Cícero Pahins

Coordinating software development with teams distributed across different sites can be challenging. SIDIA is an R&D institute that is responsible for implementing innovative solutions for the Brazilian and global market through research activities and development. SIDIA has Samsung Company as a strategic partner, and part of its teams work in collaboration with Samsung Mobile division, located in Korea. Both local and remote teams have different skills including software engineering, design… Mostrar más

Coordinating software development with teams distributed across different sites can be challenging. SIDIA is an R&D institute that is responsible for implementing innovative solutions for the Brazilian and global market through research activities and development. SIDIA has Samsung Company as a strategic partner, and part of its teams work in collaboration with Samsung Mobile division, located in Korea. Both local and remote teams have different skills including software engineering, design, and quality assurance. This report focuses on the experience at deg and deploying a new software development model called Blueprint, a model based on Scrum and Kanban agile methodologies that facilitate (i) global software development, (ii) teams and tasks allocations, and (iii) effective communication. We also report the lessons learned from adopting the Blueprint model in the development of a project. Mostrar menos

We demonstrate COVIZ, an interactive system to visually form and explore patient cohorts. COVIZ seamlessly integrates visual cohort formation and exploration, making it a single destination for hypothesis generation. COVIZ is easy to use by medical experts and offers many features: (1) It provides the ability to isolate patient demographics (e.g., their age group and location), health markers (e.g., their body mass index), and treatments (e.g., Ventilation for respiratory problems), and hence… Mostrar más

We demonstrate COVIZ, an interactive system to visually form and explore patient cohorts. COVIZ seamlessly integrates visual cohort formation and exploration, making it a single destination for hypothesis generation. COVIZ is easy to use by medical experts and offers many features: (1) It provides the ability to isolate patient demographics (e.g., their age group and location), health markers (e.g., their body mass index), and treatments (e.g., Ventilation for respiratory problems), and hence facilitates cohort formation; (2) It summarizes the evolution of treatments of a cohort into health trajectories, and lets medical experts explore those trajectories; (3) It guides them in examining different facets of a cohort and generating hypotheses for future analysis; (4) Finally, it provides the ability to compare the statistics and health trajectories of multiple cohorts at once. COVIZ relies on QDS, a novel data structure that encodes and indexes various data distributions to enable their efficient retrieval. Additionally, COVIZ visualizes air quality data in the regions where patients live to help with data interpretations. We demonstrate two key scenarios. In the ecological scenario, we show how COVIZ can be used to explore patient data to generate hypotheses on the health evolution of cohorts. In the case cross-over scenario, we show how COVIZ can be used to generate hypotheses on cohort health and pollution data. A video demonstration of COVIZ is accessible via http://bit.ly/coviz-video.
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In recent years sophisticated data structures based on datacubes have been proposed to perform interactive visual exploration of large datasets. While powerful, these approaches overlook the important fact that aggregations used to produce datacubes do not represent the actual distribution of the data being analyzed. As a result, these methods might produce biased results as well as hide important features in the data. In this paper, we introduce the Quantile Datacube Structure (QDS) that… Mostrar más

In recent years sophisticated data structures based on datacubes have been proposed to perform interactive visual exploration of large datasets. While powerful, these approaches overlook the important fact that aggregations used to produce datacubes do not represent the actual distribution of the data being analyzed. As a result, these methods might produce biased results as well as hide important features in the data. In this paper, we introduce the Quantile Datacube Structure (QDS) that bridges this gap by ing interactive visual exploration based on order statistics. To achieve this, QDS makes use of an efficient non-parametric distribution approximation scheme called p-digest and employs a novel datacube indexing scheme that reduces the memory usage of previous datacube methods. This enables interactive slicing and dicing while accurately approximating the distribution of quantitative variables of interest. We present two case studies that illustrate the ability of QDS to not only build order statistics based visualizations interactively but also to perform event detection on very large datasets. Finally, we present extensive experimental results that validate the effectiveness of QDS regarding memory usage and accuracy in the approximation of order statistics for real-world datasets. Mostrar menos

Big data visualization is a main task for data analysis. Due to its complexity in of volume and variety, very large datasets are unable to be queried for similarities among entries in traditional Database Management Systems. In this paper, we propose an effective approach for indexing millions of elements with the purpose of performing single and multiple visual similarity queries on multidimensional data associated with geographical locations. Our approach makes use of Z-Curve algorithm… Mostrar más

Big data visualization is a main task for data analysis. Due to its complexity in of volume and variety, very large datasets are unable to be queried for similarities among entries in traditional Database Management Systems. In this paper, we propose an effective approach for indexing millions of elements with the purpose of performing single and multiple visual similarity queries on multidimensional data associated with geographical locations. Our approach makes use of Z-Curve algorithm to map into 1D space considering similarities between data. Additionally, we present a set of results using real data of different sources and we analyze the insights obtained from the interactive exploration. Mostrar menos

The visual analysis of large multidimensional spatiotemporal datasets poses challenging questions regarding storage requirements and query performance. Several data structures have recently been proposed to address these problems that rely on indexes that pre-compute different aggregations from a known-a-priori dataset. Consider now the problem of handling streaming datasets, in which data arrive as one or more continuous data streams. Such datasets introduce challenges to the data structure… Mostrar más

The visual analysis of large multidimensional spatiotemporal datasets poses challenging questions regarding storage requirements and query performance. Several data structures have recently been proposed to address these problems that rely on indexes that pre-compute different aggregations from a known-a-priori dataset. Consider now the problem of handling streaming datasets, in which data arrive as one or more continuous data streams. Such datasets introduce challenges to the data structure, which now has to dynamic updates (insertions/deletions) and rebalancing operations to perform self-reorganizations. In this work, we present the Packed-Memory Quadtree (PMQ), a novel data structure designed to visual exploration of streaming spatiotemporal datasets. PMQ is cache-oblivious to perform well under different cache configurations. We store streaming data in an internal index that keeps a spatiotemporal ordering over the data following a quadtree representation, with for real-time insertions and deletions. We validate our data structure under different dynamic scenarios and compare to competing strategies. We demonstrate how PMQ could be used to answer different types of visual spatiotemporal range queries of streaming datasets. Mostrar menos

We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such… Mostrar más

We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such as binned scatterplots, linked histograms and heatmaps. We report memory usage, build time and query latencies for a variety of synthetic and real-world datasets, and find that although sometimes Hashedcubes offers slightly slower querying times to the state of the art, the typical query is answered fast enough to easily sustain a interaction. In datasets with hundreds of millions of elements, only about 2% of the queries take longer than 40ms. Finally, we discuss the limitations of data structure, potential spacetime tradeoffs, and future research directions. Mostrar menos

Spatial hashing is an efficient technique to speed up proximity queries on moving objects in the space domain, suitable for computer entertainment applications and simulations. This paper presents an efficient three-step algorithm for building a 1D hash table for spatial hashing needed to perform fast queries on objects for location and proximity detection. In contrast to existing solutions, this algorithm uses fixed-size vectors and pivots instead of dynamic data structures to deal with… Mostrar más

Spatial hashing is an efficient technique to speed up proximity queries on moving objects in the space domain, suitable for computer entertainment applications and simulations. This paper presents an efficient three-step algorithm for building a 1D hash table for spatial hashing needed to perform fast queries on objects for location and proximity detection. In contrast to existing solutions, this algorithm uses fixed-size vectors and pivots instead of dynamic data structures to deal with collisions in the hash table. This also enables iterating through entities and performing proximity queries in a linear memory. Experiments conducted shows that the proposed algorithm is, on average, at least 3 times faster than existing solutions based on dynamic data structures. This contributes to realizing interactive frame rates with massive number of moving entities. Mostrar menos

This paper presents a new Divide-and-Conquer Ray-Tracing (DACRT) algorithm that is designed to perform on multi-core processors. This new algorithm proposes a parallel and generic scheme that, without the use of any data structure for spatial subdivision, maintains memory management minimal and deterministic. Initially, the scene is divided into sub-scenes and those uniformly distributed across available hardware resources, processing each sub-scene individually. After, an iterative step to… Mostrar más

This paper presents a new Divide-and-Conquer Ray-Tracing (DACRT) algorithm that is designed to perform on multi-core processors. This new algorithm proposes a parallel and generic scheme that, without the use of any data structure for spatial subdivision, maintains memory management minimal and deterministic. Initially, the scene is divided into sub-scenes and those uniformly distributed across available hardware resources, processing each sub-scene individually. After, an iterative step to ensure the correct results is performed until the final frame is obtained. Results show that our algorithm is up to 2.4x times faster than the original DACRT in a common quad-core processor setup, allowing very high interactive frame rates in well-known benchmark scenes. Mostrar menos