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This report describes the design rationale and resulting performance seen in a single server (non-clustered) database with an extremely large table containing in excess of 11 billion records. The original research and testing had been performed on a much smaller scale into the tens of millions of records around fall 2001, with an expectation that it would be near to the projected maximum number of records. In summer 2003, after those initial results were used to design a table still in use in production today, a similar table with a much larger data set was needed and the basic research was applied again to the new table. Initially, it was expected the number of records would peak close to half a billion; however, due to continuous and unexpected growth, the table now contains more than 11 billion records and is expected to grow more as the storage capacity is increased on the database server.

This report describes the design rationale and resulting performance seen in a single server (non-clustered) database with an extremely large table containing in excess of 11 billion records. The original research and testing had been performed on a much smaller scale into the tens of millions of records around fall 2001, with an expectation that it would be near to the projected maximum number of records. In summer 2003, after those initial results were used to design a table still in use in production today, a similar table with a much larger data set was needed and the basic research was applied again to the new table. Initially, it was expected the number of records would peak close to half a billion; however, due to continuous and unexpected growth, the table now contains more than 11 billion records and is expected to grow more as the storage capacity is increased on the database server.

This report describes the design rationale and resulting performance seen in a single server (non-clustered) database with an extremely large table containing in excess of 11 billion records. The original research and testing had been performed on a much smaller scale into the tens of millions of records around fall 2001, with an expectation that it would be near to the projected maximum number of records. In summer 2003, after those initial results were used to design a table still in use in production today, a similar table with a much larger data set was needed and the basic research was applied again to the new table. Initially, it was expected the number of records would peak close to half a billion; however, due to continuous and unexpected growth, the table now contains more than 11 billion records and is expected to grow more as the storage capacity is increased on the database server.

This report describes the design rationale and resulting performance seen in a single server (non-clustered) database with an extremely large table containing in excess of 11 billion records. The original research and testing had been performed on a much smaller scale into the tens of millions of records around fall 2001, with an expectation that it would be near to the projected maximum number of records. In summer 2003, after those initial results were used to design a table still in use in production today, a similar table with a much larger data set was needed and the basic research was applied again to the new table. Initially, it was expected the number of records would peak close to half a billion; however, due to continuous and unexpected growth, the table now contains more than 11 billion records and is expected to grow more as the storage capacity is increased on the database server.

We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator. In literature, oracle inequalities have been derived under restricted eigenvalue or compatibility conditions. In this paper, we complement this with entropy conditions which allow one to improve the dual norm bound, and demonstrate how this leads to new oracle inequalities. The new oracle inequalities show that a smaller choice for the tuning parameter and a trade-off between $\ell_1$-norms and small compatibility constants are possible. This implies, in particular for correlated design, improved bounds for the prediction error of the Lasso estimator as compared to the methods based on restricted eigenvalue or compatibility conditions only.

We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator. In literature, oracle inequalities have been derived under restricted eigenvalue or compatibility conditions. In this paper, we complement this with entropy conditions which allow one to improve the dual norm bound, and demonstrate how this leads to new oracle inequalities. The new oracle inequalities show that a smaller choice for the tuning parameter and a trade-off between $\ell_1$-norms and small compatibility constants are possible. This implies, in particular for correlated design, improved bounds for the prediction error of the Lasso estimator as compared to the methods based on restricted eigenvalue or compatibility conditions only.

We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator. In literature, oracle inequalities have been derived under restricted eigenvalue or compatibility conditions. In this paper, we complement this with entropy conditions which allow one to improve the dual norm bound, and demonstrate how this leads to new oracle inequalities. The new oracle inequalities show that a smaller choice for the tuning parameter and a trade-off between $\ell_1$-norms and small compatibility constants are possible. This implies, in particular for correlated design, improved bounds for the prediction error of the Lasso estimator as compared to the methods based on restricted eigenvalue or compatibility conditions only.

We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator. In literature, oracle inequalities have been derived under restricted eigenvalue or compatibility conditions. In this paper, we complement this with entropy conditions which allow one to improve the dual norm bound, and demonstrate how this leads to new oracle inequalities. The new oracle inequalities show that a smaller choice for the tuning parameter and a trade-off between $\ell_1$-norms and small compatibility constants are possible. This implies, in particular for correlated design, improved bounds for the prediction error of the Lasso estimator as compared to the methods based on restricted eigenvalue or compatibility conditions only.

We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator. In literature, oracle inequalities have been derived under restricted eigenvalue or compatibility conditions. In this paper, we complement this with entropy conditions which allow one to improve the dual norm bound, and demonstrate how this leads to new oracle inequalities. The new oracle inequalities show that a smaller choice for the tuning parameter and a trade-off between $\ell_1$-norms and small compatibility constants are possible. This implies, in particular for correlated design, improved bounds for the prediction error of the Lasso estimator as compared to the methods based on restricted eigenvalue or compatibility conditions only.

This thesis represents a model of web-database analysis, design and implementation. An electronic bulletin board for the Naval Postgraduate School is implemented for demonstration. The model includes Oracle8i DBMS as the database, Java (Java Server Pages, Java Script Enterprise Java Beans, Java Servlets) as the programming language. Apache HTTP Server v.1.3 Tomcat v.1.2 is used as the Web server and JSP engine. Windows NT4.O served as the OS environment From the technical aspect, Database Management Systems, Web-Database Architectures, Server Extension Programs, Oracle8i as well as several other software and hardware components are reviewed, and some are recommended.

This thesis represents a model of web-database analysis, design and implementation. An electronic bulletin board for the Naval Postgraduate School is implemented for demonstration. The model includes Oracle8i DBMS as the database, Java (Java Server Pages, Java Script Enterprise Java Beans, Java Servlets) as the programming language. Apache HTTP Server v.1.3 Tomcat v.1.2 is used as the Web server and JSP engine. Windows NT4.O served as the OS environment From the technical aspect, Database Management Systems, Web-Database Architectures, Server Extension Programs, Oracle8i as well as several other software and hardware components are reviewed, and some are recommended.

Oracle on Organizing a Design Jam The Gamification Summit - The Concourse Exhibition Center Oracle Applications User Experience is getting into the game with research and design on how to gamify enterprise applications, such as the company's Fusion. Oracle's research with real users helped them change how they design applications to include game mechanics, increasing participation in communities, increasing productivity in workers and making work more engaging for all. Learn the research approaches, design patterns and examples that have made Oracle a leader in gamification of enterprise apps - and how you can make them work for your organization.Erika Webb, User Experience Manager at Oracle

Oracle on Organizing a Design Jam The Gamification Summit - The Concourse Exhibition Center Oracle Applications User Experience is getting into the game with research and design on how to gamify enterprise applications, such as the company's Fusion. Oracle's research with real users helped them change how they design applications to include game mechanics, increasing participation in communities, increasing productivity in workers and making work more engaging for all. Learn the research approaches, design patterns and examples that have made Oracle a leader in gamification of enterprise apps - and how you can make them work for your organization.Erika Webb, User Experience Manager at Oracle

Oracle on Organizing a Design Jam The Gamification Summit - The Concourse Exhibition Center Oracle Applications User Experience is getting into the game with research and design on how to gamify enterprise applications, such as the company's Fusion. Oracle's research with real users helped them change how they design applications to include game mechanics, increasing participation in communities, increasing productivity in workers and making work more engaging for all. Learn the research approaches, design patterns and examples that have made Oracle a leader in gamification of enterprise apps - and how you can make them work for your organization.Erika Webb, User Experience Manager at Oracle

This thesis represents a model of web-database analysis, design and implementation. An electronic bulletin board for the Naval Postgraduate School is implemented for demonstration. The model includes Oracle8i DBMS as the database, Java (Java Server Pages, Java Script Enterprise Java Beans, Java Servlets) as the programming language. Apache HTTP Server v.1.3 Tomcat v.1.2 is used as the Web server and JSP engine. Windows NT4.O served as the OS environment From the technical aspect, Database Management Systems, Web-Database Architectures, Server Extension Programs, Oracle8i as well as several other software and hardware components are reviewed, and some are recommended.

This thesis represents a model of web-database analysis, design and implementation. An electronic bulletin board for the Naval Postgraduate School is implemented for demonstration. The model includes Oracle8i DBMS as the database, Java (Java Server Pages, Java Script Enterprise Java Beans, Java Servlets) as the programming language. Apache HTTP Server v.1.3 Tomcat v.1.2 is used as the Web server and JSP engine. Windows NT4.O served as the OS environment From the technical aspect, Database Management Systems, Web-Database Architectures, Server Extension Programs, Oracle8i as well as several other software and hardware components are reviewed, and some are recommended.

This thesis represents a model of web-database analysis, design and implementation. An electronic bulletin board for the Naval Postgraduate School is implemented for demonstration. The model includes Oracle8i DBMS as the database, Java (Java Server Pages, Java Script Enterprise Java Beans, Java Servlets) as the programming language. Apache HTTP Server v.1.3 Tomcat v.1.2 is used as the Web server and JSP engine. Windows NT4.O served as the OS environment From the technical aspect, Database Management Systems, Web-Database Architectures, Server Extension Programs, Oracle8i as well as several other software and hardware components are reviewed, and some are recommended.

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders' valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis.

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders' valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis.

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders' valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis.

Preserved by Hyrule Interviews. Our mission is to celebrate and preserve all interviews or public writings by people who have worked on The Legend of Zelda game series. INTERVIEWEES: Hidemaro Fujibayashi PRODUCTS DISCUSSED: Oracle of Seasons, Oracle of Ages PUBLICATION: Game Design Gazette, Nintendo Online Magazine JOURNALIST/MEDIA: Ishaan Sahdev, Claire Rouveyrol PUBLISH DATE: 2001 LANGUAGE: English SOURCE/ISBN: http://www.gamedesigngazette.com/2017/12/the-legend-of-zelda-oracle-of.html

Preserved by Hyrule Interviews. Our mission is to celebrate and preserve all interviews or public writings by people who have worked on The Legend of Zelda game series. INTERVIEWEES: Hidemaro Fujibayashi PRODUCTS DISCUSSED: Oracle of Seasons, Oracle of Ages PUBLICATION: Game Design Gazette, Nintendo Online Magazine JOURNALIST/MEDIA: Ishaan Sahdev, Claire Rouveyrol PUBLISH DATE: 2001 LANGUAGE: English SOURCE/ISBN: http://www.gamedesigngazette.com/2017/12/the-legend-of-zelda-oracle-of.html