Twitter location (sometimes) matters: Exploring the relationship between georeferenced tweet content and nearby feature classes

In this paper, we investigate whether microblogging texts (tweets) produced on mobile devices are related to the geographical locations where they were posted. For this purpose, we correlate tweet topics to areas. In doing so, classified points of interest from OpenStreetMap serve as validation points. We adopted the classification and geolocation of these points to correlate with tweet content by means of manual, supervised, and unsupervised machine learning approaches. Evaluation showed the manual classification approach to be highest quality, followed by the supervised method, and that the unsupervised classification was of low quality. We found that the degree to which tweet content is related to nearby points of interest depends upon topic (that is, upon the OpenStreetMap category). A more general synthesis with prior research leads to the conclusion that the strength of the relationship of tweets and their geographic origin also depends upon geographic scale (where smaller scale correlations are more significant than those of larger scale)

Feature-based terrain editing from complex sketches

We present a new method for first person sketch-based editing of terrain models. As in usual artistic pictures, the input sketch depicts complex silhouettes with cusps and T-junctions, which typically correspond to non-planar curves in 3D. After analysing depth constraints in the sketch based on perceptual cues, our method best matches the sketched silhouettes with silhouettes or ridges of the input terrain. A deformation algorithm is then applied to the terrain, enabling it to exactly match the sketch from the given perspective view, while insuring that none of the user-defined silhouettes is hidden by another part of the terrain. We extend this sketch-based terrain editing framework to handle a collection of multi-view sketches. As our results show, this method enables users to easily personalize an existing terrain, while preserving its plausibility and style.This work was conducted during an internship of Flora Ponjou Tasse at Inria Rhône-Alpes in Grenoble. It was partly supported by the ERC advanced grant EXPRESSIVE.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S009784931400081

Modeling Large Time Series for Efficient Approximate Query Processing

Evolving customer requirements and increasing competition force business organizations to store increasing amounts of data and query them for information at any given time. Due to the current growth of data volumes, timely extraction of relevant information becomes more and more difficult with traditional methods. In addition, contemporary Decision Support Systems (DSS) favor faster approximations over slower exact results. Generally speaking, processes that require exchange of data become inefficient when connection bandwidth does not increase as fast as the volume of data. In order to tackle these issues, compression techniques have been introduced in many areas of data processing. In this paper, we outline a new system that does not query complete datasets but instead utilizes models to extract the requested information. For time series data we use Fourier and Cosine transformations and piece-wise aggregation to derive the models. These models are initially created from the original data and are kept in the database along with it. Subsequent queries are answered using the stored models rather than scanning and processing the original datasets. In order to support model query processing, we maintain query statistics derived from experiments and when running the system. Our approach can also reduce communication load by exchanging models instead of data. To allow seamless integration of model-based querying into traditional data warehouses, we introduce a SQL compatible query terminology. Our experiments show that querying models is up to 80 % faster than querying over the raw data while retaining a high accuracy

Toward a climate downscaling for the Eastern Mediterranean at high-resolution

International audienceAs a first step toward downscaling global model simulations of future climates for the eastern Mediterranean Sea and surrounding land area, mesoscale-model simulations with the Pennsylvania State University ? National Center for Atmospheric Research (NCAR) mesoscale model, version 5 (MM5) are verified in the context of precipitation amount. The simulations are driven with January NCAR-NCEP reanalysis project (NNRP) lateral-boundary conditions and assimilate surface and upper air observations. The results of the simulations compare reasonably well with rain gauge and satellite estimates of monthly total precipitation, and the model reproduces the overall trends in inter-annual precipitation variability for one test region. Cyclones during the period were tracked, and their properties identified

On the socio-technical potential for onshore wind in Europe : a response to Enevoldsen et al. (2019), Energy Policy, 132, 1092-1100

Acknoweldgements: S.W. and J.S. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (reFUEL, grant agreement No. 758149). J.L. and T.T. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 715132).Peer reviewedPostprin