Over eighty percent of all available data has a spatial component. There is increasing commercial interest in exploiting this spatial component, and a demand for the integration of spatial functionality within many diverse contexts. There are many application of spatial data. Traditionally it was used for mapmaking, cartography, digital photogrammetry but more recently it has been used for emergency response planning, urban development, location-based services, way finding and planning. There are many technological applications that use spatial data.
The pace at which data is being examined is high and companies are inclined towards hiring a team of experts for data analysis. How big data impacts social media? Imagine nearly 20 terabytes of data being gathered from sources like business records and data bases like Thomson and Bloomberg. Now multiply this amount of by four and imagine the volume that will be born. This large amount of data is extracted through social media site frequently.
Big data refer to massive data sets that have large and complex structure which is difficult to store, analyze and visualize for processing and results. The research for large amounts of data to reveal hidden patterns is known as Big Data Analytics (BDA). Big data is used to gain deeper insights and competitive advantage. Big data refer to two conditions, the technological challenge which is about dealing with data intensive domains such as high energy physics, astronomy or internet search. The second condition is dealing with social problems with the main focus being on cultural and environmental factors, when data about people is collected and obtained by organizations such as Facebook, Twitter, Google to mention a few.
In additionto analyzing huge amount of data, Big Data Analytics poses other unique challengesfor machine learning and data analysis, includes format variation of the raw data, trustworthiness of the data analysis, fast moving streaming data, noisy and poor quality data highly distributed inputsources, high dimensionality, scalability of algorithms, unsupervised and un-categorized data, limited supervised/labeled data, imbalanced input data, etc. Adequate data storage, data indexing or tagging, and fast information retrieval are other key problems in Big Data Analytics. Innovative data analysis and data management solutions are warranted when working with Big Data. For example, in a recent work we examined the high-dimensionality of bioinformatics domain data and investigated feature selection techniques to address the problem. A more detailed overview of Big Data Analytics is presented in “Big data
Let me use this article to explain what's behind the massive 'big data' buzz and demystify some of the hype. Basically, big data refers to our ability to collect and analyze the vast amounts of data we are now generating in the world. The ability to harness the ever-expanding amounts of data is completely transforming our ability to understand the world and everything within it. The advances in analyzing big data allow us to
This is because all the data are storing in in the storage through electronically. All the data can get through using the electronic devices. The big data is store on in memory computing that enable to store a large amounts of data. That organization does not need to find the data through manually that require a lot of time in retrieval process. Moreover, big data that store manual using the traditional process is hard to access because its will place in the huge storage that need to find one by one through paper.
Since, it encompasses wide range of activities, which most of time transcend factories or national boundary, complex interdependencies are built into it. As the power base continues to shift from companies towards customers, customer demands have gotten more complex. Companies are looking at Big Data analytics to revamp their supply chain, thereby using Big Data Analytics as a strategic lever. Companies are collecting vast amount of supply chain related data with help of technologies such as sensors, Barcode and GPS, Jacob House (2014). Big Data Analytics offers companies the ability to leverage on the enormous amounts of information driving their global supply chains, Harvard Business review, (2013).
integrating a variety of sources of data, including unstructured data, such as legal documentation, trusts, stock filings, corporate actions. Traditionally, they haven't been able to marry that type of information with structured data, and that's one of the big areas where organizations perceive that big data can help them. We believe that big data enables big questions. e.g. Take corporate actions.
That could include Web server logs and Internet click stream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things. Some people exclusively associate big data with semi-structured and unstructured data of that sort, but consulting firms like Gartner Inc. and Forrester Research Inc. also consider transactions and other structured data to be valid components of big data analytics
This is because Industrial Big Data is generated by automated equipment and processes, where the environment and operations are more controlled and human involvement is reduced to minimum. Although machines are more connected and networked, it is necessary pre-process the data before actually analyzing it. For example, due to communication issues and multiple sources, data from the system might be discrete and asynchronous and the data should be pre-processed to make them complete, continuous and synchronized. Moreover, since variables usually possess clear physical meanings, data integrity is of vital importance to the development of the analytical system. Low-quality data or incorrect recordings will alter the relationship between different variables and will have a catastrophic impact on the estimation accuracy.