This document discusses enhancing data management workflows through CAD-integrated simulation. It proposes integrating simulation data management tools with CAD tools so they can communicate. This allows capturing simulation metadata in CAD files to build an intelligent simulation framework. Key benefits include reusing existing simulation data, improving consistency and collaboration across teams, and managing the growing amounts of simulation data. An example workflow is described that incorporates the framework to better organize simulation processes.
The document provides an overview of the topics covered in a systems analysis and design course, including software used, information system components, analyzing the business case, managing projects, requirements modeling, data modeling, object modeling, development strategies, output and interface design, data design, and system architecture. Key concepts discussed include SWOT analysis, business cases, feasibility studies, project management techniques, UML, data flow diagrams, use cases, object-oriented analysis, cost-benefit analysis methods, user interface design, data structure, normalization, and entity relationship diagrams.
PROGNOZ Platform is a fully integrated BI platform that provides easy-to-use tools for constructing business applications with a broad range of analytic capabilities.
Key features:
1. One of the best ever designs for a BI platform
2. Entirely new user-friendly interface
3. Advanced Web-based capabilities for creating reports, dashboards, and scorecards using different kinds of visualization tools (charts, maps, gauges)
4. Modeling, forecasting, and time series analysis
5. Enhanced mobile capabilities (Apple iOS)
6. Collaboration tools: Integration with social media (Facebook, Twitter, and so on)
7. Portal integration tools (Microsoft SharePoint, SAP NetWeaver, IBM WebSphere)
8. Cross-platform application server (Windows and Linux)
9. Enhanced mapping visualizations with Web mapping services (Google Maps, Microsoft Bing, OpenStreetMap, ArcGIS) and 3D technology
10. Common security, metadata, administration, portal integration, object model, and query engine for all platform components
This document describes the development of a Construction Management Decision Support System (CMDSS) that integrates a data warehouse and decision support system (DSS) to provide construction managers with timely analysis reports and insights to support both long-term and short-term decision making. It first reviews data warehouses, online analytical processing (OLAP), and DSS. It then outlines the design of the data warehouse using a star schema with fact and dimension tables, and the transformation of data into multidimensional cubes using OLAP for analysis. Finally, it discusses the design of the DSS frontend and reporting tools to enable construction managers to directly access and analyze data to make more informed decisions.
Lecture 09 - Migration to the Architected Environmentphanleson
The chapter discusses the process of migrating to an architected data warehouse environment. It describes developing a migration plan starting with a corporate data model and defining a system of record. The plan also includes designing the data warehouse, building interfaces to load data, and populating the first subject area. An iterative development approach is used where the data architect gets feedback from analysts to continually improve the data warehouse over time. Strategic considerations include addressing both operational and decision support needs.
The document discusses establishing a service-oriented architecture (SOA) through a step-by-step process. It recommends starting with a pilot project to test technical and architectural decisions on a small scale before growing SOA into a general strategy. Governance is important to guide the process and avoid issues, led by a central SOA team. SOA requires both technical infrastructure and organizational changes, and its success relies on leadership, management support, collaboration, and an iterative approach.
Stuart Arnold seeks a career in business systems and project management. He has over 16 years of experience in ERP systems including SAP, database management, and data analysis. His experience includes migrating data between various systems, building reports in Microsoft and Tableau, and project management. He currently works as a database analyst at CDC focusing on Access, Tableau, and SharePoint solutions.
How To Collect Budget Data Across20 30 DimsCurtis_Neumann
The document summarizes how AT&T migrated budget data collection from an outdated Access database process to Oracle Hyperion Planning. It describes challenges with the legacy process and initial attempts to use Hyperion Planning directly. The final solution used Hyperion Planning to collect data via smart lists, then exported it to an Essbase cube for reporting. This allowed over 20 dimensions of sparse data to be managed across the two tools in a scalable way.
Lecture 18 - Model-Driven Service Developmentphanleson
This document discusses model-driven service development (MDSD). MDSD involves generating code for both service providers and consumers based on models or descriptions of services. Code generators can be used to produce common code structures for different services, reducing duplication. Models define services and their properties, and may be represented using different notations like UML or XML. Meta-models define the structure of models. MDSD processes involve defining meta-models, creating models, transforming models using generators to produce code, and setting up consumer-driven or provider-driven transformation workflows.
The document provides an overview of the topics covered in a systems analysis and design course, including software used, information system components, analyzing the business case, managing projects, requirements modeling, data modeling, object modeling, development strategies, output and interface design, data design, and system architecture. Key concepts discussed include SWOT analysis, business cases, feasibility studies, project management techniques, UML, data flow diagrams, use cases, object-oriented analysis, cost-benefit analysis methods, user interface design, data structure, normalization, and entity relationship diagrams.
PROGNOZ Platform is a fully integrated BI platform that provides easy-to-use tools for constructing business applications with a broad range of analytic capabilities.
Key features:
1. One of the best ever designs for a BI platform
2. Entirely new user-friendly interface
3. Advanced Web-based capabilities for creating reports, dashboards, and scorecards using different kinds of visualization tools (charts, maps, gauges)
4. Modeling, forecasting, and time series analysis
5. Enhanced mobile capabilities (Apple iOS)
6. Collaboration tools: Integration with social media (Facebook, Twitter, and so on)
7. Portal integration tools (Microsoft SharePoint, SAP NetWeaver, IBM WebSphere)
8. Cross-platform application server (Windows and Linux)
9. Enhanced mapping visualizations with Web mapping services (Google Maps, Microsoft Bing, OpenStreetMap, ArcGIS) and 3D technology
10. Common security, metadata, administration, portal integration, object model, and query engine for all platform components
This document describes the development of a Construction Management Decision Support System (CMDSS) that integrates a data warehouse and decision support system (DSS) to provide construction managers with timely analysis reports and insights to support both long-term and short-term decision making. It first reviews data warehouses, online analytical processing (OLAP), and DSS. It then outlines the design of the data warehouse using a star schema with fact and dimension tables, and the transformation of data into multidimensional cubes using OLAP for analysis. Finally, it discusses the design of the DSS frontend and reporting tools to enable construction managers to directly access and analyze data to make more informed decisions.
Lecture 09 - Migration to the Architected Environmentphanleson
The chapter discusses the process of migrating to an architected data warehouse environment. It describes developing a migration plan starting with a corporate data model and defining a system of record. The plan also includes designing the data warehouse, building interfaces to load data, and populating the first subject area. An iterative development approach is used where the data architect gets feedback from analysts to continually improve the data warehouse over time. Strategic considerations include addressing both operational and decision support needs.
The document discusses establishing a service-oriented architecture (SOA) through a step-by-step process. It recommends starting with a pilot project to test technical and architectural decisions on a small scale before growing SOA into a general strategy. Governance is important to guide the process and avoid issues, led by a central SOA team. SOA requires both technical infrastructure and organizational changes, and its success relies on leadership, management support, collaboration, and an iterative approach.
Stuart Arnold seeks a career in business systems and project management. He has over 16 years of experience in ERP systems including SAP, database management, and data analysis. His experience includes migrating data between various systems, building reports in Microsoft and Tableau, and project management. He currently works as a database analyst at CDC focusing on Access, Tableau, and SharePoint solutions.
How To Collect Budget Data Across20 30 DimsCurtis_Neumann
The document summarizes how AT&T migrated budget data collection from an outdated Access database process to Oracle Hyperion Planning. It describes challenges with the legacy process and initial attempts to use Hyperion Planning directly. The final solution used Hyperion Planning to collect data via smart lists, then exported it to an Essbase cube for reporting. This allowed over 20 dimensions of sparse data to be managed across the two tools in a scalable way.
Lecture 18 - Model-Driven Service Developmentphanleson
This document discusses model-driven service development (MDSD). MDSD involves generating code for both service providers and consumers based on models or descriptions of services. Code generators can be used to produce common code structures for different services, reducing duplication. Models define services and their properties, and may be represented using different notations like UML or XML. Meta-models define the structure of models. MDSD processes involve defining meta-models, creating models, transforming models using generators to produce code, and setting up consumer-driven or provider-driven transformation workflows.
New IBM Information Server 11.3 - Bhawani Nandan PrasadBhawani N Prasad
The document summarizes new features in Information Server V11.3, including enhancements to Information Governance Catalog, Data Integration, Data Quality, and the roadmap for ongoing releases. Key updates are improved metadata collaboration in Information Governance Catalog, self-service data integration in Data Click, a Governance Dashboard to monitor data quality objectives, and performance optimizations for profiling and rules. Future releases will add additional platform and cloud support along with new Data Click and MDM integration capabilities.
Using the information server toolset to deliver end to end traceabilityIBM Sverige
The document discusses using IBM's Information Server Toolset to deliver end-to-end traceability. It describes why end-to-end traceability is important for understanding data flows and impacts of changes. It also provides examples of how Information Server tools like Information Analyzer, Information Services Director, and InfoSphere Data Architect can be used to achieve traceability across source systems, data integration processes, data warehouses and analytics applications.
Cognos Data Module Architectures & Use CasesSenturus
Demos of Cognos data module architectures, real-world data module use cases, concepts of data set libraries and current data module gaps as compared to Framework Manager and other modeling use cases. View our on-demand webinar and download this deck at: https://senturus.com/resources/cognos-data-module-architectures-and-use-cases/.
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Traditional operational views of capacity planning is not the same as BI Capacity planning. I created this presentation to help establish a BI Infrastructure Capacity planning process.
Informatica push down optimization implementationdivjeev
The document discusses a new pushdown optimization option available in Informatica PowerCenter 8 that can improve data integration performance and scalability. It works by generating database-specific logic to represent the overall data flow and pushing the execution of that logic into the database to perform data transformations. This allows taking advantage of database processing power and avoids extracting large amounts of data. The option provides flexibility in controlling where processing takes place and leverages a single design environment. Pushing logic to the database can significantly increase performance by avoiding extracting and reloading large amounts of data.
This document describes an efficient student faculty management system that was developed to streamline processes like personnel management, class scheduling, student results analysis, and communication between faculty and students. The system provides functionalities for viewing and updating staff, student, and course details securely through a web-based interface. It aims to reduce paperwork and allow for easy information sharing compared to traditional manual methods. The system was designed with input, output, and database components following common principles of system design. It was implemented in stages including planning, training, testing and transitioning to production use.
Exercise solution of chapter3 of datawarehouse cs614(solution of exercise)AYESHA JAVED
The document discusses making a company's existing data warehouse web-enabled to satisfy management's directive. Three major tasks for web-enabling the data warehouse are: 1) Bringing the data warehouse to the web to allow self-service data access, interactive analysis, and delivery to external users. 2) Capturing clickstream data from the company's website and performing ETL to load it into a webhouse for analysis. 3) Configuring the architecture of the web-enabled data warehouse to include a webhouse repository for clickstream data in addition to the traditional data warehouse repository.
Exercise solution of chapter1 of datawarehouse cs614(solution of exercise)AYESHA JAVED
The document discusses solutions to review and exercise questions about data warehousing and strategic information systems. It provides answers to questions about matching terms related to information systems, how technology trends help make data warehousing feasible, examples of opportunities from strategic information, how strategic information can increase frequent flyers for an airline, and a proposal explaining why a data warehouse would provide strategic information for a company's marketing vice president.
IS L02 - Development of Information SystemsJan Wong
This document provides an overview of information systems development using the System Development Lifecycle (SDLC) framework. The SDLC is a 5 phase process: 1) Planning, 2) Analysis, 3) Design, 4) Implementation, and 5) Operation, Support and Security. Each phase is described in detail, from assembling a project team to gathering requirements to designing and building the system to deployment and ongoing maintenance. The goal is to systematically develop an information system that solves business problems by following best practices at each step of the process.
BI architecture presentation and involved models (short)Thierry de Spirlet
The document discusses the components of a BI architecture, including models, processes, scheduling, and monitoring. It describes different types of models used in BI solutions, such as OLTP models, ERD models, dimensional models, and presentation models. It also discusses different layers in a BI architecture, including the extract area, conceptualization area, data warehouse, and datamarts. Choosing the right model for each layer and implementing the correct BI landscape from the beginning is important for an effective architecture.
The document provides an overview of the Structured Systems Analysis and Design Method (SSADM). It describes SSADM as a comprehensive, structured approach to systems development that is considered the true successor to traditional system development lifecycles. The key techniques of SSADM are described as logical data modeling, data flow modeling, and entity event modeling. The stages of the SSADM methodology are then outlined, including feasibility study, investigation of the current environment, business system options, requirements specification, technical system options, logical design, and physical design.
Become BI Architect with 1KEY Agile BI Suite - OLAPDhiren Gala
Business intelligence uses applications and technologies to analyze data and help users make better business decisions. Online transaction processing (OLTP) is used for daily operations like processing, while online analytical processing (OLAP) is used for data analysis and decision making. Data warehouses integrate data from different sources to provide a centralized system for analysis and reporting. Dimensional modeling approaches like star schemas and snowflake schemas organize data to support OLAP.
This document discusses various business analysis and decision support tools. It begins by describing five main categories of decision support tools: reporting tools, managed query tools, executive information system tools, online analytical processing (OLAP) tools, and data mining tools. It provides details on the different types of tools within each category. It also discusses the Cognos Impromptu reporting and query tool, including its features and capabilities. Finally, it briefly describes common OLAP operations on multidimensional data like roll-up, drill-down, slice and dice, and pivot.
Implementing bi in proof of concept techniquesRanjith Ramanan
Mark Kromer and Daniel Yu are senior product managers at Microsoft specializing in business intelligence solutions.
Between 70-80% of business intelligence projects fail due to poor communication between IT and business users, a failure to understand business needs, and viewing BI as only an IT issue rather than a way to improve business performance.
It is better to implement BI solutions across many business users to increase ROI for each user, rather than focusing only on analytical users.
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
This document discusses integrating big data in the banking sector to speed up analytical processes. It proposes developing a Hadoop data warehouse using ETL tools to integrate heterogeneous banking data from various sources into a single format and location. A multi-dimensional data model using star and snowflake schemas would be designed. Data would be extracted from sources, transformed and loaded into a staging area, then into the data warehouse. This would allow generating reports faster for business analysis, forecasting and developing strategies to enhance the bank's objectives.
Microstrategy is a business intelligence software that allows reporting and analysis of data stored in relational databases, multidimensional databases, or flat files. It includes various components like the Intelligence Server for report serving, the Web Universal interface for interactive reports in a web browser, and the Narrowcast Server for automated delivery of business information to users. Microstrategy uses technologies like Java/J2EE and supports features such as real-time dashboards, detailed reports, and data analysis.
Companies often need reporting capabilities beyond what is available in their ERP systems. 1KEY Business Intelligence provides these additional capabilities through an external reporting tool. It allows users to analyze and interpret data from ERP databases and other sources to gain insights. Companies using 1KEY BI have seen reductions in personnel time needed for reporting of 50-90%. The tool empowers business users to access, format, and analyze data themselves to support decision making without relying on IT.
Accelerating Machine Learning as a Service with Automated Feature EngineeringCognizant
Building scalable machine learning as a service, or MLaaS, is critical to enterprise success. Key to translate machine learning project success into program success is to solve the evolving convoluted data engineering challenge, using local and global data. Enabling sharing of data features across a multitude of models within and across various line of business is pivotal to program success.
An Integrated Simulation Tool Framework for Process Data ManagementCognizant
Digital simulations play an increasing role in product lifecycle management (PLM) processes and simulation data management (SDM) based on the PLM XML protocol, which is a key interface with computer-aided engineering (CAE) applications. We offer a framework for aligning SDM with the overall product development process to shorten lead times and optimize output.
New IBM Information Server 11.3 - Bhawani Nandan PrasadBhawani N Prasad
The document summarizes new features in Information Server V11.3, including enhancements to Information Governance Catalog, Data Integration, Data Quality, and the roadmap for ongoing releases. Key updates are improved metadata collaboration in Information Governance Catalog, self-service data integration in Data Click, a Governance Dashboard to monitor data quality objectives, and performance optimizations for profiling and rules. Future releases will add additional platform and cloud support along with new Data Click and MDM integration capabilities.
Using the information server toolset to deliver end to end traceabilityIBM Sverige
The document discusses using IBM's Information Server Toolset to deliver end-to-end traceability. It describes why end-to-end traceability is important for understanding data flows and impacts of changes. It also provides examples of how Information Server tools like Information Analyzer, Information Services Director, and InfoSphere Data Architect can be used to achieve traceability across source systems, data integration processes, data warehouses and analytics applications.
Cognos Data Module Architectures & Use CasesSenturus
Demos of Cognos data module architectures, real-world data module use cases, concepts of data set libraries and current data module gaps as compared to Framework Manager and other modeling use cases. View our on-demand webinar and download this deck at: https://senturus.com/resources/cognos-data-module-architectures-and-use-cases/.
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Traditional operational views of capacity planning is not the same as BI Capacity planning. I created this presentation to help establish a BI Infrastructure Capacity planning process.
Informatica push down optimization implementationdivjeev
The document discusses a new pushdown optimization option available in Informatica PowerCenter 8 that can improve data integration performance and scalability. It works by generating database-specific logic to represent the overall data flow and pushing the execution of that logic into the database to perform data transformations. This allows taking advantage of database processing power and avoids extracting large amounts of data. The option provides flexibility in controlling where processing takes place and leverages a single design environment. Pushing logic to the database can significantly increase performance by avoiding extracting and reloading large amounts of data.
This document describes an efficient student faculty management system that was developed to streamline processes like personnel management, class scheduling, student results analysis, and communication between faculty and students. The system provides functionalities for viewing and updating staff, student, and course details securely through a web-based interface. It aims to reduce paperwork and allow for easy information sharing compared to traditional manual methods. The system was designed with input, output, and database components following common principles of system design. It was implemented in stages including planning, training, testing and transitioning to production use.
Exercise solution of chapter3 of datawarehouse cs614(solution of exercise)AYESHA JAVED
The document discusses making a company's existing data warehouse web-enabled to satisfy management's directive. Three major tasks for web-enabling the data warehouse are: 1) Bringing the data warehouse to the web to allow self-service data access, interactive analysis, and delivery to external users. 2) Capturing clickstream data from the company's website and performing ETL to load it into a webhouse for analysis. 3) Configuring the architecture of the web-enabled data warehouse to include a webhouse repository for clickstream data in addition to the traditional data warehouse repository.
Exercise solution of chapter1 of datawarehouse cs614(solution of exercise)AYESHA JAVED
The document discusses solutions to review and exercise questions about data warehousing and strategic information systems. It provides answers to questions about matching terms related to information systems, how technology trends help make data warehousing feasible, examples of opportunities from strategic information, how strategic information can increase frequent flyers for an airline, and a proposal explaining why a data warehouse would provide strategic information for a company's marketing vice president.
IS L02 - Development of Information SystemsJan Wong
This document provides an overview of information systems development using the System Development Lifecycle (SDLC) framework. The SDLC is a 5 phase process: 1) Planning, 2) Analysis, 3) Design, 4) Implementation, and 5) Operation, Support and Security. Each phase is described in detail, from assembling a project team to gathering requirements to designing and building the system to deployment and ongoing maintenance. The goal is to systematically develop an information system that solves business problems by following best practices at each step of the process.
BI architecture presentation and involved models (short)Thierry de Spirlet
The document discusses the components of a BI architecture, including models, processes, scheduling, and monitoring. It describes different types of models used in BI solutions, such as OLTP models, ERD models, dimensional models, and presentation models. It also discusses different layers in a BI architecture, including the extract area, conceptualization area, data warehouse, and datamarts. Choosing the right model for each layer and implementing the correct BI landscape from the beginning is important for an effective architecture.
The document provides an overview of the Structured Systems Analysis and Design Method (SSADM). It describes SSADM as a comprehensive, structured approach to systems development that is considered the true successor to traditional system development lifecycles. The key techniques of SSADM are described as logical data modeling, data flow modeling, and entity event modeling. The stages of the SSADM methodology are then outlined, including feasibility study, investigation of the current environment, business system options, requirements specification, technical system options, logical design, and physical design.
Become BI Architect with 1KEY Agile BI Suite - OLAPDhiren Gala
Business intelligence uses applications and technologies to analyze data and help users make better business decisions. Online transaction processing (OLTP) is used for daily operations like processing, while online analytical processing (OLAP) is used for data analysis and decision making. Data warehouses integrate data from different sources to provide a centralized system for analysis and reporting. Dimensional modeling approaches like star schemas and snowflake schemas organize data to support OLAP.
This document discusses various business analysis and decision support tools. It begins by describing five main categories of decision support tools: reporting tools, managed query tools, executive information system tools, online analytical processing (OLAP) tools, and data mining tools. It provides details on the different types of tools within each category. It also discusses the Cognos Impromptu reporting and query tool, including its features and capabilities. Finally, it briefly describes common OLAP operations on multidimensional data like roll-up, drill-down, slice and dice, and pivot.
Implementing bi in proof of concept techniquesRanjith Ramanan
Mark Kromer and Daniel Yu are senior product managers at Microsoft specializing in business intelligence solutions.
Between 70-80% of business intelligence projects fail due to poor communication between IT and business users, a failure to understand business needs, and viewing BI as only an IT issue rather than a way to improve business performance.
It is better to implement BI solutions across many business users to increase ROI for each user, rather than focusing only on analytical users.
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
This document discusses integrating big data in the banking sector to speed up analytical processes. It proposes developing a Hadoop data warehouse using ETL tools to integrate heterogeneous banking data from various sources into a single format and location. A multi-dimensional data model using star and snowflake schemas would be designed. Data would be extracted from sources, transformed and loaded into a staging area, then into the data warehouse. This would allow generating reports faster for business analysis, forecasting and developing strategies to enhance the bank's objectives.
Microstrategy is a business intelligence software that allows reporting and analysis of data stored in relational databases, multidimensional databases, or flat files. It includes various components like the Intelligence Server for report serving, the Web Universal interface for interactive reports in a web browser, and the Narrowcast Server for automated delivery of business information to users. Microstrategy uses technologies like Java/J2EE and supports features such as real-time dashboards, detailed reports, and data analysis.
Companies often need reporting capabilities beyond what is available in their ERP systems. 1KEY Business Intelligence provides these additional capabilities through an external reporting tool. It allows users to analyze and interpret data from ERP databases and other sources to gain insights. Companies using 1KEY BI have seen reductions in personnel time needed for reporting of 50-90%. The tool empowers business users to access, format, and analyze data themselves to support decision making without relying on IT.
Accelerating Machine Learning as a Service with Automated Feature EngineeringCognizant
Building scalable machine learning as a service, or MLaaS, is critical to enterprise success. Key to translate machine learning project success into program success is to solve the evolving convoluted data engineering challenge, using local and global data. Enabling sharing of data features across a multitude of models within and across various line of business is pivotal to program success.
An Integrated Simulation Tool Framework for Process Data ManagementCognizant
Digital simulations play an increasing role in product lifecycle management (PLM) processes and simulation data management (SDM) based on the PLM XML protocol, which is a key interface with computer-aided engineering (CAE) applications. We offer a framework for aligning SDM with the overall product development process to shorten lead times and optimize output.
1-SDLC - Development Models – Waterfall, Rapid Application Development, Agile...JOHNLEAK1
This document provides information about different types of data models:
1. Conceptual data models define entities, attributes, and relationships at a high level without technical details.
2. Logical data models build on conceptual models by adding more detail like data types but remain independent of specific databases.
3. Physical data models describe how the database will be implemented for a specific database system, including keys, constraints and other features.
A Machine learning based framework for Verification and Validation of Massive...IRJET Journal
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
The document discusses the design phase of the system development life cycle. It describes the objectives and steps of the design phase, which include presenting design alternatives, converting logical models to physical models, designing the system architecture, making hardware and software selections, and designing inputs, outputs, data storage, and programs. Common design strategies like custom development, packaged systems, and outsourcing are also covered. The document then explains various system design methods and the stages of system design, including logical, physical, and program design. Finally, it discusses avoiding common design mistakes.
The document discusses analyzing systems using data flow diagrams (DFDs). It explains that DFDs are a key method for analyzing data-oriented systems by graphically representing how data moves through an organization. The data flow approach has advantages over narrative explanations, including freedom from early technical commitments and better communication with users. Physical DFDs show how a system will be implemented and include details like manual vs automated processes. Structured analysis techniques are used to model system processes and document data flows and stores.
IRJET- Data Analytics & Visualization using QlikIRJET Journal
This document discusses the data analytics and visualization tool Qlikview. It begins by providing background on data analytics, including the processes of data collection, cleansing, transformation, and analysis. It then describes Qlikview's key features, including its in-memory approach, associated query language, scripting abilities, and powerful visualization interfaces. The document argues that Qlikview differs from other business intelligence tools by bringing together all data to allow for unlimited, on-the-fly exploration and analysis without predefined queries. It concludes that data visualization has become important for extracting insights from data and that Qlikview continues to innovate its offerings.
There are several types of digital twins including product, process, system, performance, physics-based, and business twins. Product twins simulate individual products, process twins simulate entire systems or processes, and system twins simulate complex systems and subsystems. Performance twins are used for monitoring and optimizing real-time data, and physics-based twins create highly accurate digital replicas using advanced modeling. Business twins simulate business processes and operations.
Anylogic 2021 Conference Presentation: Automatic generation of simulation mod...Sudhendu Rai
The full talk is available at:
https://youtu.be/xz295p_wS1k
Abstract:
Discrete-event simulation modeling is a powerful technology to analyze and improve complex business processes. However, the development of such models requires significant expertise and skills. In any business, there exist general classes of processes with specific instantiations within each class. In this talk, we propose the use of data templates for capturing process data and automated generation of simulation models using those templates. These auto-generated simulation models can be further enhanced using the Anylogic visual programming interface. Other analytical features of Anylogic such as optimization, visualization, automated output of simulation results can be utilized to analyze and improve the processes. By developing a library of templates, organizational knowledge associated with the processes can be captured and stored. These models will capture process dynamics and interactions and be far richer than what is captured through the use of process maps only. This approach will enable a broader use of simulation technology to develop business processes and make improved organizational decisions through de-skilling. It will also enable the integration of simulation software into a broader process improvement technology ecosystem.
#datascience #analytics #simulationmodeling
#processexcellence #processimprovement #processoptimization #processautomation #processmining #processintelligence #processtransformation
Calame Software developed Gathering Tools to address the problem of dispersed data in enterprises. Gathering Tools allows complex Excel collection processes to be converted into secure, synchronized forms and integrated into the information system. It aims to reconcile IS departments seeking integration and operations staff wanting automated processes. Gathering Tools reproduces Excel workbooks as forms while ensuring data quality and accessibility for decision tools. It provides automation, security, and workflows that Excel lacks.
The document is a request for fully solved SMU MBA assignments from Spring 2014. It provides contact information for students to send their semester and specialization to obtain the assignments. It notes that sample assignments can be found in blog archives or by searching. The document then provides several MBA assignments related to software engineering, database management systems, computer networks, and other topics. Students are to answer the questions and provide explanations and examples.
Lumina's Analytica software allows users to create complex business models and simulations visually, without using spreadsheets or code. It supports probabilistic modeling, scenario analysis, and collaboration between managers and analysts. Key benefits include intuitive visual modeling, live testing of assumptions, and validation of decisions. While mastering Analytica is challenging, it handles specialized modeling better than other tools and helps communicate complex analyses. Analytica supports advanced quantitative operations and simulations but could provide more templates and examples for novice users.
This document discusses performance engineering and global software development. It describes Infosys' approach which combines performance engineering practices with client delivery experience. This includes workload and performance modeling, benchmarking, tuning, and optimization methodologies to deliver high-performance systems with reduced costs and timelines. The key aspects of the approach are system requirements, modeling, performance testing and benchmarking, and optimization and tuning.
Prodev provides software solutions and consulting services to meet business needs. This includes office automation, engineering change management, database applications, and web development. Prodev aims to understand client requirements and implement efficient solutions that provide measurable return on investment and free up resources for primary job functions.
The document describes a Driverless ML API that was created to automate machine learning workflows including feature engineering, model validation, tuning, selection, and deployment. The API uses machine learning interpretability techniques to provide visualizations and explanations of models. It aims to help scale data science efforts and enable both expert and junior data scientists to more quickly develop accurate, production-ready models. Key capabilities of the API include automated exploratory data analysis, feature selection and engineering, model selection and hyperparameter tuning using GPUs for faster training, and model interpretability visualizations.
Solving big data challenges for enterprise applicationTrieu Dao Minh
This document discusses the challenges of application performance monitoring (APM) systems that deal with "big data". APM systems instrument enterprise applications to monitor metrics like response times and failures across distributed systems. This generates enormous amounts of monitoring data. The document evaluates six open-source data stores (Cassandra, HBase, Voldemort, Redis, VoltDB, MySQL Cluster) for their ability to handle the throughput of APM workloads in memory-bound and disk-bound cluster setups. It aims to provide performance results, lessons learned on setup complexity, and insights for using these data stores in an industrial APM system context.
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SOLIDWORKS reseller Whitepaper by Promedia Systems
1. ENHANCING DATA MANAGEMENT
WORKFLOWS THROUGH
CAD-INTEGRATED SIMULATION
White Paper
OVERVIEW
Design and manufacturing companies today rely heavily on simulation results as the
basis for business decisions. Managing an expanding simulation environment of tools,
data, and processes is becoming more and more important. Simulation results need to
be integrated with the enterprise’s overall product development environment; however,
companies of all sizes are still struggling to manage their simulation data, which
is increasing in size and complexity by orders of magnitude as more simulations are
performed over time.
This paper proposes a novel approach to managing simulation data workflows.
Processes can be enhanced through the use of integrated CAD tools so that the data
management, CAD, and simulation tools can talk to each other. This system provides a
powerful platform for companies to leverage and effectively implement the key data
management practices.
2. Enhancing Data Management Workflows Through CAD-Integrated Simulation 1
THE IMPORTANCE OF EFFECTIVE MANAGING SIMULATION DATA
Managed simulation data can be a competitive advantage but unmanaged data can become a huge
liability. Simulation data management processes and workflows do exist. They start with these five
key data management best practices:
• Provide a more collaborative environment
• Improve traceability of design simulation
• Increase data security
• Eliminate barriers between various groups and departments
• Enable improved assessment of risks and informed decisions
Many companies have adopted this strategy by implementing engineering workflows using data
management, CAD, and simulation tools. They are more successful than laggards in their respective
industries. However, they are still looking for a more integrated system to organize, control, find,
share, and secure intellectual data.
Manufacturing industries are under continuous pressure to deliver innovative, competitive products
faster. To meet this goal, they are increasing the use of simulation to better understand and validate
product behavior up front in the design cycle. This approach allows for more information and insights
to be captured early on, which in turn leads to making more informed design decisions. The key
question is whether there is a simulation workflow process in place to capture and manage all of
this data. Equally important, can intellectual simulation data somehow be reused to avoid duplicate
efforts and save time for designers and engineers, both new simulation users and expert users? Do
companies have some sort of best practices for this? Effective management of simulation data is
increasingly important as simulation becomes a core business process and organizations rely on
simulation results as the basis for business decisions.
THE BACKBONE OF SIMULATION DATA MANAGEMENT
The simulation process itself consists of multiple steps as shown in Figure 2. It starts off with the
CAD design data: preprocessing of information such as materials, loads, restraints, and mesh; solving
the simulation setup; post-processing of engineering results data; and final collaboration in the form
of reports.
When so many of these simulation runs are done on a multitude of designs and on different variations
of the same design, it is inevitable to have vast amounts of data generated either locally or on a
network computer. Many times, these data are simply created and destroyed or overwritten while
moving from one design to the next, or even from one simulation iteration to the next. The challenge
is not only in taming the enormous quantities of data, but also in building intelligence to improve
consistency, reliability, and repeatability. To do so, the following needs to be addressed in a simulation
data management workflow:
Figure 1: The pedigree of just
one Apollo spacecraft took this
many books.
3. Enhancing Data Management Workflows Through CAD-Integrated Simulation 2
• Document simulation activities efficiently to manage the process, procedures, and files for
better communication
• Retrieve and track all inputs and results to replicate and repeat simulation
• Store enough data to recreate simulation conditions
• Ease growing data management overhead on analysts
• Build a knowledge base to rapidly retrieve simulation information
• Share simulation methods across departments
• Manage and archive the process for audit trails
There are data management tools capable of being able to integrate the simulation workflow into
the main engineering workflow in which simulation files can be managed for different versions of
the design and simulation as well as the type of simulation done. However, addressing the key
challenges above requires more than just setting up a workflow. An intelligence framework needs to
be integrated into the workflow process that allows simulation users to quickly look up a CAD model
for any analysis information and be able to reuse existing data as a template or reference for doing a
new analysis. Some simulation tools also offer unique and intuitive functionality, like library features
for loads, fixtures, and virtual connectors,that provide a powerful platform for reusing information.
CREATING AN INTELLIGENT SIMULATION FRAMEWORK
Figure 3 highlights a flow chart of a data management tool framework in which existing simulation
data can be recaptured to build a knowledge base and intelligence to reuse information. For such a
framework to work effectively, an integrated environment is required where the data management
tool, the CAD software, and simulation tools can communicate with each other. A typical data
management system contains an archive server where all the files are stored and an SQL server where
all the metadata of the files are stored. The key is to exploit the integration between the CAD and
simulation tools using the metadata information as a means to search and retrieve simulation data
from CAD files. The data management tool now becomes the bridge between managing simulation
files and also querying simulation-specific information from the CAD files.
For example, a linear static simulation is performed on a particular design. This generates CAD files with
simulation setup, solver files, mesh files, reports, images, videos, and more, that all go into the data
management system and serve as a knowledge base. The integrated CAD and simulation environment
allows the data management tool to not only archive these files as a source of knowledge, but also
retrieve and store simulation information such as simulation type, materials, loads and fixtures, the
mesh. A simulation library and a family of CAD files with searchable simulation data are now created.
These can be retrieved anytime, anywhere, by anyone to perform future simulations.
Figure 2: Simulation stages
leading to enormous generation
of data
4. Enhancing Data Management Workflows Through CAD-Integrated Simulation 3
THE FIVE Ws OF THE SIMULATION WORKFLOW
The intelligent simulation framework can be easily incorporated into a simulation workflow. Figure 4
illustrates a typical flowchart for a simulation workflow. This flowchart requires creating more visibility
among different levels of users without reinventing the wheel or wasting time reviewing requirements.
For this workflow to be effective, some core elements must be incorporated based on the five Ws below:
• Who performed the simulation?
• What type of simulation(s) was performed?
• When was the simulation done?
• Where did the simulation data, such as geometry, material properties,
load conditions originate?
• Why was the simulation done?
Figure 3: Intelligent simulation
framework in a data
management system
Figure 4: Typical flowchart for a
simulation workflow
5. Enhancing Data Management Workflows Through CAD-Integrated Simulation 4
The simulation workflow now becomes a branch of the main engineering workflow. The result of this
approach is that now, we can not only manage but share simulation methods across cross-functional
departments to improve reliability, consistency, and communication. Figure 5 represents an outline
of what an entire engineering workflow with the integrated simulation workflow might look like.
Figure 6 is a close-up detail of the simulation workflow.
Figure 5: Outline of an
engineering workflow including
simulation workflow
Figure 6: Simulation workflow
branched off the engineering
workflow
6. Enhancing Data Management Workflows Through CAD-Integrated Simulation 5
Figure 7 illustrates a detailed simulation workflow incorporating building the intelligence framework
during each stage.
Figure 7: Detailed simulation
workflow
In the workflow shown in Figure 7, six process steps are executed among three users—the engineer,
the simulation engineer, and the simulation manager. Each user of this workflow can be assigned
different usage rights to different portions of the workflow. The workflow itself is designed and
executed as follows:
Step 1: The engineer creates CAD files (new design or a new version of an existing design) and
submits them for simulation requirements.
Step 2: The simulation manager gets an automatic notification from the data management workflow
and reviews the design and simulation requirements. If no simulation needs to be performed, then the
simulation manager submits the CAD file back to the engineering workflow in the data management
system. A notification is sent automatically to the appropriate person (example: engineering manager)
for final approval on the design.
Step 3: If the simulation manager decides that the design needs to go through the simulation
workflow, then it is assigned to the specific simulation engineer. The simulation manager can also
create specific simulation requirements. Figure 8 shows an example of how the data management
tool can be customized to capture and track the desired requirements set by the simulation manager.
For example, the simulation manager can check off “Linear Statics” and a flag for linear statics is created
in the CAD file, which becomes searchable.
A notification is sent automatically to the simulation engineer by the data management workflow system.
The simulation engineer can then access the custom interface to review the simulation requirements.
Figure 8: Example of custom
interface in data management
tool
7. Enhancing Data Management Workflows Through CAD-Integrated Simulation 6
Step 4: The simulation engineer reviews the requirements and gets ready to do the simulation. This
is where the intelligent simulation framework discussed earlier plays a crucial role. The simulation
engineer uses the data management system to search for similar CAD files that have simulation
information already built into them. Powerful search options can be built inside the data management
system, such as the one shown in Figure 9.
For example, the simulation engineer can search for a CAD file that contains the keyword “flange” and
contains a “Linear Statics” setup. The search comes back with a file that has already been through
the workflow approval process. The simulation engineer can simply preview or open the CAD file and
see the contents to get an idea of what the material, fixture, loads, mesh size, and other elements
look like, so that they can be easily replicated on the new design. This is shown in Figure 10.
Figure 9: Example of custom
interface in data management
tool
Figure 10: Searchable CAD file
with Simulation setup
8. Enhancing Data Management Workflows Through CAD-Integrated Simulation 7
Another way the simulation engineer can reuse simulation data is a smart library feature that can reside
in the data management system. Some of the CAD-integrated simulation tools offer this technology.
For example, if a new user needs to know which faces of the CAD geometry have a load or where a
fixture needs to be applied and how to apply it, library features offer very powerful options. Figures
11, 12, and 13 show how this might work in a CAD-integrated simulation environment.
• The simulation input, such as load or fixture, is first saved to a folder residing in the data
management system using the “Add to Library” option.
Figure 11: Creating a simulation
library feature
• The saved library feature can be simply dragged and dropped onto the CAD graphics window.
The appropriate dialog box opens up and the user simply needs to complete the geometry
selections. Figure 12 shows an example of a force load on a flange design. Note that the
library feature already has a predefined value for the load.
Figure 12: Reusing a simulation
library feature using drag and
drop
9. Enhancing Data Management Workflows Through CAD-Integrated Simulation 8
Simulation library features can also be created such that the drag and drop actually shows a preview
on the geometry selections to be made.
Figure 13 shows an example of dragging and dropping a symmetry fixture library feature. A preview
shows what geometry needs to be selected on the current design to apply that symmetry fixture.
Simulation library features serve as templates and are a great means of enforcing standard simulation
Figure 13: Reusing a simulation
library feature using drag and
drop
guidelines and best practices set forth by an organization. Most importantly, intelligent simulation
data have now become a knowledge base that can be reused anytime, anywhere, by anyone in an
organization.
After the simulation is done, the simulation engineer can create additional supporting documents
such as reports or videos, and make these part of the simulation workflow process as well. Figure 14
shows a Microsoft®
Word file report referenced with the CAD file on which the simulation was done.
Figure 14: A report file
referenced with CAD file in data
management system
10. Enhancing Data Management Workflows Through CAD-Integrated Simulation 9
The simulation engineer then submits the CAD file along with simulation data back into the simulation
workflow. A notification is automatically sent to the simulation manager.
Step 5: The simulation manager reviews whether all the simulation requirements have been completed
by the simulation engineer. If not, the CAD file is resubmitted to the simulation workflow and a
notification is automatically sent to the simulation engineer.
Step 6: If all requirements are completed, the simulation manager signs and approves the simulation
work and submits the CAD file and simulation data along with any supporting documentation to the
main engineering workflow. A notification is sent to the appropriate person (example: engineering
manager) for final design approval. Figure 15 shows the last stage of the simulation workflow
where the simulation manager sets the workflow status to “Simulation Approved.”
Figure 15: Simulation status set
to “Simulation Approved” by
simulation manager
Figure 16: Simulation workflow
history
Figure 16 illustrates the simulation workflow history as recorded by the data management system.
All information and activity, from the initial stages to the final stages of the workflow, is tracked and
monitored. The five Ws of simulation data management have been addressed.