Smartphones are conquering the mobile phone market; they are not just phones; they also act as media players, gaming consoles, personal calendars, storage, etc. They are portable computers with fewer ...computing capabilities than personal computers. However, unlike personal computers, users can carry their smartphone with them at all times. The ubiquity of mobile phones and their computing capabilities provide an opportunity of using them as a life-logging device. Life-logs (personal e-memories) are used to record users’ daily life events and assist them in memory augmentation. In a more technical sense, life-logs sense and store users’ contextual information from their environment through sensors, which are core components of life-logs. Spatio-temporal aggregation of sensor information can be mapped to users’ life events. We propose UbiqLog, a lightweight, configurable, and extendable life-log framework, which uses mobile phone as a device for life logging. The proposed framework extends previous research in this field, which investigated mobile phones as life-log tool through continuous sensing. Its openness in terms of sensor configuration allows developers to create flexible, multipurpose life-log tools. In addition to that, this framework contains a data model and an architecture, which can be used as reference model for further life-log development, including its extension to other devices, such as ebook readers, T.V.s, etc.
In supply chain management (SCM), two topics have gained importance over the last years. On the one hand, sustainable SCM (SSCM) has become increasingly relevant and many publications have ...contributed to the topic. On the other hand, information technology (IT) is being progressively considered as a key enabler for efficiency in supply chains. Several research efforts have contributed to the field of IT for SSCM. However, this paper is the first recent attempt to summarise the current state of the art of how IT can affect SSCM in any structured way and to compare it with IT for 'general' SCM to give guidance for future research. This paper surveys 55 peer-reviewed articles that were retrieved through keyword searches (until May 2014). The analysis identifies research deficits as well as a lack of scientific discourse employing empirical techniques and a lack of investigations on the social sustainability. Additionally, possible topics for further research were derived by comparing the survey's results with the current research on IT for 'general' SCM following the analysis of 631 articles. Six fields could be identified, namely output/effects of IT, machine communication and multiagents, inputs and IT-supported processing, IT-enabled interorganisational exchange, quantitative IT approaches and a sector focus.
Privacy requirements engineering acts as a role to systematically elicit privacy requirements from system requirements and legal requirements such as the GDPR. Many methodologies have been proposed, ...but the majority of them are focused on the waterfall approach, making adopting privacy engineering in agile software development difficult. The other major issue is that the process currently is to a high degree manual. This paper focuses on closing these gaps through the development of a machine learning-based approach for identifying privacy requirements in an agile software development environment, employing natural language processing (NLP) techniques. Our method aims to allow agile teams to focus on functional requirements while NLP tools assist them in generating privacy requirements. The main input for our method is a collection of user stories, which are typically used to identify functional requirements in agile software development. The NLP approach is then used to automate some human-intensive tasks such as identifying personal data and creating data flow diagrams from user stories. The data flow diagram forms the basis for the automatic creation of privacy requirements. Our evaluation shows that our NLP method achieves a fairly good performance in terms of F-Measure. We are also demonstrate the feasibility of our NLP approach in CamperPlus project. Lastly, we are developing a tool to integrate our NLP approach into the privacy requirements engineering pipeline, allowing for manual editing of results so that agile teams can maintain control over the automated approach.
Privacy requirements engineering is a crucial aspect of privacy engineering. It aims to integrate privacy principles into organizational and technical processes throughout the software development ...lifecycle. This specialized field involves various strategies, including compliance with regulatory frameworks, asset analysis, and system diagram development for threat modeling. The wide range of approaches, while beneficial in providing different perspectives, presents a significant challenge to the novice privacy engineer or developer in identifying the most effective methodologies. The lack of a single methodology highlights the need for a systematic literature review (SLR) to establish a standardized process for privacy requirements engineering that promotes consistency across different methodologies. To address this issue, we conducted a comprehensive SLR to synthesize existing privacy requirements engineering methodologies. Our analysis involved dissecting each method’s processes, tasks, techniques, work products, and resources. Our review examined 40 privacy requirements engineering methodologies detailed in 50 papers, from which we extracted five key processes commonly followed in privacy requirements engineering research. We used this as the foundation for a holistic approach to facilitate the adoption of a comprehensive privacy requirements engineering process. The review also identifies ongoing challenges and suggests future directions in this field.
The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile ...representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.
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