مقاله انگلیسی رایگان در مورد تجزیه و تحلیل کلان داده و هوش تجاری در صنعت – اسپرینگر 2017

The pervasive nature of digital technologies as witnessed in industry, services and everyday life has given rise to an emergent, data-focused economy stemming from many aspects of human individual and business activity. The richness and vastness of these data are creating unprecedented research opportunities in several fields including urban studies, geography, economics, finance, and social science, as well as physics, biology and genetics, public health and many others. Big data is the term for a collection of large and complex datasets from different sources that are difficult to process using traditional data management and processing applications. Big data is the description of a large amount of either organized or unorganized data that is analyzed to make an informed decision or evaluation. The data can be taken from a large variety of sources including browsing history, geolocation, social media, purchase history and medical records. Big data consists of complex data that would overwhelm the processing power of traditional simple database systems (Hung 2016). There are three main characteristics associated with big data (Dave 2013): & Volume is a characteristic used to describe the vast amounts of data that is utilized by big data. The amounts of data usually range from gigabytes to yottabytes. The big data should be able to handle any amount of data even as it expectedly grows exponentially. & Variety is a characteristic used to describe the many different types of data sources that are used as part of a big data analytics system. There are multiple data storage formats that are utilized by computer devices throughout the world. There are structured data such as databases, .csv, video, Short Message Service (SMS) and excel sheets. The unstructured data could be in the form such as handwritten notes. All the data from these sources would ideally be used to a big data analytics system. & Velocity is a characteristic used to describe the speed at which data is generated. It is also used to describe the speed at which the generated data is processed. With a click of a button, an online retailer can quickly view large data about a certain customer. Velocity is also important to ensure that data is current and updated in real-time, thus allowing the system to perform the best it can. This speed is essential as real-time data generation helps organizations speed up operations processes; which can save organizations a large amount of money.