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To illustrate the power of text analytics in terms everyone can understand, the Pivotal Data Science team recently collected Twitter data from the New York Democratic and Republican primaries and used them as a backdrop to illustrate three common scenarios where Natural Language Processing (NLP) can help businesses, including a basic understanding of word embeddings, sentiment analysis, and topic analysis.
Pivotal has extensive experience with platforms for the connected car. In this post, three of our data science experts give a deep dive on how these architectures work, using a predictive maintenance application. Besides an explanation of the problems, data sources, processing workloads, feature creation approaches, and machine learning algorithms, they offer several sets of sample code.
In part one of this blog series, Pivotal’s Gautam Muralidhar and Srivatsan Ramanujam introduced the task of edge detection, an important problem in developing computer vision algorithms. In the previous post, they demonstrated that a sample native application can be seamlessly integrated and scaled up for data parallel problems on HAWQ, Pivotal's SQL-on-Hadoop solution. In this second part of the series, Muralidhar and Ramanujam demonstrate how the same task can be achieved via the PL/C user defined function.
Learn how one university is using data science to understand students and identify the factors leading to student success and retention by analyzing structured and unstructured data. In this post, one of Pivotal's Senior Data Scientists explains the background, approach, solution, models, and outcomes of a recent project.
In this two-part series, one of Pivotal’s Senior Data Scientists provides an overview of how to achieve part-of-speech tagging on tweets at scale. In this second part of the series, using code examples, we dive deeper into PL/Java UDFs and write a wrapper on ArkTweetNLP to perform POS tagging at scale with Pivotal’s MPP data platform.
In this two-part series, open source code is shared as one of Pivotal’s Senior Data Scientists provides an overview of part-of-speech tagging on tweets, explains the purpose and value, dives into the challenges with social language processing, and shows how to use open source Java tools on PostgreSQL as well as Pivotal’s massively parallel processing data platforms to increase scale.
It’s our final day of work and fun at Acadia National Park. So far, we’ve learned about the research problems of interest in relation to climate change, spent two days as citizen scientists, collecting data from bird to barnacles, and grew an understanding the challenges in that process. We also talked about how data science and a climate data lake could aid researchers in measuring, analyzing and predicting the impact of climate change on the plant and animal species in the park. Today, we fleshed out a plan in making those ideas more concrete.
In posts one and two, we covered the background of the climate-based research in Acadia National Park, the goals of our project, and the specific ways scientists could use technology to improve the process. In this post, we share how sensors, data processing, models, and visualization tools are used, what scale could be provided with Pivotal technologies, and where citizen scientists could play a role in helping global climate change.
In the first post of this series, we gave the background on the climate-based research going on in Acadia National Park and our role in helping define the next generation of applications in the space. In part two, we explain how automated image capture of birds and tide pools could be processed on a data like and used to help scientists spend more time on the science and less time collecting and manipulating data.
Data science is being used to transform the way we measure the world around us, particularly in the area of climate change. In the first post of this series, Pivotal’s Senior Data Scientist, Srivatsan Ramanujam, shares a journal of his trip to Acadia National Park to work with a cross functional team from Pivotal, EMC, Earthwatch, and Schoodic Research Institute, improving the way they collect and analyze data within the context of a data lake.