. doi:10.1038/nnano.2009.207Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823-1835. doi:10.1002/asi.21086Rafols, I., & Meyer, M. (2009). Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263-287. doi:10.1007/s11192-009-0041-yRafols, I., Porter, A. L., & Leydesdorff, L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871-1887. doi:10.1002/asi.21368Rosvall, M., & Bergstrom, C. T. (2010). Mapping Change in Large Networks. PLoS ONE, 5(1), e8694. doi:10.1371/journal.pone.0008694Schoen , A. Villard , L. Laurens , P. Cointet , J. Heimeriks , G. Alkemade , F. 2012 The network structure of technological developments: Technological distance as a walk on the technology mapSmall, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. doi:10.1002/asi.4630240406Van den Besselaar, P., & Leydesdorff, L. (1996). Mapping change in scientific specialties: A scientometric reconstruction of the development of artificial intelligence. Journal of the American Society for Information Science, 47(6), 415-436. doi:10.1002/(sici)1097-4571(199606)47:63.0.co;2-yWaltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378-2392. doi:10.1002/asi.2274

How to get away with technical debt: An explorative multiple-case study on autonomous teams and technical debt management

Technical debt (TD) is constantly accumulating throughout software development processes. In many autonomous teams this technical debt will damage and injure the process, prohibiting them from adding new functionalities to their products. Tech companies must therefore understand how they can manage TD to avoid getting stuck fixing bad code. In the research on technical debt management (TDM), there seems to be a lack of empirical studies that examine how TD is managed in autonomous teams. Some frameworks are developed with the purpose of investigating TDM but lack the empirical validation and reliability. This study investigates how autonomous teams actively manage technical debt, by conducting a multiple-case study in a Norwegian fintech company. The teams are studied by utilizing the TDM framework, measuring autonomous teams’ degree of maturity within different TDM activities in order to understand their current state of practice and how to further improve these. The study found that all autonomous teams practiced TDM, but to various extents. Some teams had structured processes, while others had no clear strategies. Most of the teams were ranked with what the framework call “received level of maturity”, and conducted TDM activities occasionally based on their current needs. The study also found challenges related to the TDM frameworks maturity levels relation to TDM success, and identified that TDM activities ranked as highly mature did not necessarily translate into higher TDM success. The study identified a need for the TDM framework to be further empirically tested and iterated on for it to work as a an accurate tool for understanding and improving autonomous teams’ TDM processes. Keywords: agile software development, autonomous teams, technical debt, technical debt management, case stud

Perception is Reality: Change Leadership and Work Engagement

Purpose The purpose of this paper is to investigate how employee perceptions of change and leadership might impact work engagement following major organizational change. Design/methodology/approach Social media invited US workers recently experiencing major organizational change to anonymously complete a web-based survey requesting qualitative and quantitative responses. Values-based coding and thematic analysis were used to explore qualitative data. Hierarchical and linear regression, and bootstrapped mediation were used to analyze quantitative data. Findings Analysis of qualitative data identified employees’ perceptions of ideal change and ideal leadership were well supported in the change leadership literature. Analysis of quantitative data indicated that employee perceptions of leadership fully mediated the relationship between employee perceptions of change and work engagement. Practical implications Study findings imply that how employees perceive change is explained by how they perceive leadership during change, and that these perceptions impact work engagement. Although these findings appear commonsensical, the less than stellar statistics on major organizational change may encourage leaders to become more follower-focused throughout the change process. Originality/value The study makes a contribution to an understudied area of organizational research, specifically applied information processing theory. This is the first study that identifies employee perceptions of leadership as a mediator for perceptions of change and work engagement. From a value perspective, leaders as successful change agents recognize significant cost savings in dollars and human welfare by maintaining healthy workplaces with highly engaged workers