Improving Big Data Visual Analytics with Interactive Virtual Reality

For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.Comment: 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing Conference (HPEC '15); corrected typo

Contributions of academic articles to the practice of journalism and data management

The increasing number of open data resources and the arrival of big data have boosted the data available as a source of news. Journalists need new skills for collecting the data and creating the news. In addition, all this data can also be used to provide new media services and to take media business decisions, and journalists need new skills related to data for these tasks. Taking into account these areas of knowledge required by journalists for the use of data, we perform a structured literature review (SLR) followed by a content analysis. The results confirm the relevance of data management in journalistic practice, requiring skills in statistics, data visualization, technology, but also in ethics, marketing or audience monitoring

Visualización de datos y medios de comunicación: scoping review

Data visualization in journalism today has an intensive application as a storytelling tool. However, research and theoretical contributions in this field do not match it in volume or importance. Given these circumstances, the aim of this report is to analyze the scientific production on data visualization in news media; and, more specifically, on information visualization, infographics, visual narratives and visual storytelling. With this, the aim is, firstly, to know the state of the art on the discipline of visualization, secondly, to identify new trends, challenges, technologies and protocols and, thirdly, to determine its possibilities and limitations in the field of communication and journalism. The results indicate that the main studies developed in recent years on this discipline have focused mainly on infographics, data journalism, structured journalism, media literacy on the reader's interpretation of visual data, visualization proposals in digital news media and visual analytics. Regarding data visualization technologies and tools, very interesting studies have been carried out on Big data and media, and more specifically on software and on the creation of visual analysis tools. In addition, data visualization practices in newsrooms have been studied, in this sense, studies on productive routines, data visualization proposals in digital news media, visualization design and interaction techniques stand out. Finally, studies on visual analytics, challenges and decision making have also been carried out

Big Data: challenges, opportunities and Cloud based solutions

We are living in an era of information explosion. There are challenges with large and complex amount of data generated every day by social networks, wikis, blogs, emails, traffic system, bridges, airplanes and engine, satellites and weather sensors. 90% of current data in the world has been created in the last two years. Our smart planet becomes more and more intelligent. Besides the challenges posed by such vast amount of data including storage, search, sharing, analysis, and visualization, there are also much opportunities for the world as it becomes more and more digitalized. This study presents Big Data and highlights its key concepts and state-of-the-art implementation as well as research challenges and suggests research directions for future. IT log analytics, Fraud detection pattern, social media pattern and modeling and management patterns are some of opportunities. Hadoop is a cloud based and open source solution for Big Data Analytics which has been written by java. Hadoop solution is currently still immature. In this paper, three topics are suggested for research direction: Security issues in Big Data, context-aware information retrieval, and integrating ontology with Big Data

Understanding Digital Humanities

2012 In the first decade of the 21 st century, the researchers in the humanities and humanistic social sciences have gradually started to adopt computational and visualization tools. The majority of this work often referred as "digital humanities" has focused on textual data (e.g., literature, historical records, or social media) and spatial data (e.g., locations of people, places, or events visualization and computational analysis of large collections of images and video suitable for researchers in media studies, the humanities, and the social sciences who do not have technical background, and to apply these techniques to progressively large media data sets. Our second goal was theoretical -to examine existing practices and assumptions of visualization and computational data analysis (thus the name "Software Studies"), and articulate new research questions enabled by humanistic computational work with "big cultural data" in general, and visual media specifically. 3 This chapter draws on the number of my articles written since we started the lab where I discuss history of visualization, the techniques that we developed for visualizing large sets of visual media, and their applications to various types of media. 4 The reader is advised to consult these 1 For recent discussions of digital humanities, see David M

Affine-Invariant Outlier Detection and Data Visualization

A wealth of data is generated daily by social media websites that is an essential component of the Big Data Revolution. In many cases, the data is anonymized before being disseminated for research and analysis. This anonymization process distorts the data so that some essential characteristics are lost which may not be captured by methods that are not robust against such transformations. In this paper we propose novel algorithms, for two-dimensional data, for a recently discovered statistical data analysis measure, the Ray Shooting Depth (RSD) that provides an affineinvariant ranking of data points. In addition, we prove some complexity results and illustrate some of the desirable properties of RSD via comparisons with other similar notions. We develop an open-source data visualization tool based on RSD, and show its applications in distribution estimation, outlier detection, and 2D tolerance-region construction

Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings

Unstructured data in social media is as part of the “big data” spectrum. Unstructured data in Social media can provide useful insights into social phenomena and citizen opinions, both of which are critical to government policy and businesses decisions. Teachers of business intelligence and analytics commonly use quantitative data from sales, marketing, finance and manufacturing to demonstrate various analytics concepts in a business context. However, researchers have seldom used social media data to analyze social behavior and communication. In this study we aim to demonstrate an assessment structure for teaching social media analytics concepts with the goal of analyzing and interpreting social media content. We base this assessment on forum postings regarding two recent events: the Christchurch earthquake in New Zealand, and the Japanese earthquake and tsunami. The aim of the assessment is to discover social insights. We base the assessment structure on Cooper’s Analytics Framework to cover such concepts as term frequency (TF), term frequency–inverse document frequency (TFIDF), data visualization, sentiments and opinions analysis, the Nearest Neighbor K-NN classification algorithm, and Information Diffusion theory. We review how the students performed on the assignment that used this assessment, and we make recommendations for future studies

Big Social Data and GIS: Visualize Predictive Crime

Social media is a desirable Big Data source used to examine the relationship between crime and social behavior. Observation of this connection is enriched within a geographic information system (GIS) rooted in environmental criminology theory, and produces several different results to substantiate such a claim. This paper presents the construction and implementation of a GIS artifact producing visualization and statistical outcomes to develop evidence that supports predictive crime analysis. An information system research prototype guides inquiry and uses crime as the dependent variable and a social media tweet corpus, operationalized via natural language processing, as the independent variable. This inescapable realization of social media as a predictive crime variable is prudent; researchers and practitioners will better appreciate its capability. Inclusive visual and statistical results are novel, represent state-of-the-art predictive analysis, increase the baseline R2 value by 7.26%, and support future predictive crime-based research when front-run with real-time social media

New and Existing Approaches Reviewing of Big Data Analysis with Hadoop Tools

             الجميع متصل بوسائل التواصل الاجتماعي مثل) الفيس بوك وتويتر ولنكدان والانستغرام ...الخ) , التي تتولد من خلالها كميات هائلة من البيانات لا تستطيع التطبيقات التقليدية من معالجتها , حيث تعتبر وسائل التواصل الاجتماعي منصة مهمة لتبادل المعلومات والآراء والمعرفة التي يجريها العديد من المشتركين ,على الرغم من هذه السمات الأساسية ، تساهم البيانات الضخمة أيضًا في العديد من المشكلات ، مثل جمع البيانات ، والتخزين ، والنقل ، والتحديث ، والمراجعة ، والنشر ، والمسح الضوئي ، والتصور ، وحماية البيانات ... إلخ. للتعامل مع كل هذه المشاكل، ظهرت الحاجة إلى نظام مناسب لا يقوم فقط بإعداد التفاصيل، بل يوفر أيضًا تحليلًا ذا مغزى للاستفادة من المواقف الصعبة، سواء ذات الصلة بالأعمال التجارية، أو القرار المناسب، أو الصحة، أو وسائل التواصل الاجتماعي، أو العلوم، الاتصالات، البيئة... إلخ.يلاحظ المؤلفون من خلال قراءة الدراسات السابقة أن هناك تحليلات مختلفة من خلال Hadoop وأدواته المختلفة مثل المشاعر في الوقت الفعلي وغيرها. ومع ذلك، فإن التعامل مع هذه البيانات الضخمة يعد مهمة صعبة. لذلك فإن هذا النوع من التحليل يكون بكفاءه أكثر أكثر كفاءة فقط من خلال نظام Hadoop البيئي.، الغرض من هذه الورقة هو تحليل الأدبيات المتعلقة بتحليل البيانات الضخمة لوسائل التواصل الاجتماعي باستخدام إطار Hadoop لمعرفة أدوات التحليل تقريبًا الموجودة في العالم تحت مظلة Hadoop وتوجهاتها بالإضافة إلى الصعوبات والأساليب الحديثة لها للتغلب على تحديات البيانات الضخمة في المعالجة غير المتصلة وفي الوقت الفعلي. تعمل التحليلات في الوقت الفعلي على تسريع عملية اتخاذ القرار إلى جانب توفير الوصول إلى مقاييس الأعمال وإعداد التقارير. كما تم توضيح المقارنة بين Hadoop و spark.Everybody is connected with social media like (Facebook, Twitter, LinkedIn, Instagram…etc.) that generate a large quantity of data and which traditional applications are inadequate to process. Social media are regarded as an important platform for sharing information, opinion, and knowledge of many subscribers. These basic media attribute Big data also to many issues, such as data collection, storage, moving, updating, reviewing, posting, scanning, visualization, Data protection, etc. To deal with all these problems, this is a need for an adequate system that not just prepares the details, but also provides meaningful analysis to take advantage of the difficult situations, relevant to business, proper decision, Health, social media, science, telecommunications, the environment, etc. Authors notice through reading of previous studies that there are different analyzes through HADOOP and its various tools such as the sentiment in real-time and others. However, dealing with this Big data is a challenging task. Therefore, such type of analysis is more efficiently possible only through the Hadoop Ecosystem. The purpose of this paper is to analyze literature related analysis of big data of social media using the Hadoop framework for knowing almost analysis tools existing in the world under the Hadoop umbrella and its orientations in addition to difficulties and modern methods of them to overcome challenges of big data in offline and real –time processing. Real-time Analytics accelerates decision-making along with providing access to business metrics and reporting. Comparison between Hadoop and spark has been also illustrated

Visualización de datos y medios de comunicación: scoping review

[spa] La visualización de datos en periodismo tiene una aplicación intensiva como herramienta para narrar historias, sin embargo, las investigaciones y aportaciones teóricas sobre este campo no acompañan ni en volumen ni en importancia. Ante estas circunstancias nace este informe, que analiza la producción científica sobre medios de comunicación, visualización de datos y, más concretamente sobre la visualización de la información, infografías, narrativas visuales y visual Storytelling. Con ello, se pretende en primer lugar, conocer el estado del arte sobre esta disciplina, en segundo lugar, identificar nuevas tendencias, desafíos, tecnologías y protocolos y, en tercer lugar, determinar sus posibilidades y limitaciones en el campo de la comunicación y el periodismo. Los resultados señalan que los principales estudios desarrollados en los últimos años sobre esta disciplina se han centrado principalmente en la infografía, el periodismo de datos, el periodismo estructurado, la alfabetización mediática sobre la interpretación del lector sobre los datos visuales, las propuestas de visualización en cibermedios y la analítica visual. Respecto a las tecnologías y herramientas de visualización de datos se han realizado estudios muy interesantes sobre Big data y medios de comunicación, y más específicamente sobre software y sobre creación de herramientas de análisis visual. Adicionalmente, se han estudiado las prácticas de visualización de datos en las redacciones periodísticas, en este sentido, destacan los estudios sobre rutinas productivas, propuestas de visualización de datos en cibermedios, diseño de visualizaciones y sobre técnicas de interacción.[eng] Data visualization in journalism today has an intensive application as a storytelling tool. However, research and theoretical contributions in this field do not match it in volume or importance. Given these circumstances, the aim of this report is to analyze the scientific production on data visualization in news media; and, more specifically, on information visualization, infographics, visual narratives and visual storytelling. With this, the aim is, firstly, to know the state of the art on the discipline of visualization, secondly, to identify new trends, challenges, technologies and protocols and, thirdly, to determine its possibilities and limitations in the field of communication and journalism. The results indicate that the main studies developed in recent years on this discipline have focused mainly on infographics, data journalism, structured journalism, media literacy on the reader's interpretation of visual data, visualization proposals in digital news media and visual analytics. Regarding data visualization technologies and tools, very interesting studies have been carried out on Big data and media, and more specifically on software and on the creation of visual analysis tools. In addition, data visualization practices in newsrooms have been studied, in this sense, studies on productive routines, data visualization proposals in digital news media, visualization design and interaction techniques stand out. Finally, studies on visual analytics, challenges and decision making have also been carried out.Este trabajo forma parte del proyecto "Parámetros y estrategias para incrementar la relevancia de los medios y la comunicación digital en la sociedad: curación, visualización y visibilidad (CUVICOM)”. PID2021-123579OB-I00 (MICINN), Ministerio de Ciencia e Innovación (España). Actividad financiada por la Unión Europea-NextGenerationEU, Ministerio de Universidades y Plan de Recuperación, Transformación y Resiliencia, mediante convocatoria de la Universidad Pompeu Fabra (Barcelona)