Physical-Virtual Unison + business context without with just connecting physical and system is meaningless, This was done in 2005-08, industry is catching up now with Digital Twin in an imperfect way but hopefully will correct itself to align with this vision!!
Data Capture Market of 2014 - Navigating Competitive LandscapeDmitri Khanine
This session provides a deeper understanding of 2014 Data Capture market and makes it easy for you to navigate the world of competing vendors, products and prices. We will also take a deeper look and understand the strengths and competitive advantages of Oracle Capture and Oracle Forms Recognition
The reason we, and our customers, have embraced and adopted RFID is simple: Automation speeds up processes, removes human error, and lets real human resources be allocated for higher level processes. That's the gist of how and why it works in manufacturing; it works with safety & equipment tracking systems in a similar fashion.
Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights.
Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement.
A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.
Data Capture Market of 2014 - Navigating Competitive LandscapeDmitri Khanine
This session provides a deeper understanding of 2014 Data Capture market and makes it easy for you to navigate the world of competing vendors, products and prices. We will also take a deeper look and understand the strengths and competitive advantages of Oracle Capture and Oracle Forms Recognition
The reason we, and our customers, have embraced and adopted RFID is simple: Automation speeds up processes, removes human error, and lets real human resources be allocated for higher level processes. That's the gist of how and why it works in manufacturing; it works with safety & equipment tracking systems in a similar fashion.
Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights.
Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement.
A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.
How to Build Fast Data Applications: Evaluating the Top ContendersVoltDB
Massive amounts of data generated from mobile devices, M2M communications, sensors and other IoT devices is redefining the world. What kind of applications will you build to take advantage of this data and provide value to your customers? What technologies are out there to help you? This deck will illustrate the difference between fast OLAP, stream-processing, and OLTP database solutions. You will also learn the importance of state, real-time analytics and real-time decisions when building applications on streaming data, and how streaming applications deliver more value when built on a super-fast in-memory, SQL database. To view the webinar in its entirety, click here: http://learn.voltdb.com/WRFastDataAppsTopContenders.html
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use CasesSanjay Sharma
Financial institutions today are under intense pressure to provide more value add to the customers, reduce IT costs and also grow year to year. This challenge has been further complicated by huge amounts of data being generated as well as mandatory federal compliances in place.
Similarly, Manufacturing industry today also is facing the challenge to process huge amount of data in real time and predict failures as early as possible to reduce cost and increase production efficiency.
The session will cover some high level Big Data use cases applicable to financial and manufacturing domain and how big data technologies are being used successfully to solve these challenges using some examples in credit card/banking industry in financial domain and semi-conductor production in manufacturing domain.
50 Shades of Data - Dutch Oracle Architects Platform (February 2018)Lucas Jellema
Gone are the days of a single enterprise database – typically and Oracle RDBMS – that holds all data in a strictly normalized form. We work with many more types of data (big and fast, structured and unstructured) that we use in various ways. Relational and ACID is not applicable to all of those. Always the latest, freshest data may not be uniformly valid either. We will continue to see an increase in specialized data stores that cater for specific needs and specific scenarios.
This presentation is a combination of a presentation and a demonstration on the various dimensions and use cases of using data and data stores in various ways – while ensuring the appropriate (!) levels of freshness, integrity, performance. Key take away: what as an architect you should know about the various types of data in enterprise IT and how to store/manage/query/manipulate them. What products and technologies are at your disposal. How can you make these work together - for a consistent (enough) overall data presentation. How are upcoming architectural patterns such as CQRS (command query responsibility segregation) , event sourcing and microservices influencing the way we handle data in the enterprise? Some of the technologies discussed: products such as MongoDB, MySQL, Neo4J, Apache Kafka, Redis, Elastic Search and Hadoop/Spark, Oracle Data Hub Cloud (based on Apache Cassandra) – used locally, in containers and on the cloud. Additionally we will discuss data replication scenarios.
The purpose of business intelligence is to support better business decision making. BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
Presentation about BigData from a German Webcast: http://business-services.heise.de/it-management/big-data/beitrag/big-data-technologie-einsatzgebiete-datenschutz-160.html?source=IBM_12_2013_IT_Conn
How to Build Fast Data Applications: Evaluating the Top ContendersVoltDB
Massive amounts of data generated from mobile devices, M2M communications, sensors and other IoT devices is redefining the world. What kind of applications will you build to take advantage of this data and provide value to your customers? What technologies are out there to help you? This deck will illustrate the difference between fast OLAP, stream-processing, and OLTP database solutions. You will also learn the importance of state, real-time analytics and real-time decisions when building applications on streaming data, and how streaming applications deliver more value when built on a super-fast in-memory, SQL database. To view the webinar in its entirety, click here: http://learn.voltdb.com/WRFastDataAppsTopContenders.html
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Global Big Data Conference Hyderabad-2Aug2013- Finance/Manufacturing Use CasesSanjay Sharma
Financial institutions today are under intense pressure to provide more value add to the customers, reduce IT costs and also grow year to year. This challenge has been further complicated by huge amounts of data being generated as well as mandatory federal compliances in place.
Similarly, Manufacturing industry today also is facing the challenge to process huge amount of data in real time and predict failures as early as possible to reduce cost and increase production efficiency.
The session will cover some high level Big Data use cases applicable to financial and manufacturing domain and how big data technologies are being used successfully to solve these challenges using some examples in credit card/banking industry in financial domain and semi-conductor production in manufacturing domain.
50 Shades of Data - Dutch Oracle Architects Platform (February 2018)Lucas Jellema
Gone are the days of a single enterprise database – typically and Oracle RDBMS – that holds all data in a strictly normalized form. We work with many more types of data (big and fast, structured and unstructured) that we use in various ways. Relational and ACID is not applicable to all of those. Always the latest, freshest data may not be uniformly valid either. We will continue to see an increase in specialized data stores that cater for specific needs and specific scenarios.
This presentation is a combination of a presentation and a demonstration on the various dimensions and use cases of using data and data stores in various ways – while ensuring the appropriate (!) levels of freshness, integrity, performance. Key take away: what as an architect you should know about the various types of data in enterprise IT and how to store/manage/query/manipulate them. What products and technologies are at your disposal. How can you make these work together - for a consistent (enough) overall data presentation. How are upcoming architectural patterns such as CQRS (command query responsibility segregation) , event sourcing and microservices influencing the way we handle data in the enterprise? Some of the technologies discussed: products such as MongoDB, MySQL, Neo4J, Apache Kafka, Redis, Elastic Search and Hadoop/Spark, Oracle Data Hub Cloud (based on Apache Cassandra) – used locally, in containers and on the cloud. Additionally we will discuss data replication scenarios.
The purpose of business intelligence is to support better business decision making. BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
Presentation about BigData from a German Webcast: http://business-services.heise.de/it-management/big-data/beitrag/big-data-technologie-einsatzgebiete-datenschutz-160.html?source=IBM_12_2013_IT_Conn
Advanced use cases include IoT based integrated SAP Retail Application, cross-applications built on CAF or an x-APP, or an end to end business process complete business process irrespective of the number of SAP components involved (R3/mySAP, with XI BPM))
A virtual replica of organizations’ physical supply chain, with business context, constraints and optimization models embedded for everyday operating decisions, with alogrithms and tools abstracted for non-statistical operators
Skandsoft Frost & Sullivan Award for Setu RFID/IoT MiddlewareSurendra Kancherla
RFID Emerging Technology of the Year 2006 was awarded in San Diego to Skandsoft in the face of competition from industry big guns, for the product and technology approach.
IoT Adaptive Inventory in Manufacturing SCM Mahindra and MahindraSurendra Kancherla
The very first industrial, real-life application of RFID in adaptive inventory use case, published as case study in books and articles. All the value levers are very much valid in 2020, almost 15 years later!
Status of Innovation report by Department of Science and Technology, Govt of India, covering RFID Debit Card based HNI Personalization of Banking services, a first globally.
Commissioned debit card with embedded RFID Chip for the first ever HNI Personalization Retail Banking application, more than a decade ago. The industry is yet to catch up.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
3. RFID
• Comprises Readers, Tags and Antennae
• Working
– Tags are like SIM Cards – but can only reply ‘ I am so and so..’
– Readers/Antennae are like cell towers – but can talk to a couple of meters
only
– Tagged objects (moving/moveable) communicate what Tags are
programmed to, with the Readers through Antennae
• Not a substitute for Barcode- sometimes more and sometimes less
than a Barcode
• Just another tool for Data Capture – part of AIDC Spectrum
• Caveat - A tool is as good as the person using it
4. Perspectives
• RFID can be implemented for point solutions
• Can also be implemented for long-term Business
Process improvements
• Design an implementation for the long-term, also
accommodating point solutions
• A good implementation will use RFID data for
Data Modeling and Synthesis
5. • Your ROI depends on your perspective
RFID as business enhancer – Data
collector and router
Data Analytics, Modeling, BI
et c
RFID as solution facilitatorBAM, BPR et c
RFID as Compliance tool
RFID as technology-Point solutionsAccess Control, Automation etc.
Benefit / Return Perspective
Perspectives
6. Data Model
• Objects (moving/moveable) – with temporal and historical
attributes, relationships
• Locations – absolute and relative to movement of objects
• Transactions – interaction among objects-locations, change in
object-object, object-location relationships
• All linked through processes, time and space coordinates
• All captured through an observatory (set of sensors like
Readers)
7. Data Model
An object tagged with required details –
identification etc
8 10 10
30 20 50
25 8 15
relat’nship
1 2 3
timeid
productid
111213
8. Data Model
An object tagged with required details –
identification etc
Capture association (Containment) details. Also
association with employees, locations (BIN)
etc through sensors
Capture Transaction Details – PO, ASN
etc.,
Capture Change Parameters
– Location, Transit,
Transaction et c
Capture Location (Shelf),
Transaction, Process
et c
Capture Path, Stops,
Basket (Tagged
Trolley) etc.,
9. Data Model
• Reactive
• Silos of data – many disjoints – esp’ly physical context
• Limited Collaboration - as no control over process execution
• Aggregate Data only
Business Processes
Transactions happen real-time
Business Analysis
Non-granular data, Non-
contextual, Off-line, Latency,
• Proactive
• Informed real-time Business Analyses
• Collaborative
• Contextual, Continual data – no disjoints
• Granular Data
• Control over Process Execution and
Monitoring
• Ability to take decisions real-time and
feedback to the system
• Broader distribution with many
opportunities for collaboration
• Merging Business Analysis with Business
Process
Real-Time,
Contextual,
Process-Centric
Data
10. Business Case Approach
CONQUER
C – Collect & Control Data – Multi- protocol, vendor hardware,
24x7Health Monitoring of Grid
O – Organize Data – Filter,parse
N – Nurture Data - Convert data to valuable info, Selective Filtering
Q – Qualify Information - Apply Business Context, define Impact
U – Understand Information – Associate impacted business process
component,Sharing and Collaboration
E - Extrapolate Information – EAI – To the relevant business process
component
R - React – Execute responses on behalf of the enterprise
11. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
A Unique
Approach
12. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
Tracking Material
and people
movement, sensing
environmental
changes – Pressure,
Gas, Chemical
sensors etc.,
13. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
14. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
15. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
16. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
17. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R
18. Business Analytics Custom Reporting Enterprise Apps
Statistical
Analysis
Parameters Parameters
Analytics Preparatory Relevance Filtering Aggregation
Data Capture (AIDC) Black Box
Data Parsing
Selective Filtering
Event Generation
Rule Base
Process Enabling
Actions
Space
Ti
m
e
Material Movement People Movement En’ronment Changes
C
O
N
Q
U
E
R