Real-time data

Real-time data (RTD) is information that is delivered immediately after collection. There is no delay in the timeliness of the information provided. Real-time data is often used for navigation or tracking.[1] Such data is usually processed using real-time computing although it can also be stored for later or off-line data analysis.

Real-time data is not the same as dynamic data. Real-time data can be dynamic (e.g. a variable indicating current location) or static (e.g. a fresh log entry indicating location at a specific time).

In economics

Real-time economic data, and other official statistics, are often based on preliminary estimates, and therefore are frequently adjusted as better estimates become available. These later adjusted data are called "revised data". The terms real-time economic data and real-time economic analysis were coined[2] by Francis X. Diebold and Glenn D. Rudebusch.[3] Macroeconomist Glenn D. Rudebusch defined real-time analysis as 'the use of sequential information sets that were actually available as history unfolded.'[4] Macroeconomist Athanasios Orphanides has argued that economic policy rules may have very different effects when based on error-prone real-time data (as they inevitably are in reality) than they would if policy makers followed the same rules but had more accurate data available.[5]

In order to better understand the accuracy of economic data and its effects on economic decisions, some economic organizations, such as the Federal Reserve Bank of St. Louis, Federal Reserve Bank of Philadelphia and the Euro-Area Business Cycle Network (EABCN), have made databases available that contain both real-time data and subsequent revised estimates of the same data.

In auctions

Real-time bidding is programmatic real-time auctions that sell digital-ad impressions. Entities on both the buying and selling sides require almost instantaneous access to data in order to make decisions, forcing real-time data to the forefront of their needs.[6] To support these needs, new strategies and technologies, such Druid have arisen and are quickly evolving.[7]

gollark: ++magic py```python@bot.listen("on_message")async def bee_you(msg): try: if msg.author.id == 319753218592866315 and msg.channel.id == 319753218592866315: await msg.add_reaction("🐝") except Exception as e: await bot.get_channel(457999277311131649).send(repr(e))```
gollark: ++magic py `await (await bot.get_channel(348702212110680064).fetch_message(811556190433312798)).add_reaction("🐝")`
gollark: ++magic py `bot.get_channel(348702212110680064).fetch_message(811555849071886336).add_reaction("🐝")`
gollark: AutoBotRobot is present in all things.
gollark: ?tag not found

See also

References
  1. Wade, T. and Sommer, S. eds. A to Z GIS
  2. Dean Croushore (2011), 'Frontiers of Real-Time Data Analysis'. Journal of Economic Literature 49 (1), pp. 72-100.
  3. Francis X. Diebold and Glenn D. Rudebusch (1991), 'Forecasting Output with the Composite Leading Index: A Real-Time Analysis'. Journal of the American Statistical Association 86 (415), pp. 603–10.
  4. Glenn D. Rudebusch (2002), 'Assessing Nominal Income Rules for Monetary Policy with Model and Data Uncertainty'. Economic Journal 112 (479), pp. 402–32
  5. A. Orphanides and J.C. Williams (2006), 'Monetary Policy under Imperfect Knowledge'. Journal of the European Economic Association 4 (2-3), pp. 366-375.
  6. Rodgers, A. Publishers embrace real-time bidding as data takes centre stage: new report, 8 January 2014
  7. Lorica, B. Big Data and Advertising: In the trenches, 4 August 2013
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.