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Part 2 Predicate Pushdown
 
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Oracle Big Data SQL - Learn about partition pruning, storage indexes and predicate push down. ================================= For more information, see http://www.oracle.com/goto/oll Copyright © 2017 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
How to understand and use the query optimizer – Couchbase Connect 2016
 
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Every flight has a flight plan. Every query has a query plan. You must have seen its text form, called EXPLAIN PLAN. Query optimizer is responsible for creating this query plan for every query, and it tries to create an optimal plan for every query. In Couchbase, the query optimizer has to choose the most optimal index for the query, decide on the predicates to push down to index scans, create appropriate spans (scan ranges) for each index, understand the sort (ORDER BY) and pagination (OFFSET, LIMIT) requirements, and create the plan accordingly. When you think there is a better plan, you can hint the optimizer with USE INDEX. This talk will teach you how the optimizer selects the indices, index scan methods, and joins. It will teach you the analysis of the optimizer behavior using EXPLAIN plan and how to change the choices optimizer makes. Speaker: Keshav Murthy, Director, Query Development, Couchbase Slideshare: http://www.slideshare.net/Couchbase/how-to-understand-and-use-the-query-optimizer Visit our website for more information: https://www.couchbase.com/
Views: 460 Couchbase
Deep dive on SQL Server and big data - BRK4021
 
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Many customers have investments in data lakes with big data storage and infrastructure. Come explore a deep dive behind the technology for big data integration with SQL Server including Polybase futures and scalable performance.
Views: 764 Microsoft Ignite
Chat with Oracle's Real World Performance Team 20140516 May 16
 
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Andrew Holdsworth chats for 30 minutes about real-world performance challenges, and techniques that could help overcome them, and even eliminate them for good. Also, check out the new Real-World Performance Learning Library (http://www.oracle.com/goto/oll/rwp)! Copyright © 2014 Oracle and/or its affiliates. Oracle® is a registered trademark of Oracle and/or its affiliates. All rights reserved. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the "Materials"). The Materials are provided "as is" without any warranty of any kind, either express or implied, including without limitation warranties of merchantability, fitness for a particular purpose, and non-infringement.
The roadmap for SQL Server - BRK2416
 
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SQL Server 2017 has brought to market a new modern data platform including support for Linux, Docker Containers and rich features in intelligent performance, HADR, machine learning, and graph database. Come learn about the roadmap and new functionality planned for SQL Server including intelligent query processing, data virtualization, new features for mission critical security and HADR, and new scenarios for Linux and Docker Containers.
Views: 274 Microsoft Ignite
Lecture 15: Coreference Resolution
 
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Lecture 15 covers what is coreference via a working example. Also includes research highlight "Summarizing Source Code", an introduction to coreference resolution and neural coreference resolution. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Refactoring a 1000 Lines of Code Method into Clean(er) Code (in Serbian)
 
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Long functions in even longer classes can often be found in mature code bases. Even though every programmer knows it's wrong to keep such a beast in production, every one of us has been feeding one of those for at least some time. In this lecture, we will show the process of building large functions from scratch. We will then turn attention to one such function, which has about 1000 lines of code. You will see why we need to break such monstrous functions into smaller chunks and then we will embark on a voyage to refactor and redesign it into smaller chunks of code. If you have passion for Sudoku, then the example we present will surely amuse you. The program we will be dealing with is setting up Sudoku problems and then it solves each problem, verbalizing all decisions and explaining the solution in common English sentences. But, the way in which this interesting program does its task is, at the same time, the greatest impediment to its further development. That is the point at which this lecture begins. Before watching this recording, you may wish to try fixing the same code on your own. Please download the initial solution from GitHub repository: https://github.com/zoran-horvat/sudoku-kata
Views: 1662 Zoran Horvat
Jean-Pierre Dijcks, Oracle - On the Ground - #theCUBE
 
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Jean-Pierre Dijcks, Master Product Manager at Oracle, sits down with host Peter Burris at Oracle’s Redwood Shores Headquarters for a special On the Ground segment. @theCUBE
Versioning and Migrating with Core Data - Intermediate Core Data Tutorial - raywenderlich.com
 
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Relationships between data is critical to be successful in Core Data. In this video, you'll learn how to create them in Xcode. Watch the full series over here: https://videos.raywenderlich.com/courses/intermediate-core-data/lessons/1 ---- About www.raywenderlich.com: raywenderlich.com is a website focused on developing high quality programming tutorials. Our goal is to take the coolest and most challenging topics and make them easy for everyone to learn – so we can all make amazing apps. We are also focused on developing a strong community. Our goal is to help each other reach our dreams through friendship and cooperation. As you can see below, a bunch of us have joined forces to make this happen: authors, editors, subject matter experts, app reviewers, and most importantly our amazing readers! ---- About Core Data (from Wikipedia) Core Data is an object graph and persistence framework provided by Apple in the macOS and iOS operating systems. It was introduced in Mac OS X 10.4 Tiger and iOS with iPhone SDK 3.0. It allows data organised by the relational entity–attribute model to be serialized into XML, binary, or SQLite stores. The data can be manipulated using higher level objects representing entities and their relationships. Core Data manages the serialised version, providing object lifecycle and object graph management, including persistence. Core Data interfaces directly with SQLite, insulating the developer from the underlying SQL. Just as Cocoa Bindings handle many of the duties of the controller in a model–view–controller design, Core Data handles many of the duties of the data model. Among other tasks, it handles change management, serializing to disk, memory footprint minimization and queries against the data. Core Data owes much of its design to an early NeXT product, Enterprise Objects Framework (EOF). EOF was specifically aimed at object-relational mapping for high-end SQL database engines such as Microsoft SQL Server and Oracle. EOF's purpose was twofold: first, to connect to the database engine and hide the implementation details; second, to read the data out of the simple relational format and translate that into a set of objects. Developers typically interacted with the objects only, which dramatically simplifies development of complex programs, at the cost of some "setup". The EOF object model was deliberately designed to make the resulting programs "document like", in that the user could edit the data locally in memory, and then write out all changes with a single Save command. Throughout its history, EOF "contained" a number of bits of extremely useful code that were not otherwise available under NeXTSTEP/OpenStep. For instance, EOF required the ability to track which objects were "dirty" so the system could later write them out. This was presented to the developer not only as a document-like system, but also in the form of an unlimited "Undo" command stack. Many developers complained that this state management code was far too useful to be isolated in EOF, and it was later moved into the Cocoa API during the transition to Mac OS X. Oddly, what was not translated was EOF itself. EOF was used primarily along with another OpenStep-era product, WebObjects, which was an application server originally based on Objective-C. At the time, Apple was in the process of porting WebObjects to the Java programming language, and as part of this conversion, EOF became much more difficult to use from Cocoa. Enough developers complained about this that Apple apparently decided to do something about it. One critical realization is that the object state management system in EOF did not really have anything to do with relational databases. The same code could be, and was, used by developers to manage graphs of other objects as well. In this role, the really useful parts of EOF were those that automatically built the object sets from the raw data, and then tracked them. It is this concept, and perhaps code, that forms the basis of Core Data. About Swift (from Wikipedia) Swift is a general-purpose, multi-paradigm, compiled programming language developed by Apple Inc. for iOS, macOS, watchOS, tvOS, and Linux. Swift is designed to work with Apple's Cocoa and Cocoa Touch frameworks and the large body of extant Objective-C (ObjC) code written for Apple products. Swift is intended to be more resilient to erroneous code ("safer") than Objective-C, and more concise. It is built with the LLVM compiler framework included in Xcode 6 and later and, on platforms other than Linux, uses the Objective-C runtime library, which allows C, Objective-C, C++ and Swift code to run within one program. Swift supports the core concepts that made Objective-C flexible, notably dynamic dispatch, widespread late binding, extensible programming and similar features. These features also have well-known performance and safety trade-offs, which Swift was designed to address.
Views: 3478 raywenderlich.com
Lecture - 14 Query Processing and Optimization
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 107111 nptelhrd
Graph Links Prototype in Arcs
 
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This shows how RDF discovery plays a part in Arcs, a Semantic Web Editor & Browser that I'm hacking on. Arcs lets people navigate around some nodes described in a set of RDF graphs, and of course it's nice to be able to load in new data. So for example here I'm bringing up information about a person (using Tim since his FOAF file is nicely managed for this sort of thing), and then clicking Show Graph Links to bring up a blob next to every resource shown in this node view. Note that when the blobs come up at first, they're a mid-grey. Then Arcs goes and does RDF discovery on all of the links, which means a series of HEADs and possibly peeking at the files, and turns the blobs either blue indicating that it reckons there's RDF data afoot, or light grey, which means it doesn't think it can get anything there. Though it means a lot of network activity if you're displaying a lot of data, it's really nifty because it shows you what things you can further explore in Arcs by adding more data, and what things you'll probably have to look on the web (for which there's a Show Web Links thing too).
Views: 608 inamidst
Neo4j Online Meetup #37 :GQL: It’s Time for a Single Property Graph Query Language
 
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The time has come to create a single, unified property graph query language. Different languages for different products help no one. We’ve heard from the graph community that a common query language would be powerful: more developers with transferable expertise; portable queries; solutions that leverage multiple graph options; and less vendor lock-in. One language, one skill set. In this session Amy Hodler and Alastair Green will explain the GQL proposal and run a Q&A session. https://gql.today/#vote
Views: 600 Neo4j
Azure Red Shirt Dev Tour NYC 2017 | Part 2
 
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Join Scott Guthrie and some members of the Cloud Developer Advocate team in a tour of Azure services and tools. By the end of this viewing you'll have learned something new and be able to quickly get started trying something in Azure. Visit https://aka.ms/redshirtdemos for a list of all the things demonstrated in this event and to try them out yourself.
Views: 910 Microsoft Azure
Running open-source Databases on Google Cloud Platform (Google Cloud Next '17)
 
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Learn about the various options for running open-source databases on GCP, both self-managed and fully-managed. We will also do a deep dive with Quizlet about how to run MySQL effectively and efficiently on GCP. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Application Development here: https://goo.gl/YFgZpl
Migration experience from an on-premises enterprise data warehouse to Azure - BRK3327
 
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In this session, we take you through the challenge, lesson learned, and best practices from migrating an on-premises enterprise data warehouse workload to the Azure services.
Views: 82 Microsoft Ignite
Reynold Xin | Spark Summit 2017
 
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Reynold Xin, Databricks, at #Spark Summit on #theCUBE
Cloud-Based Automated Software Reliability Services
 
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Google Tech Talk July 22, 2010 ABSTRACT Presented by Professor George Candea http://people.epfl.ch/george.candea This talk proposes cloud-based automated software reliability services (SRS), a step toward making testing and debugging of code as easy as using webmail. SRS is automatic, without human involvement from the service user's or provider's side; this is unlike today's "testing as a service" businesses, which employ humans to write tests. First, I will outline four of the SRS components we envision: a "home edition" on-demand testing service for consumers to verify the software they are about to install on their PC or mobile device; a "programmer's sidekick" enabling developers to thoroughly and promptly test their code with minimal upfront resource investment; a public "certification service," akin to Underwriters Labs, that independently assesses the reliability, safety, and security of software; and an "automated debugging" service that helps developers fix code based on bug reports from the field. Then I will present in detail execution synthesis, the technique that makes automated debugging (the latter SRS component) a reality. Given a program and a bug report, execution synthesis combines static analysis and symbolic execution to "synthesize" a thread schedule and various required program inputs that cause the reported bug to manifest. The synthesized execution can then be played back deterministically in a regular debugger, like gdb. We have found this determinism to be particularly useful in debugging concurrency bugs. Our technique requires no runtime tracing or program modifications, thus incurring no runtime overhead and being practical for use in production systems. We evaluate it on popular software (e.g., the SQLite database, ghttpd Web server, HawkNL network library, UNIX utilities) and find that, starting from mere bug reports, it can reproduce on its own several real concurrency and memory safety bugs in less than three minutes.
Views: 7362 GoogleTechTalks
Michael Stonebraker, 2014 ACM Turing Award Recipient
 
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Discusses his life, research, and business experience in the relational database management field. Topics cover his early life and education, development of INGRES and founding of the Ingres Corporation and later he extended the use of the relational structure into areas other than business data. For more information: http://amturing.acm.org/award_winners/stonebraker_1172121.cfm
Project Lambda: Functional Prog. Constructs and Simpler Concurrency in Java SE 8
 
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Abstract The big language features for Java SE 8 are lambda expressions (closures) and default methods (formerly called defender methods or virtual extension methods). Adding lambda expressions to the language opens up a host of new expressive opportunities for applications and libraries. You might assume that lambda expressions are simply a more syntactically compact form of inner classes, but, in fact, the implementation of lambda expressions is substantially different and builds on the invokedynamic feature added in Java SE 7. This session will explain the ideas behind lambda expressions, how they will be used in Java SE 8 and look at some of the details of their implementation. Speaker Simon Ritter is Manager of the Java Technology Evangelist team at Oracle Corporation. Simon has been in the IT business since 1984 and holds a Bachelor of Science degree in Physics from Brunel University in the U.K. Originally working in the area of UNIX development for AT&T UNIX System Labs and then Novell, Simon moved to Sun in 1996. At this time he started working with Java technology and has spent time working both in Java development and consultancy. Having moved to Oracle as part of the Sun acquisition he now focuses on the core Java platform, Java for client applications and embedded Java. He also continues to develop demonstrations that push the boundaries of Java for applications like gestural interfaces, embedded robot controllers and in-car systems. Follow him on Twitter,@speakjava, and his blog atblogs.oracle.com/speakjava.
Views: v JUG
Cypher Everywhere: Neo4j, Hadoop/Spark and the Unexpected — A. Green, M. Rydberg, D. Solovyov, Neo4j
 
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Cypher started in Neo4j. It's now used by SAP HANA Graph, Redis Graph and Agens Graph over PostgreSQL, among others. The Neo4j Graph Platform will include Cypher for Apache Spark, with Hadoop and other integrations, allowing the data lake to be projected into graphs. Speakers: Alastair Green, Mats Rydberg, Dimitry Solovyov Location: GraphConnect NYC 2017
Views: 357 Neo4j
Innovation Survival: Innovation in Science
 
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Google Tech Talk April 8, 2010 ABSTRACT Presented by W. David Schwaderer. Innovation is essential for all progress and competitive survival. It provides a democratic vehicle for individuals and upstarts to challenge and neutralize powerful incumbents. Yet, because change accompanies innovation, it is a double-edged sword. This presentation examines the historical reception transformative scientific breakthroughs initially received before widespread adoption. By example, it teaches principles that can help ensure change agents personally, and their organizations, are on the delivering side of innovation's sharp edge. W. David Schwaderer has a Masters Degree in Applied Mathematics from the California Institute of Technology and an MBA from the University of Southern California. He has worked at IBM, EDS, Adaptec, Symantec, and Silicon Valley startups. He has authored six commercial software programs for a variety of machine architectures using several different languages, dozens of articles, and ten technical books that explain complex technology in approachable ways. David's soon-to-be-published 11th book follows over 10 years of research and is titled "Innovation Survival - Concept, Courage, Chance, and Change".
Views: 24536 GoogleTechTalks
Brunch with Bernie - June 29, 2012
 
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US Senator Bernie Sanders (I-VT) joins Thom Hartmann for their weekly town hall meeting. If you liked this clip of The Thom Hartmann Program, please do us a big favor and share it with your friends... and hit that "like" button! http://www.thomhartmann.com Follow Us on Twitter: http://www.twitter.com/thom_hartmann Subscribe to The Thom Hartmann Program for more: http://www.youtube.com/subscription_center?add_user=thomhartmann
08/28/18 MNPS Board Meeting
 
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Coverage of the Metro Nashville Board of Public Education meeting held August 28, 2018
Views: 254 MetroNashville
Rebuilding the Getty Provenance Index as Linked Data
 
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Rebuilding the Getty Provenance Index as Linked Data Joshua Gomez Getty Research Institute Emily Pugh Getty Research Institute For more information see https://wp.me/p1LncT-6kw CNI Spring 2016 Membership Meeting April 4-5, 2016 San Antonio, TX https://www.cni.org/
Blessed Hope part 2 Crossway Church Texarkana Texas Pastor Michael Mauldin
 
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Blessed Hope part 2 Crossway Church Texarkana Texas Pastor Michael Mauldin
Views: 92 John Cook
The Great Gildersleeve: Leila Returns / The Waterworks Breaks Down / Halloween Party
 
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The Great Gildersleeve (1941--1957), initially written by Leonard Lewis Levinson, was one of broadcast history's earliest spin-off programs. Built around Throckmorton Philharmonic Gildersleeve, a character who had been a staple on the classic radio situation comedy Fibber McGee and Molly, first introduced on Oct. 3, 1939, ep. #216. The Great Gildersleeve enjoyed its greatest success in the 1940s. Actor Harold Peary played the character during its transition from the parent show into the spin-off and later in a quartet of feature films released at the height of the show's popularity. On Fibber McGee and Molly, Peary's Gildersleeve was a pompous windbag who became a consistent McGee nemesis. "You're a haa-aa-aa-aard man, McGee!" became a Gildersleeve catchphrase. The character was given several conflicting first names on Fibber McGee and Molly, and on one episode his middle name was revealed as Philharmonic. Gildy admits as much at the end of "Gildersleeve's Diary" on the Fibber McGee and Molly series (Oct. 22, 1940). He soon became so popular that Kraft Foods—looking primarily to promote its Parkay margarine spread — sponsored a new series with Peary's Gildersleeve as the central, slightly softened and slightly befuddled focus of a lively new family. Premiering on August 31, 1941, The Great Gildersleeve moved the title character from the McGees' Wistful Vista to Summerfield, where Gildersleeve now oversaw his late brother-in-law's estate and took on the rearing of his orphaned niece and nephew, Marjorie (originally played by Lurene Tuttle and followed by Louise Erickson and Mary Lee Robb) and Leroy Forester (Walter Tetley). The household also included a cook named Birdie. Curiously, while Gildersleeve had occasionally spoken of his (never-present) wife in some Fibber episodes, in his own series the character was a confirmed bachelor. In a striking forerunner to such later television hits as Bachelor Father and Family Affair, both of which are centered on well-to-do uncles taking in their deceased siblings' children, Gildersleeve was a bachelor raising two children while, at first, administering a girdle manufacturing company ("If you want a better corset, of course, it's a Gildersleeve") and then for the bulk of the show's run, serving as Summerfield's water commissioner, between time with the ladies and nights with the boys. The Great Gildersleeve may have been the first broadcast show to be centered on a single parent balancing child-rearing, work, and a social life, done with taste and genuine wit, often at the expense of Gildersleeve's now slightly understated pomposity. Many of the original episodes were co-written by John Whedon, father of Tom Whedon (who wrote The Golden Girls), and grandfather of Deadwood scripter Zack Whedon and Joss Whedon (creator of Buffy the Vampire Slayer, Firefly and Dr. Horrible's Sing-Along Blog). The key to the show was Peary, whose booming voice and facility with moans, groans, laughs, shudders and inflection was as close to body language and facial suggestion as a voice could get. Peary was so effective, and Gildersleeve became so familiar a character, that he was referenced and satirized periodically in other comedies and in a few cartoons. http://en.wikipedia.org/wiki/Great_Gildersleeve
Views: 133057 Remember This
Web Programming - Computer Science for Business Leaders 2016
 
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noSQL, SQL; APIs; JavaScript
Views: 36709 CS50
যিকির করার গুরুত্ব এবং সর্ব উত্তম যিকির কোনটি ?শাইখ ইমাম হোসেন
 
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Views: 1522 Noorul Alanoor
CJA 01.03: Panel 2 - Santa Fe
 
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Panel 2 - Views from Federal Public Defenders Ad Hoc Committee to Review the Criminal Justice Act New Mexico State Capitol Building, Room 311 490 Old Santa Fe Trail, Santa Fe, NM 87501 Panel Participants: Maureen Franco (FPD, W.D. Tex.) Virginia Grady (FPD, D. Colo. & Wyo.) Jason Hawkins (FPD, N.D. Tex.) Stephen McCue (FPD, D.N.M.)
Views: 60 LawResourceOrg

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