Custom applications for big data analysis
The goal of custom application development for big data analysis is to reduce the time to decision or action. Traditional software manufacturers will be reluctant to bring new technologies as big data develop as a science and a market. A big data infrastructure has little value if there are few opportunities to decide or act on due to a lack of analysis capabilities relevant to the business area.
We'll now look at some further possibilities for those of us who might need unique big data analysis applications.
R environment
The "R" environment is based on Bell Laboratories' "S" statistics and analysis language developed in the 1990s. It is maintained by the GNU project and licensed under the GNU General Public License. Many users of S and R have made significant contributions to the fundamental system over the years, increasing and expanding its capabilities.
R is an integrated set of software tools and technologies for developing custom applications that help with data processing, calculation, analysis, and visual display.
R is a platform for creating interactive big data analysis methodologies. It has grown quickly, and many packages have been added to it. It's ideal for one-off, unique applications that analyze large amounts of data.
Google Prediction API
The Google Prediction API is an example of a new class of big data analysis application tools on the horizon. It's available on the Google Developers website, and it's well-documented, with a variety of ways to access it using various programming languages. It's free (with certain limits) for the first six months to help you get started. Following that, licensing is relatively limited and project-based.
The Prediction API is straightforward. It searches for patterns and compares them to existing prescriptive, authoritarian, or other patterns. It "learns" while conducting its pattern matching. It becomes more clever the more you use it. What could you "learn" if you used the Prediction API?
Assume you wanted to learn more about consumer behavior. You might wish to look for specific activity patterns in postings from Facebook, Twitter, Amazon, and Foursquare. If you're a consumer goods company, you could wish to recommend new or existing products based on social media data. If you're a Hollywood studio, you might want to let fans know about a new film starring one of their favorite actors.
The Prediction API allows you to forecast (or even promote) future behaviors by evaluating habits and previous acts.
Prediction is a RESTful API that supports .NET, Java, PHP, JavaScript, Python, Ruby, and many other languages. Google also provides scripts and a R client library for accessing the API. Predictive analysis is one of the big data's most powerful potential capabilities, and the Google Prediction API is a great way to build custom apps.
Information Flow of Prediction API (source: objectcomputing.com)
Many new custom application tools will be brought to the market as big data evolves. Some will be similar to R, while others (such as the Google Prediction API) will be introduced as APIs or libraries that programmers may use to create new ways to calculate and analyze big data. Many people in the real world do not have access to software engineers who can create unique applications. Fortunately, there are various options currently and on the horizon that you may employ to meet the demands of analysis users.
Features of Google Prediction API
A numeric or categorical value can be predicted using the Google Prediction API using data from a training set. These capabilities allow you to do everything from spam detection to recommendation engines without constructing your model.
The following is a sample of use cases that can be constructed using Google's Prediction API:
- Predict future patterns based on a set of historical facts.
- Determine whether or not an email is a spam.
- Recommend a product/movie to a person based on their shared interests.
- Determine whether a user's credit history will cause them to default.
- Using tagged sensor datasets, detect activity from cellphones.
The Prediction API performs the following specific actions on a given labeled dataset, given a new item:
- Predict a numeric value for that item using similar-valued examples from the training data (regression)
- Given a group of similarly classed items in its training data, choose the category that best characterizes it (classification)
The ability to forecast a world state parameter (target label value) for an unknown example based on previous labeled data instances is the end goal for all of the applications mentioned above. Using Google's fast and dependable computer capabilities, the Prediction API will construct appropriate models.
We are done with the blog. Let's move to faqs.
Frequently Asked Questions
What is Big Data?
Larger, more complicated data collections, especially from new data sources, are referred to as big data. Because these data sets are so large, typical data processing technologies can't handle them.
What are the characteristics of big data?
Big data has three characteristics: diversity, velocity, and volume. Diversity refers to the sources from which the data is received, and velocity refers to the rate of processing the data. Volume is the amount of data generated.
What is google prediction API?
The Google Prediction API is an example of a new big data analytics software class.
Give some real-life benefits of big data?
Industries have seen tremendous development due to the rise of Big Data.
Example: Banking, Manufacture, Technology, Consumers.
What are some use cases of Google prediction API?
The following are some use cases of Google's Prediction API: predicting future trends from a historical series of data, determining whether a given email is spam, etc.
Conclusion
In this article, we have extensively discussed Google Prediction API. We start with a brief introduction to custom applications for big data analysis and then google prediction API.
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