19,712 research outputs found
On Regulatory and Organizational Constraints in Visualization Design and Evaluation
Problem-based visualization research provides explicit guidance toward
identifying and designing for the needs of users, but absent is more concrete
guidance toward factors external to a user's needs that also have implications
for visualization design and evaluation. This lack of more explicit guidance
can leave visualization researchers and practitioners vulnerable to unforeseen
constraints beyond the user's needs that can affect the validity of
evaluations, or even lead to the premature termination of a project. Here we
explore two types of external constraints in depth, regulatory and
organizational constraints, and describe how these constraints impact
visualization design and evaluation. By borrowing from techniques in software
development, project management, and visualization research we recommend
strategies for identifying, mitigating, and evaluating these external
constraints through a design study methodology. Finally, we present an
application of those recommendations in a healthcare case study. We argue that
by explicitly incorporating external constraints into visualization design and
evaluation, researchers and practitioners can improve the utility and validity
of their visualization solution and improve the likelihood of successful
collaborations with industries where external constraints are more present.Comment: 9 pages, 2 figures, presented at BELIV workshop associated with IEEE
VIS 201
The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Development
With the general trend towards data-driven decision making (DDDM),
organizations are looking for ways to use DDDM to improve their decisions.
However, few studies have looked into the practitioners view of DDDM, in
particular for agile organizations. In this paper we investigated the
experiences of using DDDM, and how data can improve decision making. An emailed
questionnaire was sent out to 124 industry practitioners in agile software
developing companies, of which 84 answered. The results show that few
practitioners indicated a widespread use of DDDM in their current decision
making practices. The practitioners were more positive to its future use for
higher-level and more general decision making, fairly positive to its use for
requirements elicitation and prioritization decisions, while being less
positive to its future use at the team level. The practitioners do see a lot of
potential for DDDM in an agile context; however, currently unfulfilled
Boundary Objects and their Use in Agile Systems Engineering
Agile methods are increasingly introduced in automotive companies in the
attempt to become more efficient and flexible in the system development. The
adoption of agile practices influences communication between stakeholders, but
also makes companies rethink the management of artifacts and documentation like
requirements, safety compliance documents, and architecture models.
Practitioners aim to reduce irrelevant documentation, but face a lack of
guidance to determine what artifacts are needed and how they should be managed.
This paper presents artifacts, challenges, guidelines, and practices for the
continuous management of systems engineering artifacts in automotive based on a
theoretical and empirical understanding of the topic. In collaboration with 53
practitioners from six automotive companies, we conducted a design-science
study involving interviews, a questionnaire, focus groups, and practical data
analysis of a systems engineering tool. The guidelines suggest the distinction
between artifacts that are shared among different actors in a company (boundary
objects) and those that are used within a team (locally relevant artifacts). We
propose an analysis approach to identify boundary objects and three practices
to manage systems engineering artifacts in industry
OntoMaven: Maven-based Ontology Development and Management of Distributed Ontology Repositories
In collaborative agile ontology development projects support for modular
reuse of ontologies from large existing remote repositories, ontology project
life cycle management, and transitive dependency management are important
needs. The Apache Maven approach has proven its success in distributed
collaborative Software Engineering by its widespread adoption. The contribution
of this paper is a new design artifact called OntoMaven. OntoMaven adopts the
Maven-based development methodology and adapts its concepts to knowledge
engineering for Maven-based ontology development and management of ontology
artifacts in distributed ontology repositories.Comment: Pre-print submission to 9th International Workshop on Semantic Web
Enabled Software Engineering (SWESE2013). Berlin, Germany, December 2-5, 201
What influences the speed of prototyping? An empirical investigation of twenty software startups
It is essential for startups to quickly experiment business ideas by building
tangible prototypes and collecting user feedback on them. As prototyping is an
inevitable part of learning for early stage software startups, how fast
startups can learn depends on how fast they can prototype. Despite of the
importance, there is a lack of research about prototyping in software startups.
In this study, we aimed at understanding what are factors influencing different
types of prototyping activities. We conducted a multiple case study on twenty
European software startups. The results are two folds, firstly we propose a
prototype-centric learning model in early stage software startups. Secondly, we
identify factors occur as barriers but also facilitators for prototyping in
early stage software startups. The factors are grouped into (1) artifacts, (2)
team competence, (3) collaboration, (4) customer and (5) process dimensions. To
speed up a startups progress at the early stage, it is important to incorporate
the learning objective into a well-defined collaborative approach of
prototypingComment: This is the author's version of the work. Copyright owner's version
can be accessed at doi.org/10.1007/978-3-319-57633-6_2, XP2017, Cologne,
German
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
Creating business value from big data and business analytics : organizational, managerial and human resource implications
This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model
An open-access platform for camera-trapping data
In southern Mexico, local communities have been playing important roles in the design and collection of wildlife data through camera-trapping in community-based monitoring of biodiversity projects. However, the methods used to store the data have limited their use in matters of decision-making and research. Thus, we present the Platform for Community-based Monitoring of Biodiversity (PCMB), a repository, which allows storage, visualization, and downloading of photographs captured by community-based monitoring of biodiversity projects in protected areas of southern Mexico. The platform was developed using agile software development with extensive interaction between computer scientists and biologists. System development included gathering data, design, built, database and attributes creation, and quality control. The PCMB currently contains 28,180 images of 6478 animals (69.4% mammals and 30.3% birds). Of the 32 species of mammals recorded in 18 PA since 2012, approximately a quarter of all photographs were of white-tailed deer (Odocoileus virginianus). Platforms permitting access to camera-trapping data are a valuable step in opening access to data of biodiversity; the PCMB is a practical new tool for wildlife management and research with data generated through local participation. Thus, this work encourages research on the data generated through the community-based monitoring of biodiversity projects in protected areas, to provide an important information infrastructure for effective management and conservation of wildlife
Why and How Your Traceability Should Evolve: Insights from an Automotive Supplier
Traceability is a key enabler of various activities in automotive software
and systems engineering and required by several standards. However, most
existing traceability management approaches do not consider that traceability
is situated in constantly changing development contexts involving multiple
stakeholders. Together with an automotive supplier, we analyzed how technology,
business, and organizational factors raise the need for flexible traceability.
We present how traceability can be evolved in the development lifecycle, from
early elicitation of traceability needs to the implementation of mature
traceability strategies. Moreover, we shed light on how traceability can be
managed flexibly within an agile team and more formally when crossing team
borders and organizational borders. Based on these insights, we present
requirements for flexible tool solutions, supporting varying levels of data
quality, change propagation, versioning, and organizational traceability.Comment: 9 pages, 3 figures, accepted in IEEE Softwar
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