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The data have been used in over scientific publications. Sports[ edit ] Big data can be used to improve training and understanding competitors, using sport sensors.
It is also possible to predict winners in a match using big data analytics. Thus, players' value and salary is determined by data collected throughout the season.
These sensors collect data points from tire pressure to fuel burn efficiency. Besides, using big data, race teams try to predict the time they will finish the race beforehand, based on simulations using data collected over the season. The core technology that keeps Amazon running is Linux-based and as of [update] they had the world's three largest Linux databases, with capacities of 7. Amir Esmailpour at UNH Research Group investigated the key features of big data as the formation of clusters and their interconnections.
They focused on the security of big data and the orientation of the term towards the presence of different type of data in an encrypted form at cloud interface by providing the raw definitions and real time examples within the technology.
Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data. The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department's supercomputers.
The U. The project aims to define a strategy in terms of research and innovation to guide supporting actions from the European Commission in the successful implementation of the big data economy. Outcomes of this project will be used as input for Horizon , their next framework program. The findings suggest there may be a link between online behaviour and real-world economic indicators. The results hint that there may potentially be a relationship between the economic success of a country and the information-seeking behavior of its citizens captured in big data.
Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends. Hence, there is a need to fundamentally change the processing ways.
We will transform that triangle into a pyramid as we examine the specific goals, concrete measures, and the timing of the foundation and feedback information required at each level. See Figures , , and Figure Figure The Top of the Pyramid Decision makers at the upper levels of our organizations must look at the big picture. They are charged with setting long-term goals for the organization. Decision makers need to have a broad overview of their area of responsibility and not get caught up in the minutiae.
Figure Highly Summarized Measures The business intelligence utilized at this level needs to match these characteristics.
The measures delivered to these decision makers must be highly summarized. In many cases, each measure is represented, not by a number, but by a status indicator showing whether the measure is in an acceptable range, is starting to lag, or is in an unacceptable range. These highly summarized measures are known as key performance indicators. Definition: Key performance indicators KPIs are highly summarized measures designed to quickly relay the status of that measure.
They usually reflect the most vital aspects of the organization. For the full chapter, see the PDF.
To purchase a copy of the book, please go to Amazon. KPIs are used to provide these high-level decision makers with a quick way to determine the health of the essential aspects of the organization. KPIs are often presented as a graphical icon, such as a traffic light or a gauge, designed to convey the indicator's status at a glance.
We discuss KPIs in greater detail in Chapter 10 of this book.