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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Big data | 7/10 | https://en.wikipedia.org/wiki/Big_data | reference | science, encyclopedia | 2026-05-05T09:53:36.506913+00:00 | kb-cron |
=== Science === The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.99995% of these streams, there are 1,000 collisions of interest per second. As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication. If all sensor data were recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times more than all the other sources combined in the world. The Square Kilometre Array is a radio telescope built of thousands of antennas. It is expected to be operational by 2024. Collectively, these antennas are expected to gather 14 exabytes and store one petabyte per day. It is considered one of the most ambitious scientific projects ever undertaken. When the Sloan Digital Sky Survey (SDSS) began to collect astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy previously. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2020, its designers expect it to acquire that amount of data every five days. Decoding the human genome originally took 10 years to process; now it can be achieved in less than a day. The DNA sequencers have divided the sequencing cost by 10,000 in the last ten years, which is 100 times less expensive than the reduction in cost predicted by Moore's law. The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster. Google's DNAStack compiles and organizes DNA samples of genetic data from around the world to identify diseases and other medical defects. These fast and exact calculations eliminate any "friction points", or human errors that could be made by one of the numerous science and biology experts working with the DNA. DNAStack, a part of Google Genomics, allows scientists to use the vast sample of resources from Google's search server to scale social experiments that would usually take years, instantly. 23andme's DNA database contains the genetic information of over 1,000,000 people worldwide. The company explores selling the "anonymous aggregated genetic data" to other researchers and pharmaceutical companies for research purposes if patients give their consent. Ahmad Hariri, professor of psychology and neuroscience at Duke University who has been using 23andMe in his research since 2009 states that the most important aspect of the company's new service is that it makes genetic research accessible and relatively cheap for scientists. A study that identified 15 genome sites linked to depression in 23andMe's database lead to a surge in demands to access the repository with 23andMe fielding nearly 20 requests to access the depression data in the two weeks after publication of the paper. Computational fluid dynamics (CFD) and hydrodynamic turbulence research generate massive data sets. The Johns Hopkins Turbulence Databases (JHTDB) contains over 350 terabytes of spatiotemporal fields from Direct Numerical simulations of various turbulent flows. Such data have been difficult to share using traditional methods such as downloading flat simulation output files. The data within JHTDB can be accessed using "virtual sensors" with various access modes ranging from direct web-browser queries, access through Matlab, Python, Fortran and C programs executing on clients' platforms, to cut out services to download raw data. The data have been used in over 150 scientific publications.
=== Sports === 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. Future performance of players could be predicted as well. Thus, players' value and salary is determined by data collected throughout the season. In Formula One races, race cars with hundreds of sensors generate terabytes of data. These sensors collect data points from tire pressure to fuel burn efficiency. Based on the data, engineers and data analysts decide whether adjustments should be made in order to win a race. 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.
=== Technology === As of 2013, eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising. Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux-based and as of 2005 they had the world's three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB. Facebook handles 50 billion photos from its user base. As of June 2017, Facebook reached 2 billion monthly active users. Google was handling roughly 100 billion searches per month as of August 2012. A majorly renowned example of the practice of big data is Amazon. Amazon utilizes data analytics to drive its system for recommendations. Amazon has great success from the portion of sales that derives from the "recommended" section with personalized recommended items to shop.
=== COVID-19 === During the COVID-19 pandemic, big data was raised as a way to minimise the impact of the disease. Significant applications of big data included minimizing the spread of the virus, case identification and development of medical treatment. Governments used big data to track infected people to minimise spread. Early adopters included China, Taiwan, South Korea, and Israel.