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Jay W Schiele

from Union Grove, WI
Age ~67
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Also known as:
Jay T Schiele, Tona J Schiele, Jay W Schicle
Phone and address:
145 Saint James Cir, Union Grove, WI 53182
(262) 878-9278
Related to:
Alice Fredericks, 72Ed F Schiele, 73
Connected to:
Alan M Schiele, 32Alberta W Schiele, 75Amanda Schield, 34Brieana R Schield, 34Callie T Schiele, 32Chad E Schiele, 49Dag A Schield, 54Faye Schiele, 68Janet L Schield, 90Jason W Schiele, 48

Jay Schiele Phones & Addresses

  • 145 Saint James Cir, Union Grove, WI 53182 (262) 878-9278
  • Canandaigua, NY
  • 4125 Fieldview Ct, Franklin, WI 53132 (414) 423-1094
  • Oak Creek, WI
  • Mount Pleasant, WI

Work

Company: Rockwell automation Jul 2011 Position: Technical leader, da and i advanced analytics

Education

Degree: Bachelors, Bachelor of Arts School / High School: Ripon College 1974 to 1978 Specialities: Economics

Skills

Strategic Planning • Crm • Management • Integration • Sales Process • Change Management • Business Intelligence • Project Management • Program Management • B2B Marketing • New Business Development • Lead Generation • Cross Functional Team Leadership • Sales • Marketing • Itil • Process Improvement • Business Planning • Strategy • Analytics • Voice of the Customer • Account Management • Sales Management • Call Center • Leadership • Team Building • Customer Experience • Project Planning • Call Centers • Process Optimization • Operations Management • It Operations • International Business Development

Languages

German

Emails

j***[email protected]

Industries

Marketing And Advertising

Resumes

Resumes

Jay Schiele Photo 1

Technical Leader, Da And I Advanced Analytics

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Location:
2614 Brick Church Pike, Nashville, TN 37207
Industry:
Marketing And Advertising
Work:
Rockwell Automation
Technical Leader, Da and I Advanced Analytics
Hunter Business Group 2006 - Jun 2011
Director, Sales Effectiveness and Voice of Customer
Abs Associates Jun 2000 - Oct 2005
President, Chief Operations Officer
Vanstar/Inacom 1997 - 2000
Program Executive
Rhino Systems, S.a. De C.v. 1997 - 1997
Information Services Director
Education:
Ripon College 1974 - 1978
Bachelors, Bachelor of Arts, Economics
Skills:
Strategic Planning
Crm
Management
Integration
Sales Process
Change Management
Business Intelligence
Project Management
Program Management
B2B Marketing
New Business Development
Lead Generation
Cross Functional Team Leadership
Sales
Marketing
Itil
Process Improvement
Business Planning
Strategy
Analytics
Voice of the Customer
Account Management
Sales Management
Call Center
Leadership
Team Building
Customer Experience
Project Planning
Call Centers
Process Optimization
Operations Management
It Operations
International Business Development
Languages:
German

Publications

Us Patents

Industrial Internet Of Things Aiops Workflows

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US Patent:
20220334838, Oct 20, 2022
Filed:
Apr 14, 2021
Appl. No.:
17/230115
Inventors:
- Mayfield Heights OH, US
Jay W. Schiele - Union Grove WI, US
International Classification:
G06F 9/30
G06N 20/00
G06K 9/62
Abstract:
Data flows and data processing modules are provided to fulfill the implementation of: contextualized data collection, scalable analytics, and machine learning operations quality assurance. These modules may be implemented as standalone components, and/or bundled as a group of coordinated microservices. The conversion of raw domain expertise and knowledge into data processors, analytics, and visualization modules for industry oriented Industrial Internet of Things (IIoT) solutions is streamlined.

Iiot Asymmetric Reactive Buffering To Dynamically Manage And Optimize In-Motion Data Batch Size

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US Patent:
20220311689, Sep 29, 2022
Filed:
Mar 23, 2021
Appl. No.:
17/209810
Inventors:
- Mayfield Heights OH, US
Dennis Jinghui Luo - Germantown WI, US
Robert Nunoo - Mundelein IL, US
Nikhil Ashok Patange - Pune, IN
Krutika Sanjay Kansara - Pune, IN
JAY W. SCHIELE - Union Grove WI, US
International Classification:
H04L 12/26
Abstract:
A reactive buffering system for use in IIoT data pipelines dynamically adjusts data accumulation and delivery by a node of a pipeline based on aggregated downstream metrics representing current data processing latencies of downstream nodes. Based on these downstream performance metrics, a reactive node that adjusts the size of the next data batch to be sent to an adjacent downstream node. The nodes of the data pipeline are configured to support a request-response based handshaking protocol whereby the nodes that send data to downstream nodes maintain up-to-date performance level information from adjacent downstream nodes. With this performance information, together with pipeline priorities, the sending node (or reactive node) adjusts the transmission rate and intermediate buffering of data. In this way, the nodes of the pipeline can dynamically regulate interim data storage to avoid overwhelming the pipeline system with too much data during periods of high latency.

Multimodal Data Reduction Agent For High Density Data In Iiot Applications

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US Patent:
20220237157, Jul 28, 2022
Filed:
Jan 25, 2021
Appl. No.:
17/157329
Inventors:
- Mayfield Heights OH, US
Francisco P. Maturana - Lyndhurst OH, US
Robert Nunoo - Mundelein IL, US
JAY W SCHIELE - Union Grove WI, US
Braun C. Brennecke - Pittsburgh PA, US
International Classification:
G06F 16/215
G06F 16/2455
G06N 7/00
Abstract:
Data reduction services are implemented on one or more nodes of an IIoT data pipeline to intelligently determine an appropriate data reduction strategy based on characteristics of the incoming data. In one or more embodiments, data reduction components on the pipeline node or on an edge device define different data filtering rules or algorithms that are selectively applied to streaming time-series data based on a probability distribution of the data. The data pipeline node performs real-time distribution analysis on the streaming data to determine whether the data has a unimodal distribution, a multimodal distribution, or no mode, and selects one of the data filtering rules based on this determined probability distribution. In this way, the data is intelligently reduced in a manner that retains critical information within the reduced data set while achieving a high level of data reduction.
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Jay W Schiele from Union Grove, WI, age ~67 Get Report