Retail
Data analysis

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. Nowadays data science is a key point for the retail industry. The main tasks in the retail industry that require high-performance data analytics are Recommendation engines, Market basket analysis, Warranty Analytics, Price optimization, Inventory management, Location of new stores, Customer sentiment analysis, Merchandising, Lifetime value prediction and Fraud detection. In all these tasks having supper efficient text processing units are necessary because the main data type that is being generated in the retail industry is unstructured text data. Grovf offers it's text processor engines which is the fastest in the market allowing retail companies to achieve dozens of times faster speed when analyzing unstructured text data while saving the TCO of datacenter 3 times less.

 


User Sentiment Analytics

User Sentiment Analytics

The promise of artificial intelligence (AI) is to make work and life more productive. But to do so, AI needs to better understand humans, which are the most complex organisms on Earth. A significant element of AI’s limitations, to date, is understanding humans and, more specifically, human emotion. In the past few years, however, accelerated access to data (primarily social media feeds and digital video), cheaper compute power, and evolving deep learning combined with natural language processing (NLP) and computer vision are enabling technologists to watch and listen to humans with the intention of analyzing their sentiments and emotions. A better understanding of emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our needs.

Emotion and sentiment analysis is complex because emotion is complex and not very well understood. Emotion can be deceptive and expressed in multiple ways: in our speech intonation, the text of the words we say or write, our facial expressions, body posture, and gestures. These factors create variables in emotion analysis confidence scoring, which must be overcome for most sentiment and emotion analysis use cases to come into full bloom. Despite these challenges, the market for sentiment and emotion analysis has begun to expand. Tractica has identified seven use cases where significant direct software revenue will be generated through 2025: customer service, product/market research, customer experience, healthcare, automotive, education, and gaming.

This Tractica report examines the market and technology issues surrounding sentiment and emotion analysis and provides 9-year forecasts for software, hardware, and revenue supporting these applications. The report covers the ways in which sentiment and emotion analysis will be used across multiple channels in seven key use cases: customer service, product/market research, customer experience, healthcare, education, automotive, and gaming. It presents profiles for key industry players throughout the ecosystem. The study also presents global market sizing and forecasts for sentiment and emotion analysis, segmented by region, covering the period from 2016 through 2025.

Key Questions Addressed:

- What is the current state of the sentiment and emotion analysis market and how will it develop over the next decade?
What are the key use cases that will drive greater sentiment and emotion analysis adoption?
- What are the key drivers of market growth, and the key challenges faced by the industry, in each world region?
- Who are the key players in the market, what is their competitive positioning, and which ones are poised for the greatest success in the years ahead?
- What is the size of the sentiment and emotion analysis market opportunity?

Who Needs This Report?

- Artificial intelligence technology companies
- Semiconductor and component companies
- Customer-focused enterprises and solution providers
- Healthcare providers and technology vendors
- Automotive technology companies
- Market research, advertising, brand strategy, and marketing companies
- Government agencies
- Investor community


MongoDB Acceleration using Grovf's MonetX Platform

INTRODUCTION

Databases provide a wealth of functionality to a wide range of applications. Yet, there are tasks for which they are less than optimal, for instance when processing becomes more complex or the data is less structured. As data is exploding exponentially only CPU based systems no longer provide real-time insights to businesses in a cost-effective way. At Grovf we designed a Monet – A FPGA based smart memory extension for near memory data processing. Monet implemented on top of Xilinx’s Alveo U50 acceleration card and once plugged into server’s PCIe bus acts as a standard RAM memory for the Linux operating system with in-memory compute API capability. 

 

KEY BENEFITS

- 10GB/s, 2TB RAM memory on a single PCIe slot
- Network accessible memory
- In-memory data computing
- RAM Memory extension without increasing number of servers’ sockets
- Linux compatible 
 

SOLUTION OVERVIEW

MonetX acts as a standard RAM memory once connected to the server’s PCIe slot. With in-memory computing capability, it provides a simple API to host layer for easy utilization of the functions. Based on the Monet smart memory extension MongoDB has been accelerated 3.5X for all stages of data aggregation.

 

SOLUTION DETAILS

MongoDB acceleration is based on Grovf’s MonetX acceleration platform, which is an FPGA based smart memory extension for near memory data processing. The operating system recognizes MonetX as a standard memory extension which also provides high-performance computing cores API for the host layer. Data can be stored to MonetX memory extension, just like into any other memory connected to the server. Applications than can initiate different processing on the data stored into the MonetX platform directly running on FPGA, where data resides also. MonetX supports many high-performance computing cores such as Regular Expression processing, Search/Sort processing, Data compression/decompression, Statistical Data processing algorithms, etc. MongoDB performance has been boosted 3.5X only using the MonetX acceleration platform as a high bandwidth memory extension for standard server architecture without using any build-in high-performance computing cores in the FPGA. This leads to zero code change in the application(MongoDB) side and provides 3.5X acceleration. More acceleration for the MongoDB and any other application can be achieved using build-in accelerated computing cores in the FPGA residing near memory.

 

 

 

MongoDB Acceleration using Grovf's MonetX Platform