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Anirudh Kondaveeti
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  • Palo Alto
  • United States
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Profile Information

My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/anirudhkondaveeti
Professional Status
Manager
Years of Experience:
6
Your Company:
Pivotal Software Inc
Industry:
Software
Interests:
Networking

Anirudh Kondaveeti's Blog

Data Science Applications in Semiconductor Manufacturing

Posted on October 22, 2015 at 11:13am 0 Comments

In this post, we will look at how data science can be used to improve mechanical and materials engineering in the semiconductor manufacturing industry by summarizing the work that Pivotal’s Data Science team did for a real-world customer.

 

The Objective

As in any manufacturing or…

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How To Use Multivariate Time Series Techniques For Capacity Planning on VMs

Posted on October 22, 2015 at 11:11am 0 Comments

Capacity planning is an arduous, ongoing task for many operations teams, especially for those who rely on Virtual Machines (VMs) to power their business. At Pivotal, we have developed a data science model capable of forecasting hundreds of thousands of models to automate this task using a multivariate time series approach. Open to reuse for other areas such as industrial equipment or vehicles engines, this technique can be applied broadly to anything where regular monitoring…

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Using Data Science to Combat Watering Hole Security Attacks

Posted on October 22, 2015 at 11:08am 2 Comments

In this blog post, we take a look at some work I did with my colleague Jin Yu to explain how data science techniques such as sequential pattern mining can detect coordinated network threats such as watering hole attacks. Watering hole attacks target a group of users in an organization by attacking the most popular websites among these users.

By examining outbound network traffic data, researchers can analyze potential threats after…

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How To Forecast Time Series Data With Multiple Seasonal Periods

Posted on September 22, 2015 at 3:30pm 0 Comments

Forecasting a time series data signal ahead of time helps to make better decisions around planning capacity and estimating demand. Applications reach into all areas of business from predicting website traffic to actual traffic on the road.

This post will outline the modeling steps involved in forecasting a time series sequence with multiple seasonal periods, including:

  • Multiple seasonality…

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