To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static (background) data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases and applications become unresponsive.
In this paper, we address the problem of optimizing the evaluation of these queries by leveraging local views on background data. Local views enhance performance, but require maintenance processes, because changes in the background data sources are not automatically reflected in the application.
We propose a two-step query-driven maintenance process to maintain the local view: it exploits information from the query (e.g., the sliding window definition and the current window content) to maintain the local view based on user-defined Quality of Service constraints.
Experimental evaluation show the effectiveness of the approach.
This document proposes an unsupervised machine learning methodology to automatically set bus schedule coverage using AVL and APC data. The methodology involves 6 steps: 1) generating daily profiles from the data, 2) computing distances between daily profiles, 3) clustering similar days using GMM, 4) selecting the optimal number of clusters k by evaluating models using BIC and additional metrics, 5) merging results from all routes using consensual clustering, and 6) extracting understandable rules from the results. The methodology was tested on real data from a Swedish transit operator, and suggested changing to winter schedules earlier, improving on-time performance by 10% according to simulations.
The document summarizes the steps taken by a group to plan a project using a Gantt chart and network diagram. They identified the critical path and used two scenarios to reduce its duration and account for uncertainty using buffers. The cost was estimated at each step and weaknesses in the plan were discussed.
RuleML2015: GRAAL - a toolkit for query answering with existential rulesRuleML
This paper presents Graal, a java toolkit dedicated to ontological query answering in the framework of existential rules. We consider knowledge bases composed of data and an ontology expressed by existential rules. The main features of Graal are the following: a basic layer that provides generic interfaces to store and query various kinds of data, forward chaining and query rewriting algorithms, structural analysis of decidability properties of a rule set, a textual format and its parser, and import of OWL 2 files. We describe in more detail the query rewriting algorithms, which rely on original techniques, and report some experiments.
Asking “What?”, Automating the “How?”: The Vision of Declarative Performan...Jürgen Walter
Over the past decades, various methods, techniques, and tools for modeling and evaluating performance properties of software systems have been proposed covering the entire software life cycle. However, the application of performance engineering approaches to solve a given user concern is still rather challenging and requires expert knowledge and experience. There are no recipes on how to select, configure, and execute suitable methods, tools, and techniques allowing to address the user concerns. In this paper, we describe our vision of Declarative Performance Engineering (DPE), which aims to decouple the description of the user concerns to be solved (performance questions and goals) from the task of selecting and applying a specific solution approach. The strict separation of ``what'' versus ``how'' enables the development of different techniques and algorithms to automatically select and apply a suitable approach for a given scenario. The goal is to hide complexity from the user by allowing users to express their concerns and goals without requiring any knowledge about performance engineering techniques. Towards realizing the DPE vision, we discuss the different requirements and propose a reference architecture for implementing and integrating respective methods, algorithms, and tooling.
Mark Fulker is an IT professional with over 25 years of experience managing complex systems for clients such as National Grid. He has expertise in areas such as business continuity, incident management, problem management, and project management. Fulker has a track record of successfully delivering against tight SLAs, including an 85% reduction in outstanding work and transforming failing services. He is passionate about client focus and service excellence.
Monetizing Risks - A Prioritization & Optimization SolutionBlack & Veatch
This presentation explains a budget prioritization process and model that assists utilities with managing the important balance of asset/system performance, cost, and risk. Originally presentation at Texas Water 2015. Learn more at www.bv.com
Relevant Query Answering on Dynamic and Distributed DatasetsShima Zahmatkesh
This document discusses continuously evaluating relevant queries over streaming and distributed datasets. It proposes various maintenance policies for top-k continuous query answering using streams and distributed data. Preliminary results show that proposed policies like LRU.F+ and WBM.F* improve accuracy over state-of-the-art policies while maintaining sensitivity to parameters like refresh budget. Limitations include only considering join queries with filter clauses and top-k queries, as well as using a static rather than dynamic refresh budget.
KDP C is an important decision point for NASA projects where the agency decides whether to proceed to implementation and commits to a project's cost and schedule estimates. This panel discusses updated NASA processes to help ensure projects are on track for technical success within budget and schedule by KDP C. These include developing an integrated baseline, independent reviews, and documenting approvals and commitments in a decision memorandum to formalize support and establish external commitments. The integration of baseline development, independent checks, approval to proceed, and commitments is meant to help projects successfully complete implementation.
This document proposes an unsupervised machine learning methodology to automatically set bus schedule coverage using AVL and APC data. The methodology involves 6 steps: 1) generating daily profiles from the data, 2) computing distances between daily profiles, 3) clustering similar days using GMM, 4) selecting the optimal number of clusters k by evaluating models using BIC and additional metrics, 5) merging results from all routes using consensual clustering, and 6) extracting understandable rules from the results. The methodology was tested on real data from a Swedish transit operator, and suggested changing to winter schedules earlier, improving on-time performance by 10% according to simulations.
The document summarizes the steps taken by a group to plan a project using a Gantt chart and network diagram. They identified the critical path and used two scenarios to reduce its duration and account for uncertainty using buffers. The cost was estimated at each step and weaknesses in the plan were discussed.
RuleML2015: GRAAL - a toolkit for query answering with existential rulesRuleML
This paper presents Graal, a java toolkit dedicated to ontological query answering in the framework of existential rules. We consider knowledge bases composed of data and an ontology expressed by existential rules. The main features of Graal are the following: a basic layer that provides generic interfaces to store and query various kinds of data, forward chaining and query rewriting algorithms, structural analysis of decidability properties of a rule set, a textual format and its parser, and import of OWL 2 files. We describe in more detail the query rewriting algorithms, which rely on original techniques, and report some experiments.
Asking “What?”, Automating the “How?”: The Vision of Declarative Performan...Jürgen Walter
Over the past decades, various methods, techniques, and tools for modeling and evaluating performance properties of software systems have been proposed covering the entire software life cycle. However, the application of performance engineering approaches to solve a given user concern is still rather challenging and requires expert knowledge and experience. There are no recipes on how to select, configure, and execute suitable methods, tools, and techniques allowing to address the user concerns. In this paper, we describe our vision of Declarative Performance Engineering (DPE), which aims to decouple the description of the user concerns to be solved (performance questions and goals) from the task of selecting and applying a specific solution approach. The strict separation of ``what'' versus ``how'' enables the development of different techniques and algorithms to automatically select and apply a suitable approach for a given scenario. The goal is to hide complexity from the user by allowing users to express their concerns and goals without requiring any knowledge about performance engineering techniques. Towards realizing the DPE vision, we discuss the different requirements and propose a reference architecture for implementing and integrating respective methods, algorithms, and tooling.
Mark Fulker is an IT professional with over 25 years of experience managing complex systems for clients such as National Grid. He has expertise in areas such as business continuity, incident management, problem management, and project management. Fulker has a track record of successfully delivering against tight SLAs, including an 85% reduction in outstanding work and transforming failing services. He is passionate about client focus and service excellence.
Monetizing Risks - A Prioritization & Optimization SolutionBlack & Veatch
This presentation explains a budget prioritization process and model that assists utilities with managing the important balance of asset/system performance, cost, and risk. Originally presentation at Texas Water 2015. Learn more at www.bv.com
Relevant Query Answering on Dynamic and Distributed DatasetsShima Zahmatkesh
This document discusses continuously evaluating relevant queries over streaming and distributed datasets. It proposes various maintenance policies for top-k continuous query answering using streams and distributed data. Preliminary results show that proposed policies like LRU.F+ and WBM.F* improve accuracy over state-of-the-art policies while maintaining sensitivity to parameters like refresh budget. Limitations include only considering join queries with filter clauses and top-k queries, as well as using a static rather than dynamic refresh budget.
KDP C is an important decision point for NASA projects where the agency decides whether to proceed to implementation and commits to a project's cost and schedule estimates. This panel discusses updated NASA processes to help ensure projects are on track for technical success within budget and schedule by KDP C. These include developing an integrated baseline, independent reviews, and documenting approvals and commitments in a decision memorandum to formalize support and establish external commitments. The integration of baseline development, independent checks, approval to proceed, and commitments is meant to help projects successfully complete implementation.
The Green Belt project aims to reduce the learning curve of new hires in the supplemental keying lockbox process. Currently new hires are only able to achieve 5122 KSPH after 13 weeks of training, below the target of 8400 KSPH. The project team will focus on helping new hires achieve 6000 KSPH between weeks 14-17, a 17% increase over the current performance. Reducing the learning curve is expected to generate an additional 9.5 million keystrokes. The team will analyze the hiring, training, and on-the-job training processes to identify areas for improvement to reduce the time it takes for new hires to achieve productivity targets.
The document outlines a proposal for installing a wireless self-powered vibration monitoring system on 5 trains of the Bangkok Mass Transit System. The system would use vibration energy harvesters and wireless sensors to monitor vibration and temperature patterns. This would allow for remote monitoring of train conditions to reduce maintenance costs and increase safety. Key aspects of the proposal include the product description, assumptions, scope, schedule, budget, risk analysis and procurement plan. The goal is to shift from reactive maintenance to predictive maintenance based on sensor data.
This document provides a template for a Lean Six Sigma project following the DMAIC methodology. It outlines the sections and guidelines for the project, including defining the problem and goals, measuring the baseline and goals, analyzing inputs and processes, improving through solutions testing and validation, and controlling the improved process. The template aims to certify the project through Lean Six Sigma Academy and provide a summary for the organization. It includes sections for process mapping, identifying inputs, data analysis, determining improvement solutions, and validating the results meet goals.
The document discusses addressing the time/quality trade-off in view maintenance when querying linked data. It proposes optimizing maintenance to satisfy either quality constraints within the lowest response time or time constraints with the highest response quality. It describes summarizing a dataset to estimate query freshness and challenges with building individual summaries for each maintenance plan. The conclusion notes next steps are designing a more realistic dataset and comparing histogram and predicate multiplication approaches.
- Bibitor LLC is a fictitious liquor store chain that has asked a team to analyze inventory data over a 12-month period including beginning/ending inventory, purchases, and sales.
- Phase 3 of the case study introduces linear regression analysis to evaluate relationships between continuous variables in the data and identify trends. Students will select variables from two inventory case studies to analyze using linear regression visualizations in Tableau.
- The objective is to gain experience applying statistical analysis tools to leverage data for business decision making and gain an understanding of relationships that can provide insights.
- Bibitor LLC is a fictitious liquor store chain that has asked a team to analyze inventory data over a 12 month period including beginning/ending inventory, purchases, and sales.
- Phase 3 of the case study introduces linear regression analysis to evaluate relationships between continuous variables in the data and identify trends. Students will select variables from two inventory case studies to analyze using linear regression visualizations in Tableau.
- The objective is to gain experience applying statistical analysis tools to leverage data for business decision making and gain an understanding of relationships that can provide insights.
- Bibitor LLC is a fictitious liquor store chain that has asked a team to analyze inventory data over a 12 month period including beginning/ending inventory, purchases, and sales.
- Phase 3 of the case study introduces linear regression analysis to evaluate relationships between continuous variables in the data and identify trends. Students will select variables from two inventory case studies to analyze using linear regression visualizations in Tableau.
- The objective is to gain experience applying statistical analysis tools to leverage data for business decision making and gain an understanding of relationships between variables.
The project aims to develop a digital system to measure and convert time pulses from circuit breaker timing tests. It will use a microprocessor-controlled meter to convert analog voltage pulses to digital time readings with 0.1ms resolution. The 6-month project involves planning, requirements, design, integration, testing, validation and documentation. Key tasks include designing the conversion mechanism, integrating it with data collection equipment, and performing testing scenarios to validate performance. The project manager will oversee consultants and vendors to deliver the system on schedule and budget.
Matthew Egan End of Assignment Presentation 2nd RotationMatthew Egan
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Lean six sigma executive overview (case study) templatesSteven Bonacorsi
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Mortgage Data for Machine Learning AlgorithmsAnne Klieve
This document provides an overview of a project to build machine learning models to predict loan approval using Home Mortgage Disclosure Act (HMDA) data. It describes the data, features, exploratory analysis, data wrangling, model building process, and results. Several models were tested including logistic regression and random forest classifiers. The best models were able to predict loan approval with over 70% precision, recall, and F1 score. Further analysis and use of additional data sources could improve the models.
ML, Statistics, and Spark with Databricks for Maximizing Revenue in a Delayed...Databricks
In this talk, we will present how we used Spark, Databricks, Airflow and MLflow to process big data, and build a pipeline of both ML(XGBoost) and statistical models that maximizes our revenues in one of our core products, called the “Offer Wall”. The “Offer wall” is a mobile phone product that is integrated with existing apps, suggesting users to perform tasks in exchange for in-app currency. The problem gets even more interesting when considering the fact that some of the tasks users do take 15 minutes and some may take up to 2 to weeks, forcing us to make revenue determining decisions in an uncertain space all of the time. The solution we developed utilizes Databricks and Spark’s strengths and diversity in machine learning, big data, MLflow and Airflow integrations, allowing us to deliver a production-grade solution with short development time between experiments.
Dear students, get latest Solved NMIMS assignments and case study help by professionals.
Mail us at : help.mbaassignments@gmail.com
Call us at : 08263069601
"How to document your decisions", Dmytro Ovcharenko Fwdays
We will perform architecture kata around a proposed business case. We will review ADD in detail. How usually architecture vision document looks like. How to match your architecture drivers and proposed architecture decisions in architecture views. We will review what is ATAM and how to perform analysis of your decisions in the right way. And finally, we will create an architecture vision document from scratch.
Value Stream Mapping is a key component of Value Stream Management – the process by which Lean concepts and tools are utilized to minimize waste and promote one piece flow pulled by customer demand through the entire operation.
This document summarizes a workshop on CIP-002-5.1 hosted by Bryan Carr of the Western Electricity Coordinating Council (WECC). The workshop covered the requirements of CIP-002-5.1, transition guidance for moving to CIP Version 5, examples of evidence needed for compliance, lessons learned from other entities, and frequently asked questions. It also discussed upcoming WECC site visits to help entities prepare and understand their purpose.
Driving Innovation with Kanban at Jaguar Land RoverLeanKit
Find out how Kanban is accelerating product design and development at Jaguar Land Rover.
Watch the recorded webinar here: https://vimeo.com/172780037
Hamish McMinn, Automotive and IT Project Manager, will explain how Kanban is improving time, cost and quality across new vehicle development projects at Jaguar Land Rover.
You'll learn:
-Why new product development provides rich opportunities for continuous process improvement.
-Benefits and challenges of transferring agile software techniques to hardware design and development.
-How to visualize work, focus on flow and increase cross-functional collaboration using LeanKit.
Hamish will share learnings from the initial pilot project, and how Kanban is now being scaled across multiple engineering teams.
How should we estimates agile projects (CAST)Glen Alleman
“Why do so many big projects overspend and
overrun? They’re managed as if they were merely
complicated when in fact they are complex. They’re planned as if everything was known at the start when in fact they involve high levels of uncertainty and risk.” ‒ Architecting Systems: Concepts, Principles and Practice, Hillary Sillitto
PROJECT STORYBOARD: Reducing Learning Curve Ramp for Temp Employees by 2 WeeksGoLeanSixSigma.com
GoLeanSixSigma.com Black Belt Sean Halpin successfully used Lean Six Sigma methods in speeding up learning — with potential applications throughout the private and public sectors. He was able to not only reduce the time to develop employee capability, but was able to show achievement of higher capability levels than before the project.
Sean did a particularly thorough job in analyzing potential root causes and determining appropriate actions. He identified eight potential root causes, half of which proved to be real. A key finding was that training in how to deal with problems was particularly effective. Much training focuses on how things should be — not always considering common problems.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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The Green Belt project aims to reduce the learning curve of new hires in the supplemental keying lockbox process. Currently new hires are only able to achieve 5122 KSPH after 13 weeks of training, below the target of 8400 KSPH. The project team will focus on helping new hires achieve 6000 KSPH between weeks 14-17, a 17% increase over the current performance. Reducing the learning curve is expected to generate an additional 9.5 million keystrokes. The team will analyze the hiring, training, and on-the-job training processes to identify areas for improvement to reduce the time it takes for new hires to achieve productivity targets.
The document outlines a proposal for installing a wireless self-powered vibration monitoring system on 5 trains of the Bangkok Mass Transit System. The system would use vibration energy harvesters and wireless sensors to monitor vibration and temperature patterns. This would allow for remote monitoring of train conditions to reduce maintenance costs and increase safety. Key aspects of the proposal include the product description, assumptions, scope, schedule, budget, risk analysis and procurement plan. The goal is to shift from reactive maintenance to predictive maintenance based on sensor data.
This document provides a template for a Lean Six Sigma project following the DMAIC methodology. It outlines the sections and guidelines for the project, including defining the problem and goals, measuring the baseline and goals, analyzing inputs and processes, improving through solutions testing and validation, and controlling the improved process. The template aims to certify the project through Lean Six Sigma Academy and provide a summary for the organization. It includes sections for process mapping, identifying inputs, data analysis, determining improvement solutions, and validating the results meet goals.
The document discusses addressing the time/quality trade-off in view maintenance when querying linked data. It proposes optimizing maintenance to satisfy either quality constraints within the lowest response time or time constraints with the highest response quality. It describes summarizing a dataset to estimate query freshness and challenges with building individual summaries for each maintenance plan. The conclusion notes next steps are designing a more realistic dataset and comparing histogram and predicate multiplication approaches.
- Bibitor LLC is a fictitious liquor store chain that has asked a team to analyze inventory data over a 12-month period including beginning/ending inventory, purchases, and sales.
- Phase 3 of the case study introduces linear regression analysis to evaluate relationships between continuous variables in the data and identify trends. Students will select variables from two inventory case studies to analyze using linear regression visualizations in Tableau.
- The objective is to gain experience applying statistical analysis tools to leverage data for business decision making and gain an understanding of relationships that can provide insights.
- Bibitor LLC is a fictitious liquor store chain that has asked a team to analyze inventory data over a 12 month period including beginning/ending inventory, purchases, and sales.
- Phase 3 of the case study introduces linear regression analysis to evaluate relationships between continuous variables in the data and identify trends. Students will select variables from two inventory case studies to analyze using linear regression visualizations in Tableau.
- The objective is to gain experience applying statistical analysis tools to leverage data for business decision making and gain an understanding of relationships that can provide insights.
- Bibitor LLC is a fictitious liquor store chain that has asked a team to analyze inventory data over a 12 month period including beginning/ending inventory, purchases, and sales.
- Phase 3 of the case study introduces linear regression analysis to evaluate relationships between continuous variables in the data and identify trends. Students will select variables from two inventory case studies to analyze using linear regression visualizations in Tableau.
- The objective is to gain experience applying statistical analysis tools to leverage data for business decision making and gain an understanding of relationships between variables.
The project aims to develop a digital system to measure and convert time pulses from circuit breaker timing tests. It will use a microprocessor-controlled meter to convert analog voltage pulses to digital time readings with 0.1ms resolution. The 6-month project involves planning, requirements, design, integration, testing, validation and documentation. Key tasks include designing the conversion mechanism, integrating it with data collection equipment, and performing testing scenarios to validate performance. The project manager will oversee consultants and vendors to deliver the system on schedule and budget.
Matthew Egan End of Assignment Presentation 2nd RotationMatthew Egan
This document summarizes Matthew Egan's co-op rotation at Eaton Corporation. It details his assignments organizing Eaton's series ratings database and beginning the launch process for a new microinverter circuit breaker. It also outlines accomplishments like centralizing test data and estimating the potential market for the new breaker. Matthew thanks his managers for their support and looks forward to his third co-op rotation and future career as an LDP in technical sales.
Lean six sigma executive overview (case study) templatesSteven Bonacorsi
This case study describes a project to improve the average speed to answer calls at a retail business. The project team analyzed call data, identified root causes such as call type and time of day, and implemented cross-training and staffing changes. These improvements reduced customer downtime costs by $150,000 annually and increased the process sigma level. Key tools used in the project included data collection, analysis of call times, and control charts to monitor ongoing performance.
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This document provides an overview of a project to build machine learning models to predict loan approval using Home Mortgage Disclosure Act (HMDA) data. It describes the data, features, exploratory analysis, data wrangling, model building process, and results. Several models were tested including logistic regression and random forest classifiers. The best models were able to predict loan approval with over 70% precision, recall, and F1 score. Further analysis and use of additional data sources could improve the models.
ML, Statistics, and Spark with Databricks for Maximizing Revenue in a Delayed...Databricks
In this talk, we will present how we used Spark, Databricks, Airflow and MLflow to process big data, and build a pipeline of both ML(XGBoost) and statistical models that maximizes our revenues in one of our core products, called the “Offer Wall”. The “Offer wall” is a mobile phone product that is integrated with existing apps, suggesting users to perform tasks in exchange for in-app currency. The problem gets even more interesting when considering the fact that some of the tasks users do take 15 minutes and some may take up to 2 to weeks, forcing us to make revenue determining decisions in an uncertain space all of the time. The solution we developed utilizes Databricks and Spark’s strengths and diversity in machine learning, big data, MLflow and Airflow integrations, allowing us to deliver a production-grade solution with short development time between experiments.
Dear students, get latest Solved NMIMS assignments and case study help by professionals.
Mail us at : help.mbaassignments@gmail.com
Call us at : 08263069601
"How to document your decisions", Dmytro Ovcharenko Fwdays
We will perform architecture kata around a proposed business case. We will review ADD in detail. How usually architecture vision document looks like. How to match your architecture drivers and proposed architecture decisions in architecture views. We will review what is ATAM and how to perform analysis of your decisions in the right way. And finally, we will create an architecture vision document from scratch.
Value Stream Mapping is a key component of Value Stream Management – the process by which Lean concepts and tools are utilized to minimize waste and promote one piece flow pulled by customer demand through the entire operation.
This document summarizes a workshop on CIP-002-5.1 hosted by Bryan Carr of the Western Electricity Coordinating Council (WECC). The workshop covered the requirements of CIP-002-5.1, transition guidance for moving to CIP Version 5, examples of evidence needed for compliance, lessons learned from other entities, and frequently asked questions. It also discussed upcoming WECC site visits to help entities prepare and understand their purpose.
Driving Innovation with Kanban at Jaguar Land RoverLeanKit
Find out how Kanban is accelerating product design and development at Jaguar Land Rover.
Watch the recorded webinar here: https://vimeo.com/172780037
Hamish McMinn, Automotive and IT Project Manager, will explain how Kanban is improving time, cost and quality across new vehicle development projects at Jaguar Land Rover.
You'll learn:
-Why new product development provides rich opportunities for continuous process improvement.
-Benefits and challenges of transferring agile software techniques to hardware design and development.
-How to visualize work, focus on flow and increase cross-functional collaboration using LeanKit.
Hamish will share learnings from the initial pilot project, and how Kanban is now being scaled across multiple engineering teams.
How should we estimates agile projects (CAST)Glen Alleman
“Why do so many big projects overspend and
overrun? They’re managed as if they were merely
complicated when in fact they are complex. They’re planned as if everything was known at the start when in fact they involve high levels of uncertainty and risk.” ‒ Architecting Systems: Concepts, Principles and Practice, Hillary Sillitto
PROJECT STORYBOARD: Reducing Learning Curve Ramp for Temp Employees by 2 WeeksGoLeanSixSigma.com
GoLeanSixSigma.com Black Belt Sean Halpin successfully used Lean Six Sigma methods in speeding up learning — with potential applications throughout the private and public sectors. He was able to not only reduce the time to develop employee capability, but was able to show achievement of higher capability levels than before the project.
Sean did a particularly thorough job in analyzing potential root causes and determining appropriate actions. He identified eight potential root causes, half of which proved to be real. A key finding was that training in how to deal with problems was particularly effective. Much training focuses on how things should be — not always considering common problems.
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Approximate Continuous Query Answering Over Streams and Dynamic Linked Data Sets
1. oheila Dehghanzadeh, Daniele Dell’Aglio, Shen Gao,
manuele Della Valle, Alessandra Mileo , Abraham Bernstein
ICWE - 25 June
2. Outline
● Introduction to Continous Queries
● Motivating Example
● Problem Description
● Solution
● Experimental Results
● Conclusions
2ICWE - 25 June 2015
3. Introduction
DF Stream Processing engines usually register queries
and execute them in a continuous fashion.
3ICWE - 25 June 2015
RDF Stream
Generator
Query
5. Introduction
omplex continuous queries combines data streams with
remote background data.
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
5ICWE - 25 June 2015
6. Motivating Example
Finding Influential Users
nfluential User: users who have more than a specific number of
followers and are mentioned more than a specific times in a specific
period (200 seconds).
ollower number: stored in a remote endpoint.
ention number: computed by processing the stream of messages.
6ICWE - 25 June 2015
Inspired by Chris Testa's SemTech 2011 talk: http://goo.gl/kLSqGo
7. Investigating the Scenario
Symmetrical hash join
rawbacks:
• Data access constraints.
• Background data is huge and has to be fetched at every
evaluation - slow and wasting computational and financial
resources.
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
7ICWE - 25 June 2015
8. Investigating the Scenario
Nested Loop Join
rawbacks:
• One invocation for each mapping from the WINDOW
clause evaluation – high number of requests to the server.
• API restrictions (e.g., limited amount of requests over
time).
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
8ICWE - 25 June 2015
9. Investigating the Scenario
Local Views
hallenges:
• Data goes out of date
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
Local
View
9ICWE - 25 June 2015
10. Investigating the Scenario
Maintenance processes
aintenance introduces a trade-off between response quality and
time.
e propose to manage this trade-off by fixing time dimension
based on query constraints and maximizing freshness of response.
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
Local
View
Maintenance
Process
Freshness decreases
Refresh
Cost/Quality trade-
off
10ICWE - 25 June 2015
11. Problem Description
The maintenance process should identify elements of the local
view that maximize response freshness.
11ICWE - 25 June 2015
12. Requirements of The Maintenance Process
1. should satisfy the Quality of Service constraints
on responsiveness and freshness of the answer;
2. should take into account the change rates of the
data elements in the REST API;
3. should consider the dynamicity of the change
rate values;
4. may consider the sliding window operator.
12ICWE - 25 June 2015
13. Hypotheses
e formulated the following hypotheses to build the maintenance
process
P1: the freshness of the answer can increase by maintaining part
of the local view involved in the current query evaluation
P2: the freshness of the answer increases by refreshing the
(possibly) stale local view entries that would remain fresh in a
higher number of evaluations
13ICWE - 25 June 2015
15. τ
t5 6 7 8 9 10 11
W1 W2 W3 W4
124
5 6 7 8 9 10 11 124
Terminology
Best Before Time: the time
that an element will
become stale and is defined
by:
Mappings from the
WINDOW clause
Mappings in the
LOCAL VIEW
Compatible
mappings
15ICWE - 25 June 2015
16. τ
t5 6 7 8 9 10 11
W1 W2 W3 W4
124
5 6 7 8 9 10 11 124
WSJ
SJ identifies the candidate
set: the possibly stale local
view mappings involved in
the current evaluation.
SJ analyzes the content of the
current window evaluation
and identifying the
compatible mappings in the
local view.
he possibly stale mappings
are identified by analyzing
the associated best before 16ICWE - 25 June 2015
17. V L Score
τ
t5 6 7 8 9 10 11
W1 W2 W3 W4
124
5 6 7 8 9 10 11 124
WBM
BM ranks the candidate set
to determine which
mappings to update.
he ranking is computed
through two values: the
renewed best before time
and the remaining life time
he top k elements are
selected to be refreshed. The
value k is selected according
to the responsiveness
constraint. 17ICWE - 25 June 2015
18. V L Score
3
4
1
τ
t5 6 7 8 9 10 11
W1 W2 W3 W4
124
5 6 7 8 9 10 11 124
WBM: renewed best before time
hen would the mappings
became stale if refreshed
now?
he renewed best before time
V is computed as:
18ICWE - 25 June 2015
19. V L Score
3 3
4 1
1 3
τ
t5 6 7 8 9 10 11
W1 W2 W3 W4
124
5 6 7 8 9 10 11 124
WBM: remaining life time and score
or how many future
evaluations the mappings is
involved?
he remaining life time L is
computed as:
BM ranks the mappings by
using a score:
core=min(L,V)
19ICWE - 25 June 2015
20. Experiment- Data Collection
1. Streaming API
a. Twitter stream data for mention count
2. Twitter APIs to get number of followers
a. Create snapshots everyone minutes
b. Simulate the change based on user’s predefined change rates.
Streaming
Dataset
Snapshots
/synthetic
data
20ICWE - 25 June 2015
21. Experimental setup
e study our hypotheses using a comparative evaluation with
• LRU: use the least recently updated elements for maintenance
• RND: use a random subset of elements for maintenance
rror measure
• Comparing the differences between consecutive evaluation of the
motivated query against cache and real/synthetic dataset.
P1: We compared the cumulative staleness of using WSJ or not (i.e.,
GNR) for both baselines.
• GNR: candidate set is the whole view entries.
P2: We compared the cumulative staleness of using WBM and the
improved baselines.
21ICWE - 25 June 2015
22. HP1: Maintaining involved entries of local view maximizes response
accuracy.
Synthetic Real
WSJ shows better improvement by increasing the update budget than GNR.
22ICWE - 25 June 2015
23. HP2: Maintaining possibly stale entries from local view that will stay
fresh for a longer time maximizes response accuracy.
Synthetic Real
WBM doesn’t improve as well as WBM* which shows the estimation error
has caused by wrong estimation for BBT. Use more accurate prediction
for BBT.
23ICWE - 25 June 2015
24. Conclusions and Future Work
onclusions:
• We proposed using the idea of materialization to optimize processing
continuous queries.
• We proposed a policy to maximize the freshness according to time
constraint in continuous query.
• We tested our policy against based line policies (LRU and Random).
uture Work:
• Extensions of real continuous query processors with the proposed
approach
• Measuring the time overhead of maintenance
• Investigating more complex queries that have complicated join patterns
between the SERVICE and STREAM clauses.
• Dynamically estimating the change rate of users.
24ICWE - 25 June 2015
25. Slide
25
Soheila Dehghanzadeh, Daniele Dell’Aglio, Shen Gao,
Emanuele Della Valle, Alessandra Mileo , Abraham Bernstein
soheila.dehghanzadeh@insight-centre.org
http://www.slideshare.net/sallyde
ICWE - 25 June 2015
Editor's Notes
We motivate this work with a semtec talk
Problem is very specific, you should generalize it to other cases
How many time units we consider for one window
How many time units we slide the window to create the next window
Here we introduce some notions that we will use them over the window
In order to produce the stream of influential users over time, we need to access mention stream and follower’s data from REST API.
A sketch of the query in a continues query language
The less we maintain the faster we can process queries, but how much less? How to minimize the maintenance?
Extension: to consider all users from the stream, if a user doesn’t exist in the local view, we fetch it and replace it with one of the existing entries from the local view
The less we maintain the faster we can process queries, but how much less? How to minimize the maintenance?
Extension: to consider all users from the stream, if a user doesn’t exist in the local view, we fetch it and replace it with one of the existing entries from the local view
Our goal is to minimize the maintenance based on constraints on QoS as the cost function
If an entry stays fresh for a longer time but its life in window is short we choose entries that are staying longer in window
(B+D)/(A+B+C+D)
A=false positive
B= true positive
C=false negative
D=true negative
An efficient maintenance process should take into account the change rates of cached data as well as dynamics of the change rates , constraints on quality of service and definition of sliding window to optimally maintain the data.
Wbm picks the top-k based on the time constraints of the query , send them to refresher to maintain the local view only for that particular subset.
The maintenance policy will be done online at every evaluation of the sliding window to maintain the local view
It uses the content of the current window as well as the statistics of change rates to pick a sub-set of the local view which will be passed to maintainer to fetch the rest API and re-write the content of those elements only for that particular sub-set.
Our proposed solution uses the change rates(R1) to identify stale mappings (red,green,blue and pink)
Our proposed solution uses window definition (R4) to identify the involved elements. (red,yellow,blue and green)
So WSJ only considers the intersection which is red,green and blue
Our proposed solution uses the change rates(R1) to identify stale mappings (red,green,blue and pink)
Our proposed solution uses window definition (R4) to identify the involved elements. (red,yellow,blue and green)
So WSJ only considers the intersection which is red,green and blue
Our proposed solution uses the change rates(R1) to identify stale mappings (red,green,blue and pink)
Our proposed solution uses window definition (R4) to identify the involved elements. (red,yellow,blue and green)
So WSJ only considers the intersection which is red,green and blue
Our proposed solution uses the change rates(R1) to identify stale mappings (red,green,blue and pink)
Our proposed solution uses window definition (R4) to identify the involved elements. (red,yellow,blue and green)
So WSJ only considers the intersection which is red,green and blue
Our proposed solution uses the change rates(R1) to identify stale mappings (red,green,blue and pink)
Our proposed solution uses window definition (R4) to identify the involved elements. (red,yellow,blue and green)
So WSJ only considers the intersection which is red,green and blue
To investigate the first hypothesis, we investigate the effect if including(WSJ) or excluding(GNR) proposer in the maintenance process and for the ranker we used the 2 baselines.
WST= no maintenanceBST= If proposer just select stale-involved elements from the local view based on the update budget