An Abstract Domain to Infer Types over Zones in Spreadsheets

International audienceSpreadsheet languages are very commonly used, by large user bases, yet they are error prone. However, many semantic issues and errors could be avoided by enforcing a stricter type discipline. As declaring and specifying type information would represent a prohibitive amount of work for users, we propose an abstract interpretation based static analysis for spreadsheet programs that infers type constraints over zones of spreadsheets, viewed as two-dimensional arrays. Our abstract domain consists in a cardinal power from a numerical abstraction describing zones in a spreadsheet to an abstraction of cell values, including type properties. We formalize this abstract domain and its operators (transfer functions, join, widening and reduction) as well as a static analysis for a simplified spreadsheet language. Last, we propose a representation for abstract values and present an implementation of our analysis

Model inference for spreadsheets

Many errors in spreadsheet formulas can be avoided if spreadsheets are built automati- cally from higher-level models that can encode and enforce consistency constraints in the generated spreadsheets. Employing this strategy for legacy spreadsheets is dificult, because the model has to be reverse engineered from an existing spreadsheet and existing data must be transferred into the new model-generated spreadsheet. We have developed and implemented a technique that automatically infers relational schemas from spreadsheets. This technique uses particularities from the spreadsheet realm to create better schemas. We have evaluated this technique in two ways: First, we have demonstrated its appli- cability by using it on a set of real-world spreadsheets. Second, we have run an empirical study with users. The study has shown that the results produced by our technique are comparable to the ones developed by experts starting from the same (legacy) spreadsheet data. Although relational schemas are very useful to model data, they do not t well spreadsheets as they do not allow to express layout. Thus, we have also introduced a mapping between relational schemas and ClassSheets. A ClassSheet controls further changes to the spreadsheet and safeguards it against a large class of formula errors. The developed tool is a contribution to spreadsheet (reverse) engineering, because it lls an important gap and allows a promising design method (ClassSheets) to be applied to a huge collection of legacy spreadsheets with minimal effort.We would like to thank Orlando Belo for his help on running and analyzing the empirical study. We would also like to thank Paulo Azevedo for his help in conducting the statistical analysis of our empirical study. We would also like to thank the anonymous reviewers for their suggestions which helped us to improve the paper. This work is funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-010048. The first author was also supported by FCT grant SFRH/BPD/73358/2010

Model-based programming environments for spreadsheets

Spreadsheets can be seen as a flexible programming environment. However, they lack some of the concepts of regular programming languages, such as structured data types. This can lead the user to edit the spreadsheet in a wrong way and perhaps cause corrupt or redundant data. We devised a method for extraction of a relational model from a spreadsheet and the subsequent embedding of the model back into the spreadsheet to create a model-based spreadsheet programming environment. The extraction algorithm is specific for spreadsheets since it considers particularities such as layout and column arrangement. The extracted model is used to generate formulas and visual elements that are then embedded in the spreadsheet helping the user to edit data in a correct way. We present preliminary experimental results from applying our approach to a sample of spreadsheets from the EUSES Spreadsheet Corpus. Finally, we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze here the results achieved.This work is funded by ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-010048. The first author is supported by the FCT grant SFRH/BPD/73358/2010

Ecologies of e-Infrastructures

We present and discuss a historical reconstruction of the development of a Microsoft SharePoint eInfrastructure in NorthOil (2003 – 2008). The eInfrastructure was to support strategically emphasized work processes and open up a richer context of decision-making around production optimization. Specifically, the new eInfrastructure was to make it more convenient to trace decisions historically and across disciplinary and geographical boundaries – a need driven in part by post-Enron requirements for more elaborate and systematic reporting to the stock exchange. The Microsoft-based SharePoint eInfrastructure was intended to “seamlessly” integrate the many different and distinct information systems holding relevant information on production optimization. A principal aim of our study is to analyze how, why, and who resisted this largely top-down eInfrastructure initiative. We analyze how local practices rely heavily on specialized, niche information systems that are patched together as an ongoing performance to achieve commensurability. These local practices, however, are not immune to change. We discuss the indications of a transformative amalgam of (elements of) the new eInfrastructure and (elements of) the existing, local practices

The 4+1 Model of Data Science

Data Science is a complex and evolving field, but most agree that it can be defined as a combination of expertise drawn from three broad areascomputer science and technology, math and statistics, and domain knowledge -- with the purpose of extracting knowledge and value from data. Beyond this, the field is often defined as a series of practical activities ranging from the cleaning and wrangling of data, to its analysis and use to infer models, to the visual and rhetorical representation of results to stakeholders and decision-makers. This essay proposes a model of data science that goes beyond laundry-list definitions to get at the specific nature of data science and help distinguish it from adjacent fields such as computer science and statistics. We define data science as an interdisciplinary field comprising four broad areas of expertise: value, design, systems, and analytics. A fifth area, practice, integrates the other four in specific contexts of domain knowledge. We call this the 4+1 model of data science. Together, these areas belong to every data science project, even if they are often unconnected and siloed in the academy.Comment: 28 page

RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses

Virtual exchanges in higher education: developing intercultural skills of students across borders through online collaboration

Virtual exchange has been defined as a form of virtual mobility which aims to expand the reach and scope of traditional intercultural learning programs. This paper presents an example of a virtual exchange called InterCult - Intercultural Competences - which aimed to give an opportunity for students from Germany, France, and Brazil to explore intercultural aspects through online collaboration, i. e. to learn differences between own culture and other cultures by communicating and working on tasks together by using digital media. The research path was divided into three phases: project design, virtual exchange, and evaluation. The data collected during the virtual exchange involved the analysis of online conversations in international groups, face-to-face discussions during the classes at the end of each activity in national groups, the videos produced and shared in the online community, online meetings between the teachers, and the results of the online survey. Data were analyzed based on three perspectives: technology; engagement and collaborative work; intercultural competences. Results showed this type of experience is extremely important for a generation who will have to work in multicultural teams and contexts.El intercambio virtual se ha definido como una forma de movilidad virtual cuyo objetivo es ampliar el alcance y ámbito de los programas tradicionales de aprendizaje intercultural. Este artículo presenta un ejemplo de un intercambio virtual llamado InterCult - Competencias Interculturales - que pretendía dar la oportunidad a los estudiantes de Alemania, Francia y Brasil de explorar aspectos interculturales a través de la colaboración en línea, es decir, aprender las diferencias entre la propia cultura y otras culturas mediante la comunicación y el trabajo conjunto en tareas mediante el uso de medios digitales. El trabajo de investigación se dividió en tres fases: diseño del proyecto, intercambio virtual y evaluación. Los datos recogidos durante el intercambio virtual incluyeron el análisis de conversaciones en línea en grupos internacionales, discusiones cara a cara durante las clases al final de cada actividad en grupos nacionales, los videos producidos y compartidos en la comunidad en línea, reuniones en línea entre los profesores, y los resultados de la encuesta en línea. Los datos se analizaron desde tres perspectivas: tecnología; implicación y trabajo colaborativo; competencias interculturales. Los resultados mostraron que este tipo de experiencia es extremadamente importante para una generación que tendrá que trabajar en equipos y contextos multiculturales