Abstract Ubiquitous computing has brought new challenges for the design of scenarios for people–technology interactions. Despite considerable research on formal accessibility standards, related work has hardly investigated them in ubiquitous computing contexts. In this work, we investigate means of analyzing accessibility aspects in ubiquitous environments based on two standard instruments: the Universal Design (UD) Principles and Guidelines and the W3C-WCAG (Web Content Accessibility Guidelines of the World Wide Web Consortium). Both instruments were applied to the context of socioenactive ubiquitous environments, providing insights into their applicability and shortcomings. As a result of this analysis we constructed UbiAccess, an instrument to evaluate access in ubiquitous scenarios, which combines and extends characteristics of both UD and W3C-WCAG, filling in some of the gaps we identified. The application of UbiAccess to a case study shows the advantages of its use in informing the evaluation of access in ubiquitous scenarios.

Vast amounts of data are being generated daily with the adoption of Internet-of-Things (IoT) solutions in an ever-increasing number of application domains. There are problems associated with all stages of the lifecycle of these data (e.g., capture, curation and preservation). Moreover, the volume, variety, dynamicity and ubiquity of IoT data present additional challenges to their usability, prompting the need for constructing scalable data-intensive IoT data management and processing platforms. This paper presents a novel approach to model and build IoT data platforms based on the characteristics of an Actor-Oriented Database (AODB). We take advantage of two complementary case studies – in structural health monitoring and beef cattle tracking and tracing – to describe novel software requirements introduced by IoT data processing. Our investigation illustrates the challenges and benefits provided by AODB to meet these requirements in terms of modeling and IoT-based systems implementation. Obtained results reveal the advantages of using AODB in IoT scenarios and lead to principles on how to effectively use an actor model to design and implement IoT data platforms.

Scientific workflow management systems play a major role in the design, execution and documentation of computational experiments. However, they have limited support for managing workflow evolution and exploration because they lack rich metadata for the software that implements workflow components. Such metadata could be used to support scientists in exploring local adjustments to a workflow, replacing components with similar software, or upgrading components upon release of newer software versions. To address this challenge, we propose OntoSoft-VFF (Ontology for Software Version, Function and Functionality), a software metadata repository designed to capture information about software and workflow components that is important for managing workflow exploration and evolution. Our approach uses a novel ontology to describe the functionality and evolution through time of any software used to create workflow components. OntoSoft-VFF is implemented as an online catalog that stores semantic metadata for software to enable workflow exploration through understanding of software functionality and evolution. The catalog also supports comparison and semantic search of software metadata. We showcase OntoSoft-VFF using machine learning workflow examples. We validate our approach by testing that a workflow system could compare differences in software metadata, explain software updates and describe the general functionality of workflow steps.

Scientific datasets and the experiments that analyze them are growing in size and complexity, and scientists are facing difficulties to share such resources. Some initiatives have emerged to try to solve this problem. One of them involves the use of scientific workflows to represent and enact experiment execution. There is an increasing number of workflows that are potentially relevant for more than one scientific domain. However, it is hard to find workflows suitable for reuse given an experiment. Creating a workflow takes time and resources, and their reuse helps scientists to build new workflows faster and in a more reliable way. Search mechanisms in workflow repositories should provide different options for workflow discovery, but it is difficult for generic repositories to provide multiple mechanisms. This paper presents WorkflowHunt, a hybrid architecture for workflow search and discovery for generic repositories, which combines keyword and semantic search to allow finding relevant workflows using different search methods. We validated our architecture creating a prototype that uses real workflows and metadata from myExperiment, and compare search results via WorkflowHunt and via myExperiment's search interface.

The Web is witnessing an exponential growth of distributed and heterogeneous educational material. This hampers distinguishing among contents of these materials, as well as their retrieval. While information retrieval and classification mechanisms concentrate on corpus analysis, annotation approaches either target specific formats or require that a document follows interoperable standards. Rather than target only textual characteristics, our strategy is mainly based on components of educational material. The header, body, footer and numbering of slides and progress bar are examples of components of slides and videos. Though our work is general purpose, it is being tested against slides and videos from Coursera, a web platform that provides universal access to online education material and courses from universities and organizations around the world.

Scientific discovery and analysis are increasingly computational and data-driven. While scripting languages, such as Python, R and Perl, are the means of choice of the majority of scientists to encode and run their data analysis, scripts are generally not amenable to reuse or reproducibility. Scripts do rarely get reused or even shared with third party scientists. We argue in this paper that the reproducibility of scripts can be promoted by converting them into workflow research objects. A workflow research object encodes a script into a production (executable) workflow that is accompanied by annotations, example datasets and provenance traces of their execution, thereby allowing third party users to understand the data analysis encoded by the original script, run the associated workflow using the same or different dataset, or even repurpose it for a different analysis. To this end, we present a methodology for converting scripts into workflow research objects in a principled manner, guided by requirements that we elicited for this purpose. The methodology exploits tools and standards that have been developed by the community, in particular YesWorkflow, Research Objects and the W3C PROV. It is showcased using a real world use case from the field of Molecular Dynamics.

This paper addresses the problem of improving the quality of metadata in biological observation databases, in particular those associated with observations of living beings, and which are often used as a starting point for biodiversity analyses. Poor quality metadata lead to incorrect scientific conclusions, and can mislead experts. Thus, it is important to design and develop methods to detect and correct metadata quality problems. This is a challenging problem because of the variety of issues concerning such metadata, e.g., misnaming of species, location uncertainty and imprecision concerning where observations were recorded. Related work is limited because it does not adequately model such issues. We propose a geographic approach based on expert-led classification of place and/or range mismatch anomalies detected by our algorithms. Our approach enables detection of anomalies in both species' reported geographic distributions and in species' identification. Our main contribution is our geographic algorithm that deals with uncertain/imprecise locations. Our work is tested using a case study with the Fonoteca Neotropical Jacques Vielliard, one of the 10 largest animal sound collections in the world.

We are immersed in a world in which we constantly deal (and cope) with objects and phenomena in a variety of scales in space and time. With the increase in collaborative and inter-disciplinary research, there appeared a growing need for handling data in multiple scales and representations, within a single environment. The so called multi-scale environments must guarantee the manipulation of information while ensuring consistency. This paper is concerned with the challenges of managing data in multiple scales, while preserving consistency across scales. Its main contributions are the following: (a) the specification of generic, extensible multi-scale integrity constraints; and (b) the implementation of a prototype based on data versioning, which supports the maintenance of these constraints. This prototype was tested using watershed data from Brazil.

SUMMARY For decades, biologists around the world have recorded animal sounds. As the number of records grows, so does the difficulty to manage them, presenting challenges to save, retrieve, share, and manage sounds. These challenges are complicated by the fact that animal sound recordings have specific peculiarities, associated to the context in which the sound was recorded. For example, sounds emitted by individuals that are in groups may be different from ones emitted by isolated individuals. Although these characteristics may be relevant to biologists, they are seldom explicit in the recording metadata. This paper discusses our ongoing research on the management of sound recordings, considering factors such as environmental or social contexts, which are not treated by current systems. This work exploits retrieval based on context analysis. Query parameters include context variables that are dynamically derived using public services and ontologies associated with sound recording metadata. Part of the results have been validated through a web prototype, discussed in the text. Copyright © 2012 John Wiley & Sons, Ltd.

A supply chain comprises several different kinds of actors that interact either in an ad hoc fashion (e.g. an eventual deal) or in a previously well-planned way. In the latter, how the interactions develop is described in contracts that are agreed on before the interactions start. This agreement may involve several partners, thus a multi-party contract is better suited than a set of bi-lateral contracts. If one is willing to negotiate automatically such kind of contracts, an appropriate negotiation protocol should be at hand. However, the ones for bi-lateral contracts are not suitable for multi-party contracts, e.g. the way to achieve consensus when only two negotiators are haggling over some issues is quite different if there are several negotiators involved. In the first case, a simple bargain would suffice, but in the latter a ballot process is needed. This paper briefly presents our negotiation protocol for electronic multi-party contracts which seamlessly combines several negotiation styles. It also elaborates on the main negotiation patterns the protocol allows for: bargain (for peer-to-peer negotiation), auction (when there is competition among the negotiators) and ballot (when the negotiation aims at consensus) and presents other patterns that can be built on these basic ones. Finally, it describes an implementation of this protocol based on Web services, and built on the YAWL Workflow Management System.

A key issue in eScience concerns the management of data used, transformed and produced by experiments. Such data are highly heterogeneous and require a wide range of mechanisms to support their creation, documentation, mining, management and visualization. This paper tackles one of these mechanisms: annotation of scientific data. It discusses specific aspects of a framework that helps scientific research by providing support to create and manage annotations of geospatial data sources. The annotation process is driven by scientific workflows, and is thus repeatable and customizable. The annotations produced are linked to ontologies by SAM (Semantic Annotation Manager), a system we developed to ensure annotation consistency and management.

Geospatial information (GI) constitutes a significant portion of available data and are a key factor in planning and decision-making in a variety of domains, such as emergency management and agriculture. However, to be used, these data have to be interpreted, sometimes producing new data and information. This new information is generally embedded on additional files, or remains on experts' brains. Hence, every time a user wants to use its knowledge, data have to be interpreted again. This paper presents a framework for alleviating this problem based on semi-automatic annotation of geospatial data. This framework is described in detail, as well the choices made in its design and implementation. At the end, we present a case study in agriculture, used to validate our proposal.

This paper analyzes how interoperability and componentization efforts in the geospatial domain have an underestimated impact on the user perspective, directly affecting model development. This discussion is illustrated by the description of the design and implementation of WebMAPS, a geospatial information system to support agricultural planning and monitoring.

Scientific research is producing and consuming large volumes of multimedia data at an ever growing rate. Annotations to the data helps associating context and enhances content management, making it easier to interpret and share data. However, raw data often needs to go through complex processing steps before it can be consumed. During these transformation processes, original annotations from the production phase are often discarded or ignored, since their usefulness is usually limited to the first transformation step. New annotations must be associated with the final product, a time consuming task often carried out manually. Systematically associating new annotations to the result of each data transformation step is known as annotation propagation. This paper introduces techniques for structuring annotations by applying references to ontologies and automatically transforming these annotations along with data transformation processes. This helps the construction of new annotated multimedia data sets, preserving contextual information. The solution is based on: (i) the notion of semantic annotations; and (ii) a set of transformations rules, based on ontological relations.

The Web is evolving from a space for publication/consumption of documents to an environment for collaborative work, where digital content can travel and be replicated, adapted, decomposed, fusioned, and transformed. We call this the fluid Web perspective. This view requires a thorough revision of the typical document-oriented approach that permeates content management on the Web. This paper presents our solution for the Fluid Web, which allows moving from the document-oriented to a content-oriented perspective, where "content" can be any digital object. The solution is based on two axes: a self-descriptive unit to encapsulate any kind of content artifact - the digital content component (DCC) and a fluid Web infrastructure that provides management and deployment of DCCs through the Web, and whose goal is to support collaboration on the Web. Designed to be reused and adapted, DCCs encapsulate data and software using a single structure, thus allowing homogeneous composition and processing of any digital content, executable or not. These properties are exploited by our fluid Web infrastructure, which supports DCC multilevel annotation and discovery mechanisms, configuration management, and version control. Our work extensively explores taxonomic ontologies and semantic Web standards, which serve as a semantic bridge, unifying DCC management vocabularies, and improving DCC description/indexing/discovery. DCCs and infrastructure have been implemented and are illustrated by means of a running example, for a scientific application

Content-Based Image Retrieval (CBIR) presents several challenges and has been subject to extensive research from many domains, such as image processing or database systems. Database researchers are concerned with indexing and querying, whereas image processing experts worry about extracting appropriate image descriptors. Comparatively little work has been done on designing user interfaces for CBIR systems. This, in turn, has a profound effect on these systems since the concept of image similarity is strongly influenced by user perception. This paper describes an initial effort to fill this gap, combining recent research in CBIR and Information Visualization, studied from a Human-Computer Interface perspective. It presents two visualization techniques based on Spiral and Concentric Rings implemented in a CBIR system to explore query results. The approach is centered on keeping user focus on both the query image, and the most similar retrieved images. Experiments conducted so far suggest that the proposed visualization strategies improves system usability.

This paper presents a new formalism for workflow process definition, which combines research in programming languages and in database systems. This formalism is based on creating a library of workflow building blocks, which can be progressively combined and nested to construct complex workflows. Workflows are specified declaratively, using a simple high level language, which allows the dynamic definition of exception handling and events, as well as dynamically overriding workflow definition. This ensures a high degree of flexibility in data and control flow specification, as well as in reuse of workflow specifications to construct other workflows. The resulting workflow execution environment is well suited to supporting cooperative work.

There is a wide range of scientific applications requiring sophisticated management of spatio-temporal data. However existing database management systems offer very limited support for managing such data. Thus, it is left to the researchers themselves to repeatedly code this management into each application. We present an extensible framework, based on extending an object-oriented database system, with kernel spatio-temporal classes, data structures and functions, to provide support for the development of spatio-temporal applications.

Presents a new approach to user interface customization in geographic information systems (GIS). This approach is based on the integration of three main components: a GIS user interface architecture; an active database mechanism; and a generic interface builder. The GIS interface architecture provides the default interface behavior, while the active system allows customization of interfaces according to the specific context. The generic interface builder relies on a library of interface objects to dynamically construct generic and customized interfaces. The main advantage of this approach is that it decreases the costs associated with developing customized GIS interfaces.

An approach to providing view support in object-oriented (O/sub 2/) databases is presented. The approach uses what is called a hyper-view and combines results from research on database design and man-machine interfaces. A mechanism that allows interacting with databases through hyper-views and built on top of the O/sub 2/ system is described. The approach is based on conciliating stored data and their visualization. Not only views corresponding to one class, but also multiclass views are considered. Hyper-views are supported by ToonMaker, a user interface generator system implemented for the O/sub 2/ database management system.>