GROVF
Big Data Computing - Accelerated
  • Cyber Security

    Machine generated log data is growing exponentially making it extremely hard for cybersecurity software to provide real-time defense for the infrastructure. The software needs to analyze Terabytes of data every day generated by servers, applications, and firewalls. With Grovf massively parallel FPGA cores companies analyze and prevent the cyber threat 15x faster, saving critical time for organizations to detect a penetration and counteract almost in real-time. 

     

  • Financials

    In an industry where milliseconds matter and where insight directly equates to money, faster analytics offer a distinct competitive advantage. Grovf makes it possible for financial organizations to derive insights and make predictions from vast volumes of complex and streaming data in milliseconds. Use Grovf truly real-time analytics demands including fraud analysis, risk management, algorithmic trading, and high-frequency trading.

  • Retail

    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 retail industry. The main tasks in 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 task having supper efficient text processing units are necessary because the main data type that is being generated in retail industry are 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 analizing unstructured text data while saving the TCO of datacenter 3 times less.

Technology

10x

Faster Insights

Real Time Infrastructure

3x

Save on TCO

2x-4x

Power Saving

Use cases

Security Log Analytics

Implementing effective cybersecurity measures is particularly challenging today because there are more devices than people, and attackers are becoming more innovative.

Cybersecurity brings dual challenge of low-latency detection and remediation of advanced threats, and batch analysis of log data from servers, firewalls, applications and security systems. Considering how fast new threats and attacks emerge, Big Data performance and the use of new types of software and hardware accelerators is becoming more critical.

Technology is essential to giving organizations and individuals the computer security tools needed to protect themselves from cyber attacks. Three main entities must be protected: endpoint devices like computers, smart devices, and routers; networks; and the cloud.

Grovf's text processing FPGA cores and Open source software SDK provide a effortless way to use powerful FPGA devices for vast amount of security log analysis.

FPGA's powered with Grovf's Regex, Exact Search and Similiarity Search functions provide to organizations to analize hundreds of megabytes of data in real-time and detect security alerts. 

Financial Fraud/Risk Analytics

A fraud risk assessment is an essential element of an organization’s fight against fraud.

The topic of risk continues to be of critical importance across financial services segments. While there are many forms of risk, the most common form of risk across all financial segments is surrounding cybercrime and fraud. There is also a post-financial crisis regulatory aspect of risk management that forces lenders to know precisely how much capital they need in reserve. Keep too much and you tie up capital unnecessarily, lowering profit. Keep too little and you run afoul of Basel III regulations.


There is great promise in new big data and machine learning technologies to enable lenders to tap into an ever-deepening pool of new data to analyze all aspects of risk and fraud. Identifying risk and fraud can require huge data volumes and large compute clusters which are typical of modern big data systems.

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 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

In 2018, there was generated more data than in the entire previous human history. Current processors have reached their physical limit and are not capable to provide the desired business value.

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