Fuzzy Logix

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Fuzzy Logix
IndustryIT/Software/Hardware/Predictive Analytics/In-Database/GPU Analytics
Founded2007
FounderPartha Sen, Mike Upchurch
Headquarters
Charlotte, NC
,
USA
ProductsHigh Performance Analytics for Big Data (DB Lytix and FIN Lytix)

Analytics Accelerators (Saral and AdapteR) Analytics Consultancy and Services

Index Management Platform (FastINDX)
WebsiteAarkAI.com FastINDX.com

Introduction[edit]

Fuzzy Logix develops high -performance analytics solutions for Big Data. Fuzzy Logix offers in-database[1][2] and GPU-based analytics solutions built on comprehensive and growing libraries of over 600 mathematical, statistical, simulation, data mining, time series and financial models.

History[edit]

Fuzzy Logix was formed in 2007 by Partha Sen and Mike Upchurch who met while working at Bank of America and shared a goal of making analytics pervasive.[3] Kaushal Misra and Aashu Virmani joined in 2015, both from IIT Roorkee (same as Partha Sen) helped grow the company by providing expertise in Sales, Cloud, AI, Marketing, and Alliances. In 2008 Fuzzy Logix released DB Lytix, the first complete and commercially available library of in-database analytics. FIN Lytix was released in 2010 and was the first comprehensive library of in-database financial models. In 2010, Aperity OEM’d Fuzzy Logix models to run in their analytics and CPG software SaaS solutions. In 2011, Quest[4] (now Dell) released Toad for Data Analyst (Data Point) that included Fuzzy Logix's models running in MySQL. The company was started in Charlotte, NC, USA, where their headquarters are located today. Fuzzy Logix has offices in Richmond, VA, Cupertino, CA and in the UK and India and has reseller partners in Mexico, Sweden, Japan and China. Now Fuzzy Logix conducts business through AarkAI LLC.

Software[edit]

Fuzzy Logix offers four software products DB Lytix and Fin Lytix are comprehensive libraries of in-database analytic models. The libraries leverage the user defined function (UDF) capability available in database platforms. The software is available on multiple database platforms. Since data movement from the database is minimized and database platforms are growing increasingly powerful, in-database models run 5X to 100X faster than models that use multi-tiered analytics platforms.

DB Lytix[edit]

Fuzzy Logix released the first comprehensive library of in-database models, DB Lytix in 2008. The library had been under development since 1998. The library includes mathematical, statistical, data mining, simulation and classification models.

Fin Lytix[edit]

Fuzzy Logix released the first comprehensive financial library FIN Lytix, in 2010. The library contains models for equity, fixed income, foreign exchange, interest rate and time series models that are used by the financial services industry for risk management, pricing and portfolio optimization.

FastINDX[edit]

In 2016 Fuzzy Logix realized that current tools and technology for Index creation or for Index operations are based on heavily manual and error prone processes which are ultimately a drag on the growth of the overall business.

In conjunction with one of Asia’s largest Index providers, Fuzzy Logix developed an automation tool with the mission to drive 10-100x efficiency in the process, allowing customers to focus on the high value, intellectual aspect of the business, and letting the software do the menial work. As a result, customers can rapidly grow from managing a few hundred indexes, to several thousand indexes covering a larger geography with an exposure to various new asset classes – without adding any additional staff!

Supported Database Platforms[edit]

Aster Data,[5] Informix,[6] Netezza,[7][8] IBM PureData Systems, MySQL, ParAccel,[9] SQL Server,[10] Sybase IQ[11][12] and Teradata.

Industry Use[edit]

Fuzzy Logix solutions are effective in optimizing business process performance by utilizing mathematical modeling based risk management. Industries like Marketing, Healthcare, Insurance, Digital Media services, Financial Services (Investment and Retail Banking, Brokerage Houses, Stock Exchanges, Hedge Fund Management) are some examples. Same techniques and superior performance can potentially be utilized much more broadly for solving complex problems in other industries and organization (Government programs, Educational institutions, Research) when there is a need for running Analytics on Big Data using complex models. Solutions are derived from Predictive modeling of behavior in assessing risk and modeling an optimal system.

References[edit]

  1. ^ n-Database Analytics: The Heart of the Predictive Enterprise
  2. ^ Decision Management Solutions
  3. ^ "Operational Analytics: Putting Analytics to Work in Operational Systems". Archived from the original on 2012-08-05. Retrieved 2013-04-04.
  4. ^ "Quest Teams with Fuzzy Logix to Deliver a Cost-effective Solution for Predictive Modeling". Archived from the original on 2013-11-30. Retrieved 2013-04-04.
  5. ^ Aster Data's Data-Analytics Server and Analytic Power of SQL-MapReduce Draws New Partners
  6. ^ Fuzzy Logix and IBM Unveil In-Database Analytics for IBM Informix
  7. ^ New Product for In-Database Analytics
  8. ^ Fuzzy Logix and Netezza Partner
  9. ^ ParAccel Jumps On Analytics Bandwagon
  10. ^ Fuzzy Logix Unveils a New Product and a New Partner: DB Lytix(TM) In-Database Analytics for Microsoft
  11. ^ Fuzzy Logix Announces Partnership with Sybase for Introducing In-database Analytics on the World’s Leading Column-oriented Analytics Server
  12. ^ Enabling In-Database Analytics with Sybase IQ

External links[edit]

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