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Gregory S Battas

from Fishers, IN
Age ~61
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Also known as:
Greg S Battas, Kelly A Battas
Phone and address:
11060 Desert Glen Dr, Fishers, IN 46037
(317) 842-3618
Related to:
Kelly A Battas, 61David A Bellon, 71Michael P Smith, 61Earl J Smith, 70Nancy K Smith, 86Carol A Smith, 82Ronald D SmithKelly A Battas, 62Jennifer L Craig, 53Carmen SmithK SmithClyde R Smith, 83
Connected to:
Jessy Y Battash, 35Deborah Battat, 46Marcia Battat, 86Mohammad J Battat, 57Paula J Battavio, 64Sherri L Battazzo, 44Danielle A Battcher, 40Patricia E Battcock, 70David A Batte, 54Marcus M Batte, 110

Gregory Battas Phones & Addresses

  • 11060 Desert Glen Dr, Fishers, IN 46037 (317) 842-3618
  • 12169 Sunrise Cir, Fishers, IN 46038
  • Gainesville, FL
  • 720 N College Ave, Indianapolis, IN 46202
  • 8664 Rhone Ter #3D, Indianapolis, IN 46250 (317) 845-1536
  • Noblesville, IN

Industries

Computer Software

Resumes

Resumes

Gregory Battas Photo 1

Cto Business Intelligence At Hewlett-Packard

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Location:
Indianapolis, Indiana Area
Industry:
Computer Software

Publications

Us Patents

Framework, Architecture, Method And System For Reducing Latency Of Business Operations Of An Enterprise

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US Patent:
6954757, Oct 11, 2005
Filed:
Sep 7, 2001
Appl. No.:
09/948927
Inventors:
Bahman Zargham - Sunnyvale CA, US
Gregory Battas - Indianapolis IN, US
Assignee:
Hewlett-Packard Development Company, L.P. - Houston TX
International Classification:
G06F017/30
US Classification:
707101, 707100, 707 10, 707 6, 709224, 709206, 717102
Abstract:
A framework, architecture, system and method are provided for reducing business transaction latencies thereby enabling an enterprise to run as a zero latency enterprise (ZLE). The present invention enables the enterprise to integrate its services, applications and data in real time. An enterprise running as a ZLE has a uniform global view of its past and current business transactions, including its customer interactions, internal business operations, supply chain management and more; and this uniform global view can be obtained from anywhere across the enterprise and be used for real-time decision making. Namely, an enterprise equipped to run as a ZLE is capable of integrating, in real time, its enterprise-wide data, applications, business transactions, operations and values. Consequently, an enterprise conducting its business as a ZLE exhibits superior management of its resources, operations and customer care.

Zero Latency Enterprise Enriched Publish/Subscribe

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US Patent:
7856420, Dec 21, 2010
Filed:
Dec 7, 2001
Appl. No.:
10/013091
Inventors:
Bahman Zargham - Sunnyvale CA, US
Gregory Battas - Indianapolis IN, US
Assignee:
Hewlett-Packard Development Company, L.P. - Houston TX
International Classification:
G06F 7/00
G06F 17/00
US Classification:
707627, 707628
Abstract:
Solutions for reducing latencies in enterprise operations and business processes adaptation particularly as related to enriched publish and subscribe are proposed by the present invention. These solutions are implemented in a zero latency enterprise (ZLE) framework that allows the enterprise to integrate its services, applications and data in real time. Namely, an enterprise equipped to run as a ZLE is capable of integrating, in real time, its enterprise-wide data, applications, business transactions, operations and values. An operational data store operates as an information broker between the applications such that applications publish messages to the central repository and subscribe to messages from the central, rather than exchange request-response messages directly with each other. Thus, enriched publish and subscribe improves information synchronization between applications and reduce the number of request-response messages the applications would otherwise exchange. Consequently, an enterprise conducting its business as a ZLE exhibits superior management of its resources, operations and customer care.

Enabling A Zero Latency Enterprise

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US Patent:
20020107864, Aug 8, 2002
Filed:
Sep 7, 2001
Appl. No.:
09/948928
Inventors:
Gregory Battas - Indianapolis IN, US
Bahman Zargham - Sunnyvale CA, US
International Classification:
G06F007/00
US Classification:
707/101000
Abstract:
Methods are provided for enabling an enterprise to run as a zero latency enterprise (ZLE) and for running an enterprise as a ZLE. Such methods allow the enterprise to integrate its services, applications and data in real time. Namely, an enterprise equipped to run as a ZLE is capable of integrating, in real time, its enterprise-wide data, applications, business transactions, operations and values. Consequently, an enterprise conducting its business as a ZLE exhibits superior management of its resources, operations and customer care.

Knowledge Discovery Through An Analytic Learning Cycle

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US Patent:
20030220860, Nov 27, 2003
Filed:
Apr 24, 2003
Appl. No.:
10/423678
Inventors:
Michael Heytens - Austin TX, US
Steven Carr - San Jose CA, US
Gregory Battas - Indianapolis IN, US
Philip Bosinoff - Ashland MA, US
Assignee:
Hewlett-Packard Development Company,L.P. - Houston TX
International Classification:
G06F017/00
G06N005/02
G06F017/60
G06F015/18
G06F017/30
G06F007/00
US Classification:
705/035000, 706/012000, 707/003000, 706/047000, 705/007000
Abstract:
Knowledge discovery through analytic learning cycles is founded on a coherent, real-time view of data from across an enterprise, the data having been captured and aggregated and is available in real-time at a central repository. Knowledge discovery is an iterative process where each cycle of analytic learning employs data mining. Thus, an analytic learning cycle includes defining a problem, exploring the data at the central repository in relation to the problem, preparing a modeling data set from the explored data, building a model from the modeling data set, assessing the model, deploying the model back to the central repository, and applying the model to a set of inputs associated with the problem. Application of the model produces results and, in turn, creates historic data that is saved at the central repository. Subsequent iterations of the analytic learning cycle use the historic data, as well as current data accumulated in the central repository, thereby creating up-to-date knowledge for evaluating and refreshing the model.

Data Skew Insensitive Parallel Join Scheme

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US Patent:
20090024568, Jan 22, 2009
Filed:
Jul 20, 2007
Appl. No.:
11/780843
Inventors:
Awny K. Al-Omari - Cedar Park TX, US
QiFan Chen - Austin TX, US
Gregory S. Battas - Fishers IN, US
Kashif A. Siddiqui - Austin TX, US
Michael J. Hanlon - Austin TX, US
International Classification:
G06F 7/16
G06F 17/30
US Classification:
707 2, 707E17002
Abstract:
A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Data Skew Insensitive Parallel Join Scheme

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US Patent:
20120117055, May 10, 2012
Filed:
Jan 23, 2012
Appl. No.:
13/356056
Inventors:
Awny K. Al-Omari - Cedar Park TX, US
QiFan Chen - Austin TX, US
Gregory S. Battas - Fishers IN, US
Kashif A. Siddiqui - Austin TX, US
Michael J. Hanlon - Austin TX, US
International Classification:
G06F 17/30
US Classification:
707714, 707E17017
Abstract:
A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Intelligent Lifecycle Management Of Analytic Functions For An Iot Intelligent Edge With A Hypergraph-Based Approach

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US Patent:
20230017701, Jan 19, 2023
Filed:
Sep 21, 2022
Appl. No.:
17/933934
Inventors:
- Spring TX, US
Saikat Mukherjee - Bangalore, IN
Gunalan Perumal Vijayan - Bangalore, IN
Sridhar Balachandriah - Bangalore, IN
Ashutosh Agrawal - Bangalore, IN
KrishnaPrasad Lingadahalli Shastry - Bangalore, IN
Gregory S. Battas - Fishers IN, US
International Classification:
H04L 41/16
H04L 43/045
H04L 43/091
H04L 67/125
H04L 41/22
G06N 20/00
H04L 41/0816
Abstract:
The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.

Intelligent Lifecycle Management Of Analytic Functions For An Iot Intelligent Edge With A Hypergraph-Based Approach

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US Patent:
20200112490, Apr 9, 2020
Filed:
Oct 4, 2018
Appl. No.:
16/152394
Inventors:
- Houston TX, US
SAIKAT MUKHERJEE - Bangalore, IN
GUNALAN PERUMAL VIJAYAN - Bangalore, IN
SRIDHAR BALACHANDRIAH - Bangalore, IN
ASHUTOSH AGRAWAL - Bangalore, IN
KRISHNAPRASAD LINGADAHALLI SHASTRY - Bangalore, IN
GREGORY S. BATTAS - Fishers IN, US
International Classification:
H04L 12/24
H04L 29/08
H04L 12/26
G06N 99/00
Abstract:
The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.
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Gregory S Battas from Fishers, IN, age ~61 Get Report