Colin Ware, in his book Information Visualization. (Ware, 2013) Better decisions are made when backed by information reducing the uncertainty rather than the intuitive approach. It becomes overwhelming when the information comes from various sources in different formats presented in a variety of ways. The opportunity to enhance the situational awareness and aid decision-makers increases if such information can be visually represented effectively. Visualization is often considered to be an effective
What is data visualization Data visualization is the representation of data in a visual context so that people understand the data better. Some of the patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. Today’s data visualization tools are more than just charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways like infographics, dials and gauges, geographic maps
draw us a picture. The signi cance of data visualization is very high in order to have a good awareness of our network system. A well represented visualization solves the visibility problem for network and system administrators by e ectively ex- pressing the status of the environment we are dealing with, enabling network status analysis in static and real-time data, and making visual link graphs and tree maps any laymen is able to utilize. Good visualization can aid any number of critical measures
Chart Data Visualization for Businesses A picture is worth a thousand words. The ability to graphically represent your business data gives you the power to make informed business decisions quickly. (Microsoft.com, 2002) This representation must be visually appealing and easy to understand. By keeping it simple, it allows the broadest number of users to interpret the data, gain insights as to its meaning and facilitate communication on the data ultimately to solve the company¡¦s problem. Data visualization
Introduction Data science: An art of interpretation of the data and bringing insights from the data. It is also a study of observations and interpretation in any form. Data visualization: Representation of the data. Data scientists need tools to deal with the data. What best value can be brought out of it? How can it be broken down? How one parameter is correlated to other? All these questions are answered with one of the solutions - Data Visualization. Best example on our day today basis is
identified two main sources to assist in data analyzing which are business intelligence (BI) and data visualization. Business intelligence is a technological process enabling users to present the maximum of data in order to make better decisions. Its advantage is to present all sorts of information coming both internal and external sources combined into dashboard that would raise the level of information during the decision making process. Data visualization is a process to assist decision makers in
TREND OF BIG DATA The 21st century has brought with itself an explosion of data and increasing technological ability of our generation to collect, analyse and manipulate enormous amounts of data is what big data is all about. Being a student of a reputed b-school, big data, its analysis and emerging trends across the globe has definitely been a topic of consideration. The basic question is data analysis and big data one and the same? Well, the difference lies in the enormity of the data size. Huge
Statistics is the universal language behind data-enabled decision making and is widely used in our daily lives. Statistical analyses’ applications range from Google’s development choices to political parties’ campaign strategies. With the emergence of Data Science, Sports Analytics has also become an important industry. Today, almost all professional sports organizations are using data analytics to guide decision making on everything from player selection to marketing technique. As a result, there
There are many ways to handle the organizational performance data and visualize them for an effective decision making. For example, in the web analytics, it helps to answer the critical queries like "how the website is performing with respects to our marketing objectives?" From a corporate’s perspective, a new visualization method such as Dashboards offer a quick way to view data and information. The end results may include variance comparisons, single metrics, geographical maps and graphical trend
be generating large amount of complex data ranging from different entities including patients , hospitals resources, disease records etc. Recently, there has been tremendous increase in the application of informatics to health care which is defined as science of information, the practice of information processing, and the engineering of information systems The growing adoption of information technologies in healthcare and the availability of more patient data and related healthcare variables provide
• Data Grid: Data grids primarily deal with providing services and infrastructure for distributed data-intensive applications that need to access, transfer and modify massive datasets stored in distributed storage resources [4]. 1.1.3 Basic grid model: The basic grid model generally composed of a number
Title: The Enormous Profit Potential of Digital Media Products Do you have any idea about what is so common between the racing game on your console, the weather app on your smart phone and the imaging tool used in a medical care facility? All these come under the category of digital media products. It is to be noted that they are not easy to build. Different teams of experts and specialists in digital media are required for building these products. However, prior to knowing the profit potential of
CHAPTER III COLOR DESCRIPTION AND EXTRACTION 3.1 INTRODUCTION Image retrieval is the process of handling large volume of image database in order to achieve the efficiency in identifying similar images over the retrieved results. In Image retrieval, a choice of various techniques is used to represent images for searching, indexing and retrieval with either supervised or unsupervised learning models. The color feature extraction process consists of two parts: grid based representative of color selection
Introduction: Big data is a hot topic in the Information Technology industry as it is a collection of data that describes the growth of the company, present in both structured and unstructured types. As the industry is dealing with large data, they are also concerned about the security of the data which is provided by big data security tools analytics. Big Data Security Analytics is a collection of security data sets which are large and complex and it becomes difficult to process using the traditional
difficult to analysis the big and huge amount of the datasets. The healthcare data consists of the medicines data like drug molecules and structures and clinical trials, environment factors related to the health, lab reports, health insurance, and global disease survey etc. The healthcare big data analysis is the three step process: 1. Preprocessing 2. Cleaning 3. Visualization According to paper [12] healthcare big data is analyzed by using the open source platform-Hadoop. Here we consider
language for technical computing. It combines computation, visualization and programming environment. Furthermore, MATLAB is a modern programming language domain. It has cynical data structures contains built-in editing and debugging tools and guides object-oriented programming. MATLAB has much superiority over conventional computer languages (e.g. C, FORTRAN) for determining technical problems. MATLAB is an interdependent system whose basic data element is an array that does not need dimensioning. The
Over the last so many years, a large amount of data has become available like a large amount of collections of photos, genetic information, and network traffic statistics, modern technologies and cheap storage facilities have made it possible to collect huge datasets. But can we effectively use all this data? The ever increasing sizes of the datasets make it imperative to design new algorithms capable of shifting through this data with extreme efficiency. Figure 1.1 The challenges include
Big Data in Healthcare and How You as Healthcare Provider Could Get Benefit of Big Data Amal Mohammed Alghamdi 434008372 Princess Nora University Collage of Medicine Big Data in Healthcare and How You as Healthcare Provider Could Get Benefit of Big Data "Big data is a term for data sets that traditional data processing application softwares are not able to deal with them and they are so large and complex so they are facing a lot of challenges such as, storage, analysis, search, capture, data curation
organization that choose to capitalize on it. Powerful big data and analytics solutions are now being used to analyze this explosion of information and fundamentally change the way that healthcare manage their daily operations. Big data as a concept is great. But healthcare sector that rely on big data to make strategic decisions are missing the point. In order to use data effectively. An economical and effective data management approach is to keep data where it is generated, leave it in its original
There are terabytes upon terabytes of data which are collected by companies to naturally serve their purpose of increasing their bottom line. To make sense of all of the large amount of data which is collected over time, there must be a system in place. Companies who want to be successful must have an effective way of analyzing the massive amounts of data they receive in order to understand the customers and appeal to them more. One way of organizing the raw data is the 10/90 rule. Avinash Kaushik,