Data pricing and data license agreements
Magdalena Balazinska, U. Washington (videoconference)
-Key note-
International conference on
“DATA, DIGITAL BUSINESS MODELS, CLOUD COMPUTING AND ORGANIZATIONAL DESIGN”
24-25 November 2014 ,
Université Paris–Sud
Advanced Analytics for Clinical Data Full Event GuidePfizer
This document provides information about the "Advanced Analytics for Clinical Data" conference to be held February 1-2, 2017 in San Francisco. The conference will focus on applying advanced analytics and data-driven methodologies in clinical research and drug development. It will feature presentations from experts in clinical data science from major pharmaceutical companies. Topics will include implementing analytics from areas like biostatistics, omics, and wearables. Attendees will learn how to extract value from large datasets and new sources of data. The agenda also includes panels on leveraging big data in clinical trials and evolving the role of clinical data management.
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk (presented at QConSF 2018), we first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.
Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons ...Krishnaram Kenthapadi
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS, Google’s RAPPOR, and LinkedIn Salary. We will also discuss various open source as well as commercial privacy tools, and conclude with open problems and challenges for data mining / machine learning community.
Oracle ACE Director Dan Morgan and PTC Chief Strategy Officer Mark Swanholm, presented this special webinar to discuss Big Data and the choices ahead for organizations. for more details about Performance Tuning Corporation, visit www.peftuning.com .
Organizations are being bombarded with messages telling you that you must make an investment in Big Data, that without it your organization will be rendered obsolete, a mere bystander, on the road to increased growth and profitability.
But do you? How exactly will your organization benefit from Big Data? When do you invest – and does investing in Big Data mean leaving the rest of your data strategy stranded?
Oracle ACE Director Dan Morgan, an internationally recognized expert in database technology and former University of Washington lecturer, and Mark Swanholm, PTC’s Chief Strategy Officer and 22 year IT Veteran, will address the issue of Big Data from the standpoint of what it is, where the value can be found, what is actually required to turn this new technology into something of value.
This Performance Tuning Corporation online event will focus on strategy, management, planning, and budgeting, and will provide you and your management team the information they need to plan make the best possible decision with respect to an investment in Big Data technology.
This document provides an overview of data mining including definitions, objectives, types of data, applications, the data mining process, architecture, patterns that can be discovered, usefulness, and issues. It defines data mining as applying methods to large databases to discover hidden patterns and discusses why it is used in business for applications like credit ratings, fraud detection, and customer relationship management.
This document provides information about the "Big Data & Analytics for Pharma Summit" event taking place on November 3-4, 2016 in Philadelphia. The event will focus on challenges in pharmaceutical R&D, drug development, and safety monitoring, and how analytics can help address these challenges in an evolving market focused on patient-centricity. Key themes include real-world data usage, marketing, business models, decision making, and drug research. The agenda includes keynote speakers from major pharmaceutical companies discussing various analytics applications and case studies.
The document discusses techniques for quantifying and validating data quality in a data warehouse. It describes objective and subjective approaches to assessing data quality and focuses on objective metrics and validation techniques. These include calculating ratios and using minimum-maximum values to measure dimensions like believability, appropriate data amount, timeliness and accessibility. Additional techniques covered are referential integrity checks, attribute domain validation, use of data quality rules, and statistical validation using histograms.
The document discusses data governance and quality challenges for publishers. It defines data governance and highlights common data quality issues like multiple data sources, inconsistent data entry, and challenges identifying individuals and institutions uniquely. The presentation recommends developing a data governance program that includes planning, auditing existing data, improving data capture processes, using identifiers, and ongoing monitoring to improve data quality over time. A publisher example is provided that leverages tools like Ringgold identifiers and data governance dashboards to clean data and monitor quality.
Advanced Analytics for Clinical Data Full Event GuidePfizer
This document provides information about the "Advanced Analytics for Clinical Data" conference to be held February 1-2, 2017 in San Francisco. The conference will focus on applying advanced analytics and data-driven methodologies in clinical research and drug development. It will feature presentations from experts in clinical data science from major pharmaceutical companies. Topics will include implementing analytics from areas like biostatistics, omics, and wearables. Attendees will learn how to extract value from large datasets and new sources of data. The agenda also includes panels on leveraging big data in clinical trials and evolving the role of clinical data management.
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk (presented at QConSF 2018), we first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.
Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons ...Krishnaram Kenthapadi
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS, Google’s RAPPOR, and LinkedIn Salary. We will also discuss various open source as well as commercial privacy tools, and conclude with open problems and challenges for data mining / machine learning community.
Oracle ACE Director Dan Morgan and PTC Chief Strategy Officer Mark Swanholm, presented this special webinar to discuss Big Data and the choices ahead for organizations. for more details about Performance Tuning Corporation, visit www.peftuning.com .
Organizations are being bombarded with messages telling you that you must make an investment in Big Data, that without it your organization will be rendered obsolete, a mere bystander, on the road to increased growth and profitability.
But do you? How exactly will your organization benefit from Big Data? When do you invest – and does investing in Big Data mean leaving the rest of your data strategy stranded?
Oracle ACE Director Dan Morgan, an internationally recognized expert in database technology and former University of Washington lecturer, and Mark Swanholm, PTC’s Chief Strategy Officer and 22 year IT Veteran, will address the issue of Big Data from the standpoint of what it is, where the value can be found, what is actually required to turn this new technology into something of value.
This Performance Tuning Corporation online event will focus on strategy, management, planning, and budgeting, and will provide you and your management team the information they need to plan make the best possible decision with respect to an investment in Big Data technology.
This document provides an overview of data mining including definitions, objectives, types of data, applications, the data mining process, architecture, patterns that can be discovered, usefulness, and issues. It defines data mining as applying methods to large databases to discover hidden patterns and discusses why it is used in business for applications like credit ratings, fraud detection, and customer relationship management.
This document provides information about the "Big Data & Analytics for Pharma Summit" event taking place on November 3-4, 2016 in Philadelphia. The event will focus on challenges in pharmaceutical R&D, drug development, and safety monitoring, and how analytics can help address these challenges in an evolving market focused on patient-centricity. Key themes include real-world data usage, marketing, business models, decision making, and drug research. The agenda includes keynote speakers from major pharmaceutical companies discussing various analytics applications and case studies.
The document discusses techniques for quantifying and validating data quality in a data warehouse. It describes objective and subjective approaches to assessing data quality and focuses on objective metrics and validation techniques. These include calculating ratios and using minimum-maximum values to measure dimensions like believability, appropriate data amount, timeliness and accessibility. Additional techniques covered are referential integrity checks, attribute domain validation, use of data quality rules, and statistical validation using histograms.
The document discusses data governance and quality challenges for publishers. It defines data governance and highlights common data quality issues like multiple data sources, inconsistent data entry, and challenges identifying individuals and institutions uniquely. The presentation recommends developing a data governance program that includes planning, auditing existing data, improving data capture processes, using identifiers, and ongoing monitoring to improve data quality over time. A publisher example is provided that leverages tools like Ringgold identifiers and data governance dashboards to clean data and monitor quality.
Rubbish in Rubbish out: applying good data governance techniques to gain maxi...Ringgold Inc
1) The document discusses the importance of data governance and quality for organizations. It defines data governance as processes, policies, standards, organization, and technologies required to manage data availability, accessibility, quality, consistency, auditability, and security.
2) Common challenges to data quality are multiple data silos, inconsistent data entry, and lack of unique identifiers. Poor data quality can lead to incorrect decision making and lost opportunities.
3) The presentation recommends developing a data governance program including cleaning existing data, improving data capture processes, using unique identifiers, and ongoing monitoring to improve an organization's data quality over time.
Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can...Anastasija Nikiforova
This presentations is a supplementary material for presenting the "Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can Save Your Business" (authored by Anastasija Nikiforova and Natalija Kozmina) research paper during the The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2021), November 15-16, 2021. Tartu, Estonia (web-based)
Read paper here -> Nikiforova, A., & Kozmina, N. (2021, November). Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can Save Your Business. In 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 66-73). IEEE -> https://ieeexplore.ieee.org/abstract/document/9660802?casa_token=LFJa20LrXAwAAAAA:wVwhTcCPWqxdloAvDQ3-l98KkkLx70xzG3zNvIIkJbC6wvJ4VxwX_VGc3mmW_7c1T-QJlOtTiao
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
This document summarizes a paper that proposes two optimization issues to identify data leakage by untrusted agents without disturbing trusted agents. The first issue selects data objects for monitoring that are accessed by at most c trusted agents while ensuring access to at least k monitored objects by each untrusted agent. The second issue selects monitored data objects to maximize the number accessed by untrusted agents while ensuring each trusted agent accesses no more than d monitored objects. The goal is to maximize detection of data misuse by untrusted agents while minimizing monitoring effort and avoiding disturbance to trusted agents.
Microsoft SQL Server always on solutions guide for high availability and dis...Компания Робот Икс
This document discusses how SQL Server AlwaysOn solutions in SQL Server 2012 can be used to provide high availability and disaster recovery capabilities. It describes the different layers of protection provided, including infrastructure availability with Windows Server Failover Clustering, SQL Server instance level protection with AlwaysOn Failover Cluster Instances, and database availability with AlwaysOn Availability Groups. The paper also touches on concepts like planned vs unplanned downtime, recovery time objectives, and recovery point objectives that are important considerations for high availability and disaster recovery planning.
Detecting health insurance fraud using analytics Nitin Verma
Healthcare fraud, abuse and waste costs the industry nearly $80 billion per year. Predictive analytics and data mining techniques can help payers more closely monitor for fraudulent billing activities. These techniques analyze relationships within large amounts of data to predict and detect fraudulent claims, saving millions of dollars traditionally lost to healthcare fraud.
Enabling Better Clinical Operations through a Clinical Operations StoreSaama
Srini Anandakumar, Senior Director of Clinical Analytics Innovation for Saama, presented at the Big Data and Analytics in Pharma in Philadelphia, November 1, 2017.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
This document discusses challenges and opportunities in the pharmaceutical industry. It notes that R&D costs are high due to extensive outsourcing, and benchmarking outsourced R&D units could lower costs by 40%. It also describes a software tool called PAT that would help predict side effects and check patent uniqueness of new drug formulas, saving 40% of time wasted on failed experiments. Overall the document analyzes ways to improve innovation and lower costs in pharmaceutical R&D through analytical solutions.
TrialIO aims to empower patients and researchers by providing better access and analysis of clinical trial data. It reimagines the data from ClinicalTrials.gov as a spreadsheet in the cloud, allowing users to more easily identify trends in clinical trial activity over time for specific diseases, locations, investigators, and sponsors. This could help improve patient-researcher matching and support various stakeholders in planning and conducting clinical trials. The tool is envisioned as both a web application and syndicated web service to promote wider dissemination and use of clinical trial data.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
ShoBeVODSDT: Shodan and Binary Edge based vulnerable open data sources detect...Anastasija Nikiforova
This presentation is devoted to the "ShoBeVODSDT: Shodan and Binary Edge based vulnerable open data sources detection tool or what Internet of Things Search Engines know about you" research paper developed by Artjoms Daskevics and Anastasija Nikiforova and presented during the The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2021), November 15-16, 2021. Tartu, Estonia (web-based).
Read paper here -> Daskevics, A., & Nikiforova, A. (2021, November). ShoBeVODSDT: Shodan and Binary Edge based vulnerable open data sources detection tool or what Internet of Things Search Engines know about you. In 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 38-45). IEEE.
Building a Next Generation Clinical and Scientific Data Management SolutionSaama
This document describes a next-generation clinical/scientific data management solution presented by Saama Technologies. It discusses the components and benefits of building a patient data analytics solution, including reducing clinical trial costs and timelines through improved data acquisition, standardization, and analytics. The solution aims to address current challenges around clinical data management by providing a modern patient data platform with features like a patient data lake, metadata management, and machine learning capabilities.
Data Science and its relationship to Big Data and data-driven decision makingDr. Volkan OBAN
Data Science and its relationship to Big Data and data-driven
decision making
F. Provost1
Leonard N. Stern School of Business
New York University
44 W. 4th St. New York, NY, USA
fprovost@stern.nyu.edu
T. Fawcett
Data Scientists, LLC
tfawcett@acm.org
Muhammad Arif is applying for an electrical position. He has over 5 years of experience working as an electrician in power plants, oil and gas facilities in the UAE and Qatar. He has a diploma in electrical engineering and technical qualifications including Microsoft Office skills. His experience includes maintaining electrical equipment, troubleshooting issues, installing wiring, and ensuring safety standards are followed. He is looking for a role where he can continue growing his knowledge and skills.
The document outlines the candidate's credentials which include strong experience in retail, POS, merchandising, sales promotion, PR, events and training. They have worked across Europe on management projects and have experience working with marketing teams. The candidate also has strong project and team management skills, budget management experience, and is commercially astute in understanding ROI.
What is the best platform for managing social media activity ?Ludovic Martin
Hootsuite, Buffer, Sendible, Rignite, Agorapulse... You still don't know which solution to use for managing your social media activity... or the one of your customers ? I've benchmarked various solutions in this comprehensive slideshow. Hope it'll help you find the right solution depending on your requirements !
Rubbish in Rubbish out: applying good data governance techniques to gain maxi...Ringgold Inc
1) The document discusses the importance of data governance and quality for organizations. It defines data governance as processes, policies, standards, organization, and technologies required to manage data availability, accessibility, quality, consistency, auditability, and security.
2) Common challenges to data quality are multiple data silos, inconsistent data entry, and lack of unique identifiers. Poor data quality can lead to incorrect decision making and lost opportunities.
3) The presentation recommends developing a data governance program including cleaning existing data, improving data capture processes, using unique identifiers, and ongoing monitoring to improve an organization's data quality over time.
Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can...Anastasija Nikiforova
This presentations is a supplementary material for presenting the "Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can Save Your Business" (authored by Anastasija Nikiforova and Natalija Kozmina) research paper during the The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2021), November 15-16, 2021. Tartu, Estonia (web-based)
Read paper here -> Nikiforova, A., & Kozmina, N. (2021, November). Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can Save Your Business. In 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 66-73). IEEE -> https://ieeexplore.ieee.org/abstract/document/9660802?casa_token=LFJa20LrXAwAAAAA:wVwhTcCPWqxdloAvDQ3-l98KkkLx70xzG3zNvIIkJbC6wvJ4VxwX_VGc3mmW_7c1T-QJlOtTiao
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
This document summarizes a paper that proposes two optimization issues to identify data leakage by untrusted agents without disturbing trusted agents. The first issue selects data objects for monitoring that are accessed by at most c trusted agents while ensuring access to at least k monitored objects by each untrusted agent. The second issue selects monitored data objects to maximize the number accessed by untrusted agents while ensuring each trusted agent accesses no more than d monitored objects. The goal is to maximize detection of data misuse by untrusted agents while minimizing monitoring effort and avoiding disturbance to trusted agents.
Microsoft SQL Server always on solutions guide for high availability and dis...Компания Робот Икс
This document discusses how SQL Server AlwaysOn solutions in SQL Server 2012 can be used to provide high availability and disaster recovery capabilities. It describes the different layers of protection provided, including infrastructure availability with Windows Server Failover Clustering, SQL Server instance level protection with AlwaysOn Failover Cluster Instances, and database availability with AlwaysOn Availability Groups. The paper also touches on concepts like planned vs unplanned downtime, recovery time objectives, and recovery point objectives that are important considerations for high availability and disaster recovery planning.
Detecting health insurance fraud using analytics Nitin Verma
Healthcare fraud, abuse and waste costs the industry nearly $80 billion per year. Predictive analytics and data mining techniques can help payers more closely monitor for fraudulent billing activities. These techniques analyze relationships within large amounts of data to predict and detect fraudulent claims, saving millions of dollars traditionally lost to healthcare fraud.
Enabling Better Clinical Operations through a Clinical Operations StoreSaama
Srini Anandakumar, Senior Director of Clinical Analytics Innovation for Saama, presented at the Big Data and Analytics in Pharma in Philadelphia, November 1, 2017.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
This document discusses challenges and opportunities in the pharmaceutical industry. It notes that R&D costs are high due to extensive outsourcing, and benchmarking outsourced R&D units could lower costs by 40%. It also describes a software tool called PAT that would help predict side effects and check patent uniqueness of new drug formulas, saving 40% of time wasted on failed experiments. Overall the document analyzes ways to improve innovation and lower costs in pharmaceutical R&D through analytical solutions.
TrialIO aims to empower patients and researchers by providing better access and analysis of clinical trial data. It reimagines the data from ClinicalTrials.gov as a spreadsheet in the cloud, allowing users to more easily identify trends in clinical trial activity over time for specific diseases, locations, investigators, and sponsors. This could help improve patient-researcher matching and support various stakeholders in planning and conducting clinical trials. The tool is envisioned as both a web application and syndicated web service to promote wider dissemination and use of clinical trial data.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
ShoBeVODSDT: Shodan and Binary Edge based vulnerable open data sources detect...Anastasija Nikiforova
This presentation is devoted to the "ShoBeVODSDT: Shodan and Binary Edge based vulnerable open data sources detection tool or what Internet of Things Search Engines know about you" research paper developed by Artjoms Daskevics and Anastasija Nikiforova and presented during the The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2021), November 15-16, 2021. Tartu, Estonia (web-based).
Read paper here -> Daskevics, A., & Nikiforova, A. (2021, November). ShoBeVODSDT: Shodan and Binary Edge based vulnerable open data sources detection tool or what Internet of Things Search Engines know about you. In 2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 38-45). IEEE.
Building a Next Generation Clinical and Scientific Data Management SolutionSaama
This document describes a next-generation clinical/scientific data management solution presented by Saama Technologies. It discusses the components and benefits of building a patient data analytics solution, including reducing clinical trial costs and timelines through improved data acquisition, standardization, and analytics. The solution aims to address current challenges around clinical data management by providing a modern patient data platform with features like a patient data lake, metadata management, and machine learning capabilities.
Data Science and its relationship to Big Data and data-driven decision makingDr. Volkan OBAN
Data Science and its relationship to Big Data and data-driven
decision making
F. Provost1
Leonard N. Stern School of Business
New York University
44 W. 4th St. New York, NY, USA
fprovost@stern.nyu.edu
T. Fawcett
Data Scientists, LLC
tfawcett@acm.org
Muhammad Arif is applying for an electrical position. He has over 5 years of experience working as an electrician in power plants, oil and gas facilities in the UAE and Qatar. He has a diploma in electrical engineering and technical qualifications including Microsoft Office skills. His experience includes maintaining electrical equipment, troubleshooting issues, installing wiring, and ensuring safety standards are followed. He is looking for a role where he can continue growing his knowledge and skills.
The document outlines the candidate's credentials which include strong experience in retail, POS, merchandising, sales promotion, PR, events and training. They have worked across Europe on management projects and have experience working with marketing teams. The candidate also has strong project and team management skills, budget management experience, and is commercially astute in understanding ROI.
What is the best platform for managing social media activity ?Ludovic Martin
Hootsuite, Buffer, Sendible, Rignite, Agorapulse... You still don't know which solution to use for managing your social media activity... or the one of your customers ? I've benchmarked various solutions in this comprehensive slideshow. Hope it'll help you find the right solution depending on your requirements !
This document profiles Sergio Zamorano and his engineering company ZING e.i.r.l., which specializes in bulk materials handling systems including overland conveying, pipe conveyors, stacking and reclaiming, and ship/train loading and unloading. It provides an overview of Zamorano's 28 years of experience in engineering projects across North and South America, Asia, Europe, and Africa. It also lists several specific projects executed by ZING e.i.r.l. related to conveying systems for materials like coal, copper ore, and grains in countries such as Chile, China, South Africa, and Brazil.
2016 Landscape Architecture Portfolio: Zachary B.L. ReesZachary B. Rees
This document is Zachary Rees' 2016 landscape architecture portfolio, which includes information about his education and experience. It summarizes projects he worked on, including a sculpture garden planting design, an urban design project in Cincinnati, and an extracurricular community garden park design. Contact information is provided at the top for Zachary Rees.
The presentation discusses how cognitive sciences and next generation clinical data management can transform clinical trials. It notes that currently, 72% of studies are one month behind schedule, 70% experience patient enrollment delays, and 20% do not recruit any subjects. It advocates centralizing and contextualizing data in a clinical data lake to enable evidence generation and reduce time and costs. The presentation outlines Saama Technologies' clinical data-as-a-service solution which uses metadata-driven transformation, analytics applications, and data pipelines to generate insights from varied data sources in real time. It argues that disruptive thinking is now required to achieve clean, longitudinal data and operational efficiencies through cognitive systems and a patient-centric, "Silicon Valley" mindset
There are no systems that are connected to the Internet that are completely safe. Cyber-attacks are the norm. Everyone with a web presence is attacked multiple times each week. To further complicate this scenario, government entities have been found to be weakening Web security protocols and compromising business systems in the interest of national security, and hyper-competitive companies have been caught engaging in cyber-espionage. Detection of these attacks in real-time is difficult due to a number of reasons. The primary ones being the dynamism and ingenuity of the attacker and the nature of contemporary real-time attack detection systems. In this talk, I will share insights on an alternative, i.e. quickly recognizing attacks in a short period of time after the incident using audit analysis.
This document summarizes IBM's business analytics and big data strategy and capabilities. It discusses how analytics are important for businesses to gain insights and competitive advantages. It outlines IBM's investments in analytics through acquisitions, expertise, technology, and partners. It describes IBM Smarter Analytics as an approach to turn information into insights and outcomes. Key capabilities discussed include business intelligence, predictive analytics, and big data platforms and solutions.
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Data mining involves discovering patterns from large amounts of data. It can be used for applications like credit ratings, targeted marketing, fraud detection, and customer relationship management. Some common data mining techniques include classification, clustering, regression, and association rule mining. Decision trees are a popular classification technique that uses a tree structure with internal nodes representing attributes and leaf nodes representing target classes.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
This document describes a platform called Iyka dataSpryng that provides comprehensive analytics capabilities. It removes the need for complex and siloed analytic processes by allowing direct access and analysis of disparate data sources. Key features include a unified view of all data, knowledge portability to leverage ontologies and dictionaries, and self-service analytics. This empowers users and provides 2x more productivity and faster results compared to traditional analytic methods.
The data services marketplace is enabled by a data abstraction layer that supports rapid development of operational applications and single data view portals. In this presentation yo will learn services-based reference architecture, modality, and latency of data access.
- Reference architecture for enterprise data services marketplace
- Modality and latency of data access
- Customer use cases and demo
This presentation is part of the Denodo Educational Seminar , and you can watch the video here goo.gl/vycYmZ.
Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execu...Saama
Nikhil Gopinath, Senior Solutions Engineer for the Life Sciences at Saama, spoke at EyeforPharma's Clinical Trial Innovation Summit event in February 2017. These slides are from his "Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execution" presentation.
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
The document discusses how companies can drive business agility through cloud-based big data analytics. It notes that traditional big data approaches are no longer sufficient due to the increasing volume, variety, and velocity of data. The document outlines a reference architecture for analyzing diverse data sources in real-time and iteratively to gain insights. It emphasizes the need for data products to be responsive to disruption, leverage external data sources, and be resilient through cloud elasticity. Examples from Ford and healthcare are provided to illustrate integrating diverse data for predictive analytics and personalized recommendations.
How MongoDB is Transforming Healthcare TechnologyMongoDB
Healthcare providers continue to feel increased margin pressure, due to both macro-economic factors as well as significant regulatory change. In response to these pressures, leading healthcare organizations are leveraging new technologies to increase quality of care while simultaneously reducing costs.
In this session, we'll cover:
- How MongoDB has enabled successful real world projects with EHR / EMR in the healthcare industry
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- The challenges with healthcare data collection and how MongoDB handles various data types, HIPAA/PII and hybrid deployments
The document discusses the importance of data for non-profits and provides tips on finding, analyzing, and using different types of data. It explains how to create simple databases and use data for needs assessments, funding applications, evaluations, and determining community priorities. External sources of data are also identified like government agencies and surveys that can help non-profits with their work.
This document provides an introduction to big data and big data analytics. It outlines 11 modules for a course on big data, including introductions to Hadoop, NoSQL, MapReduce, data mining, clustering, recommendation systems, and mining social networks. The document discusses characteristics of big data like volume, variety, velocity, veracity, and value. It also covers traditional vs big data approaches, what is driving big data, issues in big data, benefits of big data processing, and what can be done with big data. Examples of big data solutions and a word count case study are provided. The skills required of data scientists are outlined.
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Big Data Forum at Salt River Fields (the spring training field for the Arizona Diamondbacks). Krishnan Parasuraman discusses how companies are using big data and analytics to transform their business.
Data Mining Xuequn Shang NorthWestern Polytechnical Universitybutest
This document provides information about a data mining course, including the course schedule, evaluation criteria, and topics to be covered. The course will take place on Tuesday and Friday evenings from 7-9pm, and will be taught by Xuequn Shang. Topics include association analysis, sequential pattern mining, classification, clustering, and data preprocessing. Students will be evaluated based on assignments, class participation, a project, and a final exam.
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
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Magdalena Balazinska: Data pricing and data license agreements
1. Magdalena Balazinska
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
ESCIENCE INSTITUTE
UNIVERSITY OF WASHINGTON
http://www.cs.washington.edu/people/faculty/magda
Data Pricing and Data License
Agreements in the Cloud
1
2. Acknowledgments
Research team on project
• Prasang Upadhyaya (UW, lead on licensing)
• Paraschos Koutris (UW, lead on pricing)
• Prof. Dan Suciu (UW)
• Dr. Hakan Hacigumus (NEC Labs)
Sponsors
• National Science Foundation
• NEC
• Microsoft Research
Magdalena Balazinska - University of Washington 2
3. Data Has Value
• First wave of computing: value in hardware
– IBM, Intel, DEC
• Second wave of computing: value in software
– Microsoft, Oracle, Google
• Third wave of computing: value in data
– Dun & Bradstreet, Factual, Facebook, Google
Magdalena Balazinska - University of Washington 3
4. Data itself is now a product that is being
created, improved, bought and sold on the Web
Magdalena Balazinska - University of Washington 4
5. What are the Technical Challenges
• Challenge 1: Data License Agreements
– All data comes with terms of use
– Can we automate their enforcement?
• Challenge 2: Data Pricing
– Existing pricing methods are limited
– Can we support flexible pricing?
Magdalena Balazinska - University of Washington 5
6. Data Comes with Terms of Use
Overlaying map data
with any other data is
prohibited (Navteq)
Each book may be lent
once for 2 weeks while
being inaccessible by
the lender (Kindle)
Name Ailment Birth
date
Se
x
Locati
on
John Doe Asthma Jan 7th 1972 M Seattle
Mary
Jane
Dislocated
shoulder
Mar 21st
1965
F San Diego
Alice
Summer
Flu May 28th
1986
M San
Francisco
… … … … …
Bob B Flu Oct 14th
2000
M Miami
Medical Data Maps Digital Books
Queries that try to identify
an individual referenced in
the database are
prohibited (MIMIC II)
6
7. More Examples
Terms of use Source
Overlaying Navteq data with any other data is prohibited Navteq
Each book may be lent once for a duration of 14 days and will not be
readable by the lender during the loan period
Amazon Kindle
In a month, all queries may, in total, return up to 2M characters of
data at the free tier
Microsoft Translator
OAuth calls are permitted 350 requests per hour Twitter and
Foursquare
Queries that try to identify an individual referenced in the database
are prohibited
MIMIC II
You are required to display all attribution information and any
proprietary notices associated with the Foursquare Data
Foursquare, Yelp,
World Bank
Don’t aggregate or blend our star ratings and review counts with other
providers. You may show content from multiple providers, but Yelp
data should stand on its own …
Yelp
7
8. Fine-grained Access
Look up specific patient
Augmenting Data Sources
Join medical data with
voters registry
Terms of Use Control Use, Not Access
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Histogram
0
20000
40000
60000
80000
100000
120000
140000
0 5 10 15 20 25
Linear Regression
Data
Permitted
Denied
Goal
Enforce policies
to constrain
how data is used
8
Example for medical research data
14. Approach Overview
• Data seller defines policies
• Data and policies are loaded into a DataLawyer-
enabled database system
• Buyer queries the data
• DataLawyer checks all queries before execution
Magdalena Balazinska - University of Washington 14
15. Challenge: Semantics
Example Policy 1: Can access up to
10K records/month.
If the buyer computes a histogram
on the data and filters out some
buckets, did he use the input tuples
from the filtered bucket or not?
15
16. Challenge: Performance
Example Policy 2: Only allow
aggregate queries where each
output tuple must aggregate over
at least 10 values.
Policies are expensive to check online!
Cheap
Expensive
16
17. DataLawyer Setup
17
Data
Metadata on
Usage
Policies
Features of
user and query
behavior
Data Usage
Arbitrary code
Shared across
multiple policies
SELECT DISTINCT ‘P5 violated:
Fewer than 10 patients contribute
to an answer’ AS errorMessage
FROM Provenance p
WHERE p.irid = ‘patients’
GROUP BY p.qid, p.otid
HAVING COUNT(DISTINCT p.itid) < 10
SELECT DISTINCT ‘P5 violated:
Fewer than 10 patients contribute
to an answer’ AS errorMessage
FROM Provenance p
WHERE p.irid = ‘patients’
GROUP BY p.qid, p.otid
HAVING COUNT(DISTINCT p.itid) < 10
SELECT DISTINCT ‘P5 violated:
Fewer than 10 patients contribute
to an answer’ AS errorMessage
FROM Provenance p
WHERE p.irid = ‘patients’
GROUP BY p.qid, p.otid
HAVING COUNT(DISTINCT p.itid) < 10
SELECT DISTINCT ‘P5 violated:
Fewer than 10 patients contribute
to an answer’ AS errorMessage
FROM Provenance p
WHERE p.irid = ‘patients’
GROUP BY p.qid, p.otid
HAVING COUNT(DISTINCT p.itid) < 10
Declarative policies
(DataLawyer uses SQL)
18. Usage Log
18
They capture features of a query that are used in the policies
We require them to:
1. Be deterministic
2. Be append only
3. Contain a timestamp with each tuple
Examples are:
1. Provenance
2. User log
3. Static analysis of the query
4. Pricing
5. …
20. DataLawyer Workflow Example
Magdalena Balazinska - University of Washington 20
pid disease treatment outcome
1 asthma albuterol positive
…
Data: Patients
Query: What fraction of asthma patients were treated with albuterol?
DataLawyer: Populates the usage logs
uid query table column
1 1 Patient treatment
1 1 Patient outcome
DataLawyer checks policies, which are queries over the usage logs
• Queries are not allowed to access column pid
• Queries must aggregate data from at least 10 rows in Patients
21. Example Using SQL
21
Policy: Stop queries where fewer than 10 patients contribute to any output tuple.
SELECT DISTINCT ‘P5 violated: Fewer than 10 patients contribute to an answer’
AS errorMessage
FROM Provenance p
WHERE p.irid = ‘patients’
GROUP BY p.qid, p.otid
HAVING COUNT(DISTINCT p.itid) < 10
Usage log that captures how each tuple in query result
was derived from records on disk
Provenance(ts, // Timestamp
qid, // Query id
otid, // Output tuple id, a hash of the output tuple
irid, // Input relation id, usually the name
itid // Input tuple id, usually the name
)
If false, no violation
If true, at least
one example of a violation
Policy refers to the provenance usage log
22. Need for Optimizations
22
0
50
100
150
200
250
300
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Averagetimeforthebatch(inms)
Batch number (Each batch consists of 120 queries)
Baseline, Hard
DataLawyer, Hard
Baseline, Easy
DataLawyer, Easy
Without optimizations,
time to process queries &
check policies grows
Time grows even for simple policies
DataLawyer keeps time constant
DataLawyer keeps time low
23. Policy Evaluation
• There are three major steps:
– Generate the usage logs
– Evaluate policies
– Write log to disk if everything is okay, else abort
• Our optimizations:
– Avoid generating the logs
– Prune the logs by removing data no longer needed
– Avoid evaluating all policies
– Try to evaluate cheaper, partial policies first
23
25. Data License Agreements Summary
• Data comes with terms of use
– Even free data often has terms of use
• Today, terms of use are written in natural language
– Compliance for buyers is tedious and error-prone
• Possible to automate the process: DataLawyer
– Enables more precise terms of use specification
– Enables efficient enforcement
• Open problems
– Malicious users
– Data leaving database system
Magdalena Balazinska - University of Washington 25
26. What are the Technical Challenges
• Challenge 1: Data License Agreements
– All data comes with terms of use
– Can we automate their enforcement?
• Challenge 2: Data Pricing
– Existing pricing methods are limited
– Can we support flexible pricing?
Magdalena Balazinska - University of Washington 26
27. Data Pricing Today: Fixed
Magdalena Balazinska - University of Washington 27
Not flexible!
28. Data Pricing Today: Subscriptions
Magdalena Balazinska - University of Washington 28
Not flexible!
29. Data Pricing Today: Private Price
Magdalena Balazinska - University of Washington 29
Not scalable!
30. Example Scenario
• Seller has a database of cities and business contact information
– Businesses in one province or state: $300
– One type of business: $150
– Cities with given climate: $10
• Buyer:
– Q1: “Businesses with more than 200 employees” (selection)
– Q2: “West-coast businesses in cities with high yearly
precipitation” (join)
• How to satisfy buyer?
Magdalena Balazinska - University of Washington 30
31. Current Pricing: Fixed Prices
• Fixed price for entire dataset
• Must create and price views specific to queries Q1 and Q2
• OR user must buy entire dataset if view not available
• AND user must perform joins by herself
• Certainly the case if datasets have different owners
31Magdalena Balazinska - University of Washington
32. Current Pricing: Subscriptions
• Subscriptions
– Fixed number of transactions per month
– Must create and price appropriate parameterized queries
– Today queries are dataset specific (i.e., no joins!)
– Can satisfy Q1: “Businesses with more than 200 employees”
– Cannot Q2: “West-coast businesses in cities with high yearly
precipitation”
32Magdalena Balazinska - University of Washington
33. Other Data Pricing Issues
• Today’s data pricing can also have bad properties
• Example: Weather Imagery on Azure DataMarket
– 1,000,000 transactions -> $2,400
– 100,000 -> $600
– 10,000 -> $120
– 2,500 -> $0
• Arbitrage opportunity:
– Emulate many users
– Get as much data as you want for free!
33Magdalena Balazinska - University of Washington
35. Query-Based Pricing
• Seller specifies a set of queries Q1, … Qn
• These queries form views on the data for sell
• Seller prices the views: price(Q1), …, price(Qn)
– D = all cities and businesses in North America
– V1 (businesses in one state) = $300
– V2 (businesses of one type) = $150
– V3 (cities with a given climate) = $10
35Magdalena Balazinska - University of Washington
36. Query-Based Pricing
• QueryMarket system computes other query prices
– Q2: “West-coast businesses in cities with high yearly
precipitation”
– Key idea: Compute least expensive set of views that can
be used to answer the query. The sum of the price of
these views is the price of the query
• System guarantees price properties
– Arbitrage-free prices
– Maximal prices (no unintended discounts)
36Magdalena Balazinska - University of Washington
37. Conclusion
• Data has value
• Data is bought and sold online
• Supporting modern data markets requires
– New tools for managing license agreements
– New methods for pricing data
• Much work remains to be done
http://cloud-data-pricing.cs.washington.edu
Magdalena Balazinska - University of Washington 37
39. Potential Techniques
Privacy
Mechanisms
(Reduces data utility)
Intrusion
Detection System
(Assumes the user is
malicious. Offline)
Access Control
(Want full access to
data)
Auditing Systems
(Offline)
Online Offline
Fuzzy
Semantics
Precise
Semantics
None of these work!
39
Editor's Notes
Once hardware become commoditized, the value moved to the software…
Dun & Bradstreet: To be the most trusted source of commercial insight so our customers can decide with confidence. With over 225 million records on businesses from around the world, we have more data and insight about businesses than any other information company on the planet. Founded 1841.
How they sell their data: It always says: “To get started, contact us”.
Factual: Business info, service such as translating lat/long into a neighborhood, data integration APIs (such as mapping location on different services such as Yelp, Foursquare, Facebook, and Twitter.
Companies that own data, aggregate & curate data, sell data-related services such as cleaning, integration, and analysis.
Microsoft Azure Marketplace: online market for buying and selling finished Software as a Service (SaaS) applications and premium datasets.
131 free datasets. 95 paid datasets (26 have free trial). Prices are posted. Subscription based.
Factual: Focus is on supporting mobile apps. Business info, service such as translating lat/long into a neighborhood, data integration APIs (such as mapping location on different services such as Yelp, Foursquare, Facebook, and Twitter. Private pricing (just like Dun & Bradstreet).
AggData: Business contact information. Flexible pricing full datasets, subscriptions to get updates, or personalized data product.
Gnip: Provider of social data (real time and historical). For example, real-time “firehose” data. We get every activity from social networks like Twitter, Tumblr, and WordPress, add in our own enrichments and then pass the data on to you, all in the blink of an eye. Also sell subsets of data based on user-specified selection predicates. Private pricing.
Patientslikeme: See 25 million data points about disease. We take the information patients like you share about your experience with the disease and sell it to our partners.
Xignite: Sell financial market data. Private pricing appears to be subscription based (hits).
The MIMIC-II research database (Multiparameter Intelligent Monitoring in Intensive Care) is notable for three factors: it is publicly and freely available; it encompasses a diverse and very large population of ICU patients; and it contains high temporal resolution data including lab results, electronic documentation, and bedside monitor trends and waveforms. The database can support a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development.
What medical researchers actually do is this. You email them saying you want the data. They send you the link to this three hour long course. If you are like me, and use common sense, you flunk it and spend an additional hour retaiking it. Now, all the course does is to tell you what you can’t do with the data which is, you can’t share it with anyone else and you shouldn’t try to infer the identity of patients.
Here are more examples. The matter of interest to us is that these license agreements were over 8 pages of text and impose significant cognitive burden on the users of these datasets.
You can do live processing on twitter through Twitter Stream API. But stream api doesn't send you all tweets, you have to specify either keywords to filter tweets containing given keywords or userIds to follow tweets coming from given users or geo location to filter tweets coming from a particular place. Also there are limits on number of keywords, user ids to follow etc. And lastly twitter will give you upmost 1% percent of the tweets in the global traffic. So for example if you come up with a filter which tracks less than 1% of the global trafic you will get all the tweets (in theory) otherwise you will miss some tweet as twitter won't share them with you.
We performed a survey of 13 data providers (Foursquare [8], Yelp [20], Azure Marketplace [17], Twitter [14], In-
fochimps [9], Socrata [15], Xignite [19], Digital Folio [6], DataSift [5], World Bank [18], Navteq [12], data.gov.uk [13], and DataMarket [4].
I will give the overall idea. Please find me during the poster session to discuss the algorithms in depth.
To check policies given a user query, we first run the various feature extractors, populate them into a log, that we call the usage log, evaluate all the policies and check that all of them are empty. If so, we return the query answers, if not, we give an error message for the policy that failed.
We have a bunch of optimizations that try to evaluate policies without evaluating all of the features, since they are the primary overheads.
To check policies given a user query, we first run the various feature extractors, populate them into a log, that we call the usage log, evaluate all the policies and check that all of them are empty. If so, we return the query answers, if not, we give an error message for the policy that failed.
We have a bunch of optimizations that try to evaluate policies without evaluating all of the features, since they are the primary overheads.
We found that this abstraction is powerful enough to capture a vast number of usage terms we found in real life.
Our prototype extracts three features,
user information such as user-id and query time.
schema information about the input relations of the query and its output columns.
where-provenance of tuples.
Given just these three tables, it is possible to write very sophisticated policy checking queries.
Note that it is also easy to extend the system to newer domains, say query prices in data marketplaces.
No additional SQL constructs, and extensible.
Scaleup
The seller needs to carefully design the views to
sell so as to cater to as many buyers as she can, without
knowing the exact queries from the buyers. Similarly, the
buyer needs to carefully evaluate the various views that are
available for sale to determine which views suce to answer
her query and are also the cheapest way to answer that
query
Currently supports only selection queries on a single attribute of a relation
Currently, non-trivial subset of full conjunctive queries without self-joins
[8] called the Generalized Chain Queries" (Section 5), a set that includes all
path join queries, star join queries, and their combination.
an algorithm with polynomial time data complexity for computing
the price of any generalized chain query, by reducing the
problem to network flow, (2) a complete characterization of
the class of Conjunctive Queries without self-joins that can
be priced with PTIME data complexity (this class is slightly
larger than generalize chain queries), and (3) a proof that
pricing all other queries is NP-complete, thus establishing a
dichotomy on the complexity of the pricing problem when
all views are selection queries. For the queries that are not
in PTIME, we reduce the problem to an Integer Linear Pro-
gram and compute the prices.
IDS: The user is not adversarial, but a legal user.
Highlight about why each doesn’t work.
Privacy: We discussed.
Access control: Do not want it.
Auditing systems: After the fact.
Intrusion detection system: Very different aims. Aims to find malicious users whose actions are deliberate. Our users aren’t malicious and offenses.
Access control
Offline audits
Hippocratic databases
----- Meeting Notes (1/4/13 16:47) -----
Instrsion detection argument was bad.