This document discusses machine learning methods for big data applications. It covers topics like classification learning, sampling, dimensionality reduction, and scaling up existing algorithms for big data. Specific algorithms mentioned include principal components analysis, selective KDB, and incremental Bayesian network classifiers. The document is a copyrighted overview presented by Geoffrey I Webb and colleagues at Monash University.
The document describes a system called HOLMES that uses complex event processing (CEP) and machine learning to monitor data centers. It analyzes streams of events using CEP to detect known problems and an anomaly detection algorithm to find unknown issues. A visualization module provides real-time dashboards and historical views of events. The system was successfully implemented and accepted in a large production environment.
This document summarizes recent work on improving top-N recommender systems using item-based neighborhood methods. It describes two approaches: 1) estimating a sparse item-item similarity matrix directly from training data using structural equation modeling, and 2) extending this framework to estimate a factored item-item similarity matrix to handle sparse datasets. It also discusses incorporating item side information to improve recommendations and address cold-start problems.
MAPP is a multi-agent planning platform that generates course timetables by using different optimization algorithms run in parallel by multiple solver agents. The mediator agent distributes the scheduling problem to solver agents, collects their solutions, and sends the highest scoring schedules to users. Testing showed the parallel approach solved problems significantly faster than running algorithms serially.
Pushing the rule engine to its limits with drools planner (parisjug 2010-11-09)Geoffrey De Smet
This document provides an overview of using Drools Planner to solve complex planning and scheduling problems. It discusses use cases like bin packing, employee scheduling, and patient admission scheduling. It explains how Drools Planner can handle both hard and soft constraints. The document notes that the search space for some problems can be extraordinarily large, like the patient admission scheduling problem presented, which has over 10^6851 possible solutions. It recommends using metaheuristic algorithms like local search to find good but not necessarily perfect solutions in a reasonable time, as brute force approaches are not feasible for such large problems.
This document provides a summary of a student's seminar paper on resource scheduling algorithms. The paper discusses the need for resource scheduling algorithms in cloud computing environments. It then describes several types of algorithms commonly used for resource scheduling, including genetic algorithms, bee algorithms, ant colony algorithms, workflow algorithms, and load balancing algorithms. For each algorithm type, it provides a brief introduction, overview of the basic steps or concepts, and some examples of applications where the algorithm has been used. The paper was submitted by a student named Shilpa Damor to fulfill requirements for a degree in information technology.
Drools is a Rule Engine that uses the rule-based approach to implement an Expert System
The inference engine matches the rules against the facts (objects) in memory and can match the next set of rules based on the changed facts.
Please use the presentation and the source code referred in the presentation to get started on what a rule engine is and how to use JBoss Drools for inference based rules using the Java programming language.
The document discusses how schools can implement digital learning for all students using resources they already have. It proposes a BYOD (bring your own device) model where students bring their own laptops or tablets to school, along with open source software and digital course materials, rather than expensive printed textbooks. This low-cost digital access model could help prepare students for 21st century learning and life. The document outlines key factors schools must consider for a successful BYOD implementation, including engaging parents, ensuring internet safety, providing technical support, and selecting appropriate devices, software, and materials.
The document outlines a plan for schools to implement digital learning for all students using resources they already have. It proposes that schools embrace bring-your-own-device (BYOD) policies, use open-source software and digital materials, and leverage existing community WiFi networks. This would allow schools to move from an expensive, print-based model to a low-cost digital model. The document provides a framework for critical decisions around BYOD implementation, including engagement, infrastructure, hardware, student safety, and software/materials. It presents an implementation timeline with phases for decision making, planning, and executing the transition to digital learning for all students.
The document describes a system called HOLMES that uses complex event processing (CEP) and machine learning to monitor data centers. It analyzes streams of events using CEP to detect known problems and an anomaly detection algorithm to find unknown issues. A visualization module provides real-time dashboards and historical views of events. The system was successfully implemented and accepted in a large production environment.
This document summarizes recent work on improving top-N recommender systems using item-based neighborhood methods. It describes two approaches: 1) estimating a sparse item-item similarity matrix directly from training data using structural equation modeling, and 2) extending this framework to estimate a factored item-item similarity matrix to handle sparse datasets. It also discusses incorporating item side information to improve recommendations and address cold-start problems.
MAPP is a multi-agent planning platform that generates course timetables by using different optimization algorithms run in parallel by multiple solver agents. The mediator agent distributes the scheduling problem to solver agents, collects their solutions, and sends the highest scoring schedules to users. Testing showed the parallel approach solved problems significantly faster than running algorithms serially.
Pushing the rule engine to its limits with drools planner (parisjug 2010-11-09)Geoffrey De Smet
This document provides an overview of using Drools Planner to solve complex planning and scheduling problems. It discusses use cases like bin packing, employee scheduling, and patient admission scheduling. It explains how Drools Planner can handle both hard and soft constraints. The document notes that the search space for some problems can be extraordinarily large, like the patient admission scheduling problem presented, which has over 10^6851 possible solutions. It recommends using metaheuristic algorithms like local search to find good but not necessarily perfect solutions in a reasonable time, as brute force approaches are not feasible for such large problems.
This document provides a summary of a student's seminar paper on resource scheduling algorithms. The paper discusses the need for resource scheduling algorithms in cloud computing environments. It then describes several types of algorithms commonly used for resource scheduling, including genetic algorithms, bee algorithms, ant colony algorithms, workflow algorithms, and load balancing algorithms. For each algorithm type, it provides a brief introduction, overview of the basic steps or concepts, and some examples of applications where the algorithm has been used. The paper was submitted by a student named Shilpa Damor to fulfill requirements for a degree in information technology.
Drools is a Rule Engine that uses the rule-based approach to implement an Expert System
The inference engine matches the rules against the facts (objects) in memory and can match the next set of rules based on the changed facts.
Please use the presentation and the source code referred in the presentation to get started on what a rule engine is and how to use JBoss Drools for inference based rules using the Java programming language.
The document discusses how schools can implement digital learning for all students using resources they already have. It proposes a BYOD (bring your own device) model where students bring their own laptops or tablets to school, along with open source software and digital course materials, rather than expensive printed textbooks. This low-cost digital access model could help prepare students for 21st century learning and life. The document outlines key factors schools must consider for a successful BYOD implementation, including engaging parents, ensuring internet safety, providing technical support, and selecting appropriate devices, software, and materials.
The document outlines a plan for schools to implement digital learning for all students using resources they already have. It proposes that schools embrace bring-your-own-device (BYOD) policies, use open-source software and digital materials, and leverage existing community WiFi networks. This would allow schools to move from an expensive, print-based model to a low-cost digital model. The document provides a framework for critical decisions around BYOD implementation, including engagement, infrastructure, hardware, student safety, and software/materials. It presents an implementation timeline with phases for decision making, planning, and executing the transition to digital learning for all students.
Large Scale Data Mining using Genetics-Based Machine Learningjaumebp
This document discusses techniques for large scale data mining using genetics-based machine learning. It begins by defining what "large scale" means in the context of data mining, including datasets with many records, high dimensionality, class imbalance, and many classes. It then discusses how evolutionary algorithms are naturally parallel and suited for large scale problems. The challenges of data mining at large scales are outlined, particularly related to data handling and representation. Finally, the document introduces several kaleidoscopic techniques for large scale data mining using genetic-based machine learning, including efficiency enhancement techniques like windowing, exploiting regularities in the data, fitness surrogates, and hybrid methods, as well as hardware acceleration techniques and parallelization models.
This document discusses active learning by outlier detection for handling inconsistencies and outliers. It focuses on model outliers in labeled data where the current model is inaccurate. Typically outliers are discarded, but the author proposes keeping outliers and changing the model instead. Obtaining additional labeled data is costly, so the goal is to learn from existing labeled data, even if inconsistent, by making minor tweaks to the model.
071510 sun b_1515_feldman_stephen_forpublicSteve Feldman
This document discusses scaling Blackboard to support large online learning communities. It notes that online enrollments are growing significantly faster than overall higher education. Communities and stakes are getting larger as competition increases for students and funding. Blackboard must support larger class sizes, richer content, extended user sessions, and near 24/7 availability. This requires scalable, high-performance, and highly available systems through techniques like virtualization, fast provisioning, emphasis on asynchronous tools and databases, and advanced monitoring.
Building a decision tree from decision stumpsMurphy Choy
The document discusses building decision tree stumps from full decision trees. It introduces decision trees and defines a decision tree stump. It compares the CART and CHAID algorithms, noting CART handles different data types better and uses more robust statistics. It discusses splitting criteria like Gini impurity and building a SAS macro to create a decision tree stump by recursively splitting on the variable with the maximum Gini decrease. Finally, it notes decision tree stumps can be linked to build a full decision tree.
The document discusses how schools can implement digital learning for all students using existing resources. It argues that the future of learning and work is digital, and limited access to digital resources limits students' mastery. Every printed page is a waste when digital alternatives exist. The document proposes that schools can achieve digital learning for all through BYOD programs, open source software, and digital materials. This approach reduces costs while increasing student engagement over traditional print and software models. Schools should focus on equitably providing digital access to all students. The document outlines goals and considerations for teaching and learning, leadership, and resources to guide schools in transitioning to digital learning models.
The document describes ImaginLabs, a startup that provides automated sub-pixel registration of satellite imagery via their proprietary technology, with an initial focus on applications in agriculture including crop monitoring for consulting companies. It outlines their technology, experiments contacting potential customer segments like NASA and USDA, and a shift to focus solely on the agriculture market where they received the most promising feedback.
The Modern Columbian Exchange: Biovision 2012 PresentationMerck
The Columbian Exchange is a term used to capture what happened to North American Native Indians when the arrival of European settlers introduced ideas, animals, plants, and diseases that otherwise they had not yet been exposed to. Today, the Modern Columbian Exchange is occurring at a global scale, caused by unprecedented global travel and the Internet. An outcome of this Modern Columbian Exchange is disease outbreaks which have and will continue to affect dozens of countries in a very short time, impacting agriculture, tourism, and ultimately resulting in social tensions and the loss of life. The global response requires tight and timely coordination across countries. This necessitates the processing of large volumes of data – “BIG DATA” – which implies variety, variability and velocity. In this presentation, we explore the challenges of BIG DATA for preventative global health care. We answer the questions: a) how can human intelligence be more effectively leveraged to develop new insights, and b) how does this impact the design of data and information repositories? We conclude “The Time is NOW” for a new real-time analytics paradigm to transform the discovery and learning process.
The document discusses approaches to maximizing knowledge bases (KBs) about publications, packages, licenses, and subscriptions. It notes that the "Big Four" KBs employ around 80 full-time employees for maintenance but have minor differences, and efforts are duplicated. It advocates for open data, collaborative communities, enriched information, and standards/best practices to improve data quality and availability, reduce duplication of effort, and increase interoperability. The goal is to maximize KBs by sharing publication and package information openly to seed all systems.
The document discusses the need for digital learning for all students to prepare them for the 21st century. It argues that the world has become flat, digital, and constantly changing, requiring new skills. It proposes providing every student with a device and open digital materials to achieve digital learning for all. The implementation would occur over three phases: decision making, planning, and execution. The goal is to change systems, culture and leadership to support digital learning and close equity gaps.
This document discusses emerging trends in education that are being driven by new technologies and models. It covers topics such as the rise of online and self-directed learning through platforms like Khan Academy and MOOCs. Competency-based models and stackable credentials that decouple learning from traditional courses are also discussed. The use of data and analytics to personalize learning and improve student outcomes is highlighted. New partnerships and business models between educational institutions and other organizations are changing the traditional value chains in higher education.
Given its ability to analyze structured, unstructured, and "multi-structured" data, Hadoop is an increasingly viable option for analytics and business intelligence within the enterprise. Dramatically more scalable and cost-effective than traditional data warehousing technologies, Hadoop is also increasingly used to perform new kinds of analytics that were previously impossible. When it comes to Big Data, retailers are at the forefront of leveraging large volumes of nuanced information about customers, to improve the effectiveness of promotional campaigns, refine pricing models, and lower overall customer acquisition costs. Retailers compete fiercely for consumers' attention, time, and money, and effective use of analytics can result in sustained competitive advantage. Forward-thinking retailers can now take advantage of all data sources to construct a complete picture of a customer. This invariably consists of both structured data (customer and inventory records, spreadsheets, etc.) and unstructured data (clickstream logs, email archives, customer feedback and comment fields, etc.). This allows, for example, online retailers with structured, transactional sales data to connect that data with unstructured comments from product reviews, providing insight into how reviews affect consumers' propensity to purchase a particular product. This session will examine several real-world customer use cases applying combined analysis of structured and unstructured data.
Supporting Libraries in Leading the Way in Research Data ManagementMarieke Guy
Marieke Guy, Institutional Support Officer, Digital Curation Centre, UKOLN, University of Bath, UK presents on Supporting Libraries in Leading the Way in Research Data Management at Online Information, London 20th -21st November 2012
Goncol CoEs Service Presentation Short ENTabea Hirzel
The Göncöl Foundation is a non-profit environmental foundation in Hungary established in 1985 that works to preserve nature, human, and social values. It has over 15 years of experience in education, communication, and conservation projects. The foundation provides services such as competency development training, field research, and organizing conferences to support sustainable development. It partners with other organizations and the private sector to address environmental issues and support biodiversity conservation.
The document introduces COBWEB, a research project that develops a crowdsourcing infrastructure for collecting and analyzing environmental data provided by citizens. The project aims to address data quality issues and support policy decisions. It has several pilot sites and partners, including UNESCO biosphere reserves. The framework includes mobile apps, QA processes, and a portal to view and analyze citizen-submitted data. It uses open standards and aims to be customizable for different use cases involving topics like biological monitoring and flooding.
using Self-Supervised Learning Can Improve Model Robustness and uncertainty....ssuserbafbd0
Self-supervised learning using rotation prediction can improve model robustness and uncertainty. Models trained with this method showed improved robustness to common corruptions like noise, blur and weather effects. They also showed robustness to adversarial perturbations and label corruptions. These models were better able to detect out-of-distribution examples. Ablation studies demonstrated that self-attention helps networks learn shape and compare regions, aiding in out-of-distribution detection.
If Big Data is data that exceeds the processing capacity of conventional systems, thereby necessitating alternative processing measures, we are looking at an essentially technological challenge that IT managers are best equipped to address.
The DCC is currently working with 18 HEIs to support and develop their capabilities in the management of research data and, whilst the aforementioned challenge is not usually core to their expressed concerns, are there particular issues of curation inherent to Big Data that might force a different perspective?
We have some understanding of Big Data from our contacts in the Astronomy and High Energy Physics domains, and the scale and speed of development in Genomics data generation is well known, but the inability to provide sufficient processing capacity is not one of their more frequent complaints.
That’s not to say that Big Science and its Big Data are free of challenges in data curation; only that they are shared with their lesser cousins, where one might say that the real challenge is less one of size than diversity and complexity.
This brief presentation explores those aspects of data curation that go beyond the challenges of processing power but which may lend a broader perspective to the technology selection process.
CVPR2022 paper reading - Balanced multimodal learning - All Japan Computer Vi...Antonio Tejero de Pablos
Introduction of the CVPR2022 paper: Balanced multimodal learning via on-the-fly gradient modulation @ The All Japan Computer Vision Study Group (2022/08/07)
Webinar: How We Evaluated MongoDB as a Relational Database ReplacementMongoDB
This webinar will explain the process, methodology, and results used at Apollo Group to evaluate MongoDB and ultimately replace Oracle for a core platform component.
The document introduces COBWEB, a European Commission-funded project that develops a crowdsourcing infrastructure for collecting and analyzing environmental data. It summarizes the goals of the project, its partners which include UNESCO biosphere reserves, methods for co-designing use cases, and the development of quality assurance processes and mobile/web apps. Key components under development include workflows, services, sensor networks, and tools for customizing data collection and ensuring data quality.
Large Scale Data Mining using Genetics-Based Machine Learningjaumebp
This document discusses techniques for large scale data mining using genetics-based machine learning. It begins by defining what "large scale" means in the context of data mining, including datasets with many records, high dimensionality, class imbalance, and many classes. It then discusses how evolutionary algorithms are naturally parallel and suited for large scale problems. The challenges of data mining at large scales are outlined, particularly related to data handling and representation. Finally, the document introduces several kaleidoscopic techniques for large scale data mining using genetic-based machine learning, including efficiency enhancement techniques like windowing, exploiting regularities in the data, fitness surrogates, and hybrid methods, as well as hardware acceleration techniques and parallelization models.
This document discusses active learning by outlier detection for handling inconsistencies and outliers. It focuses on model outliers in labeled data where the current model is inaccurate. Typically outliers are discarded, but the author proposes keeping outliers and changing the model instead. Obtaining additional labeled data is costly, so the goal is to learn from existing labeled data, even if inconsistent, by making minor tweaks to the model.
071510 sun b_1515_feldman_stephen_forpublicSteve Feldman
This document discusses scaling Blackboard to support large online learning communities. It notes that online enrollments are growing significantly faster than overall higher education. Communities and stakes are getting larger as competition increases for students and funding. Blackboard must support larger class sizes, richer content, extended user sessions, and near 24/7 availability. This requires scalable, high-performance, and highly available systems through techniques like virtualization, fast provisioning, emphasis on asynchronous tools and databases, and advanced monitoring.
Building a decision tree from decision stumpsMurphy Choy
The document discusses building decision tree stumps from full decision trees. It introduces decision trees and defines a decision tree stump. It compares the CART and CHAID algorithms, noting CART handles different data types better and uses more robust statistics. It discusses splitting criteria like Gini impurity and building a SAS macro to create a decision tree stump by recursively splitting on the variable with the maximum Gini decrease. Finally, it notes decision tree stumps can be linked to build a full decision tree.
The document discusses how schools can implement digital learning for all students using existing resources. It argues that the future of learning and work is digital, and limited access to digital resources limits students' mastery. Every printed page is a waste when digital alternatives exist. The document proposes that schools can achieve digital learning for all through BYOD programs, open source software, and digital materials. This approach reduces costs while increasing student engagement over traditional print and software models. Schools should focus on equitably providing digital access to all students. The document outlines goals and considerations for teaching and learning, leadership, and resources to guide schools in transitioning to digital learning models.
The document describes ImaginLabs, a startup that provides automated sub-pixel registration of satellite imagery via their proprietary technology, with an initial focus on applications in agriculture including crop monitoring for consulting companies. It outlines their technology, experiments contacting potential customer segments like NASA and USDA, and a shift to focus solely on the agriculture market where they received the most promising feedback.
The Modern Columbian Exchange: Biovision 2012 PresentationMerck
The Columbian Exchange is a term used to capture what happened to North American Native Indians when the arrival of European settlers introduced ideas, animals, plants, and diseases that otherwise they had not yet been exposed to. Today, the Modern Columbian Exchange is occurring at a global scale, caused by unprecedented global travel and the Internet. An outcome of this Modern Columbian Exchange is disease outbreaks which have and will continue to affect dozens of countries in a very short time, impacting agriculture, tourism, and ultimately resulting in social tensions and the loss of life. The global response requires tight and timely coordination across countries. This necessitates the processing of large volumes of data – “BIG DATA” – which implies variety, variability and velocity. In this presentation, we explore the challenges of BIG DATA for preventative global health care. We answer the questions: a) how can human intelligence be more effectively leveraged to develop new insights, and b) how does this impact the design of data and information repositories? We conclude “The Time is NOW” for a new real-time analytics paradigm to transform the discovery and learning process.
The document discusses approaches to maximizing knowledge bases (KBs) about publications, packages, licenses, and subscriptions. It notes that the "Big Four" KBs employ around 80 full-time employees for maintenance but have minor differences, and efforts are duplicated. It advocates for open data, collaborative communities, enriched information, and standards/best practices to improve data quality and availability, reduce duplication of effort, and increase interoperability. The goal is to maximize KBs by sharing publication and package information openly to seed all systems.
The document discusses the need for digital learning for all students to prepare them for the 21st century. It argues that the world has become flat, digital, and constantly changing, requiring new skills. It proposes providing every student with a device and open digital materials to achieve digital learning for all. The implementation would occur over three phases: decision making, planning, and execution. The goal is to change systems, culture and leadership to support digital learning and close equity gaps.
This document discusses emerging trends in education that are being driven by new technologies and models. It covers topics such as the rise of online and self-directed learning through platforms like Khan Academy and MOOCs. Competency-based models and stackable credentials that decouple learning from traditional courses are also discussed. The use of data and analytics to personalize learning and improve student outcomes is highlighted. New partnerships and business models between educational institutions and other organizations are changing the traditional value chains in higher education.
Given its ability to analyze structured, unstructured, and "multi-structured" data, Hadoop is an increasingly viable option for analytics and business intelligence within the enterprise. Dramatically more scalable and cost-effective than traditional data warehousing technologies, Hadoop is also increasingly used to perform new kinds of analytics that were previously impossible. When it comes to Big Data, retailers are at the forefront of leveraging large volumes of nuanced information about customers, to improve the effectiveness of promotional campaigns, refine pricing models, and lower overall customer acquisition costs. Retailers compete fiercely for consumers' attention, time, and money, and effective use of analytics can result in sustained competitive advantage. Forward-thinking retailers can now take advantage of all data sources to construct a complete picture of a customer. This invariably consists of both structured data (customer and inventory records, spreadsheets, etc.) and unstructured data (clickstream logs, email archives, customer feedback and comment fields, etc.). This allows, for example, online retailers with structured, transactional sales data to connect that data with unstructured comments from product reviews, providing insight into how reviews affect consumers' propensity to purchase a particular product. This session will examine several real-world customer use cases applying combined analysis of structured and unstructured data.
Supporting Libraries in Leading the Way in Research Data ManagementMarieke Guy
Marieke Guy, Institutional Support Officer, Digital Curation Centre, UKOLN, University of Bath, UK presents on Supporting Libraries in Leading the Way in Research Data Management at Online Information, London 20th -21st November 2012
Goncol CoEs Service Presentation Short ENTabea Hirzel
The Göncöl Foundation is a non-profit environmental foundation in Hungary established in 1985 that works to preserve nature, human, and social values. It has over 15 years of experience in education, communication, and conservation projects. The foundation provides services such as competency development training, field research, and organizing conferences to support sustainable development. It partners with other organizations and the private sector to address environmental issues and support biodiversity conservation.
The document introduces COBWEB, a research project that develops a crowdsourcing infrastructure for collecting and analyzing environmental data provided by citizens. The project aims to address data quality issues and support policy decisions. It has several pilot sites and partners, including UNESCO biosphere reserves. The framework includes mobile apps, QA processes, and a portal to view and analyze citizen-submitted data. It uses open standards and aims to be customizable for different use cases involving topics like biological monitoring and flooding.
using Self-Supervised Learning Can Improve Model Robustness and uncertainty....ssuserbafbd0
Self-supervised learning using rotation prediction can improve model robustness and uncertainty. Models trained with this method showed improved robustness to common corruptions like noise, blur and weather effects. They also showed robustness to adversarial perturbations and label corruptions. These models were better able to detect out-of-distribution examples. Ablation studies demonstrated that self-attention helps networks learn shape and compare regions, aiding in out-of-distribution detection.
If Big Data is data that exceeds the processing capacity of conventional systems, thereby necessitating alternative processing measures, we are looking at an essentially technological challenge that IT managers are best equipped to address.
The DCC is currently working with 18 HEIs to support and develop their capabilities in the management of research data and, whilst the aforementioned challenge is not usually core to their expressed concerns, are there particular issues of curation inherent to Big Data that might force a different perspective?
We have some understanding of Big Data from our contacts in the Astronomy and High Energy Physics domains, and the scale and speed of development in Genomics data generation is well known, but the inability to provide sufficient processing capacity is not one of their more frequent complaints.
That’s not to say that Big Science and its Big Data are free of challenges in data curation; only that they are shared with their lesser cousins, where one might say that the real challenge is less one of size than diversity and complexity.
This brief presentation explores those aspects of data curation that go beyond the challenges of processing power but which may lend a broader perspective to the technology selection process.
CVPR2022 paper reading - Balanced multimodal learning - All Japan Computer Vi...Antonio Tejero de Pablos
Introduction of the CVPR2022 paper: Balanced multimodal learning via on-the-fly gradient modulation @ The All Japan Computer Vision Study Group (2022/08/07)
Webinar: How We Evaluated MongoDB as a Relational Database ReplacementMongoDB
This webinar will explain the process, methodology, and results used at Apollo Group to evaluate MongoDB and ultimately replace Oracle for a core platform component.
The document introduces COBWEB, a European Commission-funded project that develops a crowdsourcing infrastructure for collecting and analyzing environmental data. It summarizes the goals of the project, its partners which include UNESCO biosphere reserves, methods for co-designing use cases, and the development of quality assurance processes and mobile/web apps. Key components under development include workflows, services, sensor networks, and tools for customizing data collection and ensuring data quality.