Wolfram Data Drop
The Internet of Things is more or less synonymous with data. Devices and users generate, monitor, and collect data in just about every way imaginable; but when it comes time to make the data useful through analysis and presentation, there are really only two choices: Build the software and systems from scratch, or adopt a third-party platform.
Last week Stephen Wolfram, creator of the Wolfram Alpha “knowledge engine” and related computer science tools, announced a new option for dealing with data. The Wolfram Data Drop is an open system for storing, analyzing and sharing data, which creates a sort of hybrid between the DIY and ready-made approaches.
The central concept is the Databin, a cloud-hosted repository for data from one or more sources (which could be devices, web services, etc.). Each Databin has a unique URL, which serves as both a management dashboard for the bin’s owner and as an identifier for accessing the data inside. Point a device or other source toward the bin, and its data will show up there in real time -- or as quickly as the device sends updates. Bins can be public or private depending on what the owner wants to do with the data.
Each Databin has a bit of semantic information associated with it, which tells it how to interpret incoming data so that, say, temperatures don’t get mixed up with time stamps. This also helps homogenize the data, which means information that comes from different sources or is stored in different formats can be combined, compared, and otherwise manipulated together.
Data Drop is integrated with the rest of the Wolfram family of computational tools for analyzing and displaying data. Databins are automatically searchable through Wolfram Alpha, which provides metadata like the bin owner, the sources, and raw data feeds. The Wolfram Data Framework handles semantics and keeps the units consistent. And a few lines of code in the Wolfram Language can turn the contents of a Databin into a wide array of visualizations, from scatter plots and histograms to 3-D image cubes or heatmaps. That makes it easy to extract the data so it can be displayed on web pages, integrated into apps, called through APIs, tied into automation tools, or used in myriad other ways.
Learn more from Mr. Wolfram himself at his blog.
to the future (PDF)
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