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Cristina Ribeiro

Bio: Cristina Ribeiro is an academic researcher from University of Porto. The author has contributed to research in topics: Metadata & Workflow. The author has an hindex of 16, co-authored 143 publications receiving 1097 citations. Previous affiliations of Cristina Ribeiro include Universidade Nova de Lisboa & American Board of Legal Medicine.


Papers
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Journal ArticleDOI
TL;DR: In this article, a new mixed-integer model was proposed, where binary decision variables are associated with each discrete point of the board (a dot) and with each piece type.

96 citations

Proceedings ArticleDOI
30 Mar 2008
TL;DR: This exploratory study uses two collections of web search queries to investigate the use of temporal expressions and finds that temporal expressions are rarely used and, when used, they are related to current and past events.
Abstract: While trying to understand and characterize users' behavior online, the temporal dimension has received little attention by the research community. This exploratory study uses two collections of web search queries to investigate the use of temporal information needs. Using state-of-the-art information extraction techniques we identify temporal expressions in these queries. We find that temporal expressions are rarely used (1.5% of queries) and, when used, they are related to current and past events. Also, there are specific topics where the use of temporal expressions is more visible.

92 citations

Journal ArticleDOI
TL;DR: A synthetic overview of current platforms that can be used for data management purposes and shows that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools.
Abstract: Research data management is rapidly becoming a regular concern for researchers, and institutions need to provide them with platforms to support data organization and preparation for publication. Some institutions have adopted institutional repositories as the basis for data deposit, whereas others are experimenting with richer environments for data description, in spite of the diversity of existing workflows. This paper is a synthetic overview of current platforms that can be used for data management purposes. Adopting a pragmatic view on data management, the paper focuses on solutions that can be adopted in the long tail of science, where investments in tools and manpower are modest. First, a broad set of data management platforms is presented—some designed for institutional repositories and digital libraries—to select a short list of the more promising ones for data management. These platforms are compared considering their architecture, support for metadata, existing programming interfaces, as well as their search mechanisms and community acceptance. In this process, the stakeholders’ requirements are also taken into account. The results show that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools. Nevertheless, depending on the context, some platforms can meet all or part of the stakeholders’ requirements.

70 citations

Journal ArticleDOI
TL;DR: A classification for spatio-temporal systems based on the properties of the represented objects is introduced, and it is claimed that features of some complex objects can be derived from those of simpler ones, suggesting an evolutionary approach.
Abstract: The fields of application of spatio-temporal systems, i.e., systems that must operate with time-varying spatial properties, are vast and heterogeneous. Since it would be difficult to treat such diversity as a whole, we introduce a classification for spatio-temporal systems based on the properties of the represented objects. Building on this classification, we also claim that features of some complex objects can be derived from those of simpler ones, suggesting an evolutionary approach, starting with the study of simple objects and progressing by enriching them with new features. This paper focuses on the definition of a data model for representation of moving points. The model is based on the decomposition of the trajectory of moving points into sections. The movement within each section of a trajectory is described by a variability function. Since, for most systems, it is not possible to store the exact knowledge about the movement of a mobile, the answers to queries may be imprecise. We propose two additional approaches to deal with imprecision, the superset and the subset semantics, based on a maximum value for the variability function, and a smooth technique to integrate them in the model. Finally, we analyse some functional aspects of the implementation of the data model on a Relational Database Management System (RDBMS) and outline some directions for future research.

60 citations

Proceedings ArticleDOI
08 Sep 2008
TL;DR: WikiChanges, a web-based application designed to plot an article's revision timeline in real time, and a revisions summarization task that addresses the need to understand what occurred during a given set of revisions are introduced.
Abstract: Wikis are popular tools commonly used to support distributed collaborative work. Wikis can be seen as virtual scrap-books that anyone can edit without having any specific technical know-how. The Wikipedia is a flagship example of a real-word application of wikis. Due to the large scale of Wikipedia it's difficult to easily grasp much of the information that is stored in this wiki. We address one particular aspect of this issue by looking at the revision history of each article. Plotting the revision activity in a timeline we expose the complete article's history in a easily understandable format. We present WikiChanges, a web-based application designed to plot an article's revision timeline in real time. WikiChanges also includes a web browser extension that incorporates activity sparklines in the real Wikipedia. Finally, we introduce a revisions summarization task that addresses the need to understand what occurred during a given set of revisions. We present a first approach to this task using tag clouds to present the revisions made.

37 citations


Cited by
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Journal Article
TL;DR: In this article, the authors explore the effect of dimensionality on the nearest neighbor problem and show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance of the farthest data point.
Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

1,992 citations

Journal ArticleDOI
TL;DR: An improved typology of C&P problems is presented, which is partially based on Dyckhoff’s original ideas, but introduces new categorisation criteria, which define problem categories different from those of Dykhoff.

1,359 citations

Journal ArticleDOI
16 May 2000
TL;DR: A novel, R*-tree based indexing technique that supports the efficient querying of the current and projected future positions of moving objects and is capable of indexing objects moving in one-, two-, and three-dimensional space is proposed.
Abstract: The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R*-tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

880 citations

01 Jan 2013
TL;DR: Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: to reproduce or to verify research, to make results of publicly funded research available to the public, to enable others to ask new questions of extant data, and to advance the state of research and innovation.
Abstract: We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

634 citations

Drew McDermott1
01 Jan 2005
TL;DR: A common disclaimer by an AI author is that he has neglected temporal considerations to avoid complication; the implication is nearly made that adding a temporal dimension to the research would be a familiar but tedious exercise that would obscure the new material presented by the author.
Abstract: Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first-order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It may be possible to implement a temporal-inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history.

530 citations