SIDIA is an R&D Institute located in Manaus (AM) focused on research and software development. SIDIA works in partnership with the Global Device Manufacturer developing the Android operating system embedded in mobile devices (smartphones and tablets) for Latin America. This complex scenario involves the development of products in collaboration with partners in different locations to accomplish manufacturing schedules. This poses a challenge in managing software development projects. This experience report describes a tool-based approach aiming to improve the availability of resources (software and hardware under development) as well as the requirements management process. We also report the lessons learned and difficulties of adopting the tool-based approach in the software development process.

Developing Android mobile devices with geographically distributed teams can be arduous. SIDIA is an R&D institute that is responsible for research and development for the Brazilian and global market. SIDIA has clients around the world, and one of them is a mobile device manufacturer which embeds the Android operating system into such devices. To sell smartphones in all desired countries, some regulations must be adhered to. In Brazil and Colombia, one of the mandatory regulations by law is that every product must contain a User Instruction Manual (UIM), which helps users to figure out distinctive aspects and features of the product. Currently, the Android OS Version for mobile devices commercialized in America (Latin mobile devices) and UIM are developed and designed in cities from different states (Manaus, Amazonas - Campinas, São Paulo) from Brazil. Moreover, to design the UIM is necessary to have access to the physical device, so it must be shipped across distant cities. Due to logistical problems and bureaucracy in administrative processes, the device may take a long time to be delivered and, consequently, may delay smartphone manufacturing, causing financial damage. This experience report describes the lessons learned on developing and integrating a tool-based approach that allows users to remotely access a device thereby solving the physical device dependency problem and addressing specific requirements of the UIM development.

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 supporting 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.

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 designing 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 members communication. We also report the lessons learned from adopting the Blueprint model in the development of a project.

With ever-larger software development systems involving more people with different skills, it is necessary to think about the process of bug assignment to groups of developers and not just to a developer alone. This work aims to leverage Bug Triage Process by suggesting a list of specialized groups of developers, or Technical Groups (TG's), to be attributed to a new bug report, based on other bugs that are similar and have been resolved by these TG's in the past. In the dataset used in our experiments, the mean time to correctly assign bug reports to their proper TG is 14 days, and just by then, the bug fixing process starts. This is a critical problem for software development and management since issues tend to accumulate a high-resolution time, which compromises developer performance and deliveries. In order to enhance the Bug Triage Process, we propose T-REC, an auxiliary SW Project Management system that accurately and efficiently analyzes similar issues to provide personalized TG recommendation. T-REC is a method that ensemble Machine Learning (ML) and Information Retrieval (IR) algorithms to recommend a list of TG's to handle an issue. Our experiments show that T-REC recommendation reaches an overall Acc@1 of 50.9%, Acc@2 of 63.2%, Acc@5 of 76.1%, Acc@10 of 83.6%, and Acc@20 of 89.7%. To the best of our knowledge, our work is the first to associate multiple machine learning strategies (classifiers, attributes, and training history) on the prediction of specialized groups of developers. We validate our approach on a real-world dataset from a large company that comprises 9.5M mobile-related bug reports from January 2001 to January 2019.

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, ecological scenario and case cross-over scenario . A video demonstration of COVIZ is accessible via http://bit.ly/video-coviz.

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.

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.