Data Warehouse Part Ii: Dwh Data Modeling & Olap
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Top 40 Singles Top 40 Albums
16 December 2001 CHART #1290 Top 40 Singles Top 40 Albums FALLIN' GET THE PARTY STARTED THE RECORD INTERNATIONAL SUPER HITS 1 Alicia Keys 21 Pink 1 Bee Gees 21 Green Day Last week 1 / 15 weeks Gold / BMG Last week 22 / 5 weeks BMG Last week 3 / 3 weeks Platinum / UNIVERSAL Last week 30 / 6 weeks Gold / WARNER EVERYWHERE QUEEN OF MY HEART SWING WHEN YOU'RE WINNING WHOA NELLY 2 Michelle Branch 22 Westlife 2 Robbie Williams 22 Nelly Furtado Last week 11 / 7 weeks WARNER Last week 23 / 4 weeks BMG Last week 1 / 4 weeks Platinum x3 / CAPITOL/EMI Last week 20 / 35 weeks Platinum x2 / UNIVERSAL CAN'T GET YOU OUT OF MY HEAD 1+1+1 (IT AIN'T TWO) ECHOES - THE BEST OF COLLECTION 3 Kylie Minogue 23 K'Lee 3 Pink Floyd 23 Tracy Chapman Last week 3 / 11 weeks Gold / FMR Last week 17 / 9 weeks UNIVERSAL Last week 2 / 5 weeks Platinum x2 / CAPITOL/EMI Last week 21 / 7 weeks Platinum / WARNER EMOTION FAMILY AFFAIR MY GIFT TO YOU ULTIMATE CAT STEVENS 4 Destiny's Child 24 Mary J Blige 4 Hayley Westenra 24 Cat Stevens Last week 2 / 10 weeks COL/SONY Last week 33 / 4 weeks UNIVERSAL Last week 9 / 3 weeks Platinum / UNIVERSAL Last week 15 / 2 weeks Platinum x3 / UNIVERSAL STARLIGHT ALL IT TAKES TUSCAN SKIES DREAMING 5 The Superman Lovers 25 Stellar 5 Andrea Bocelli 25 Andre Rieu Last week 4 / 15 weeks BMG Last week 26 / 17 weeks EPIC/SONY Last week 10 / 7 weeks Platinum / UNIVERSAL Last week 36 / 2 weeks UNIVERSAL TOO CLOSE YOUTHFUL ROTTEN APPLES - GREATEST HIT.. -
Normalized Form Snowflake Schema
Normalized Form Snowflake Schema Half-pound and unascertainable Wood never rhubarbs confoundedly when Filbert snore his sloop. Vertebrate or leewardtongue-in-cheek, after Hazel Lennie compartmentalized never shreddings transcendentally, any misreckonings! quite Crystalloiddiverted. Euclid grabbles no yorks adhered The star schemas in this does not have all revenue for this When done use When doing table contains less sensible of rows Snowflake Normalizationde-normalization Dimension tables are in normalized form the fact. Difference between Star Schema & Snow Flake Schema. The major difference between the snowflake and star schema models is slot the dimension tables of the snowflake model may want kept in normalized form to. Typically most of carbon fact tables in this star schema are in the third normal form while dimensional tables are de-normalized second normal. A relation is danger to pause in First Normal Form should each attribute increase the. The model is lazy in single third normal form 1141 Options to Normalize Assume that too are 500000 product dimension rows These products fall under 500. Hottest 'snowflake-schema' Answers Stack Overflow. Learn together is Star Schema Snowflake Schema And the Difference. For step three within the warehouses we tested Redshift Snowflake and Bigquery. On whose other hand snowflake schema is in normalized form. The CWM repository schema is a standalone product that other products can shareeach product owns only. The main difference between in two is normalization. Families of normalized form snowflake schema snowflake. Star and Snowflake Schema in Data line with Examples. Is spread the dimension tables in the snowflake schema are normalized. Like price weight speed and quantitiesie data execute a numerical format. -
Sixth Normal Form
3 I January 2015 www.ijraset.com Volume 3 Issue I, January 2015 ISSN: 2321-9653 International Journal for Research in Applied Science & Engineering Technology (IJRASET) Sixth Normal Form Neha1, Sanjeev Kumar2 1M.Tech, 2Assistant Professor, Department of CSE, Shri Balwant College of Engineering &Technology, DCRUST University Abstract – Sixth Normal Form (6NF) is a term used in relational database theory by Christopher Date to describe databases which decompose relational variables to irreducible elements. While this form may be unimportant for non-temporal data, it is certainly important when maintaining data containing temporal variables of a point-in-time or interval nature. With the advent of Data Warehousing 2.0 (DW 2.0), there is now an increased emphasis on using fully-temporalized databases in the context of data warehousing, in particular with next generation approaches such as Anchor Modeling . In this paper, we will explore the concepts of temporal data, 6NF conceptual database models, and their relationship with DW 2.0. Further, we will also evaluate Anchor Modeling as a conceptual design method in which to capture temporal data. Using these concepts, we will indicate a path forward for evaluating a possible translation of 6NF-compliant data into an eXtensible Markup Language (XML) Schema for the purpose of describing and presenting such data to disparate systems in a structured format suitable for data exchange. Keywords :, 6NF,SQL,DKNF,XML,Semantic data change, Valid Time, Transaction Time, DFM I. INTRODUCTION Normalization is the process of restructuring the logical data model of a database to eliminate redundancy, organize data efficiently and reduce repeating data and to reduce the potential for anomalies during data operations. -
Sexion Q 3-14
www.ExpressGayNews.com • June 17, 2002 Q1 CYMK CoverCover StoryStory Even though her astonishing voice makes it seem predestined, stardom has been a long time coming for Anastacia. The Chicago-born diva went through many start-and-stop career moments before her chops were recognized. She traipsed along the edge of the music world as a backup dancer and upscale hair salon receptionist for years with her biggest talent going unrecognized, mostly because nobody could quite figure out what to do with her. She was ready to give up until an appearance on the now-defunct MTV talent show The Cut gave her the chance to shine. Although she didn’t win top prize on the show, the budding vocalist did scoop up a record deal and recognition from the likes of pop superstar Michael Jackson and megaproducer David Foster. Now, she has finally earned her stripes after years of honing her sound and gaining a massive European follow- ing. Her debut album, Not That Kind, (See ANASTACIA on page Q9) Q2 www.ExpressGayNews.com • June 17, 2002 CYMK InIn ReviewReview The first clue that Nixon’s Nixon is not fact that the Kennedys were assassinated or The funniest scenes are when Nixon that they are watching the real thing. His supposed to be historically accurate is that else they would be stuck with the whole goads Kissinger into playacting conversations performance is so dead on that it makes you there are 68 stars on the American flag that Vietnam mess and toy with the idea of starting with Brezhnev and Mao Tse Tung. -
The Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: an Assessment of Experimental Approach Based on Query Execution Performance
WSEAS TRANSACTIONS on COMPUTERS Radek Němec, František Zapletal The Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: An Assessment of Experimental Approach Based on Query Execution Performance RADEK NĚMEC, FRANTIŠEK ZAPLETAL Department of Systems Engineering Faculty of Economics, VŠB - Technical University of Ostrava Sokolská třída 33, 701 21 Ostrava CZECH REPUBLIC [email protected], [email protected] Abstract: - The decision making processes need to reflect changes in the business world in a multidimensional way. This includes also similar way of viewing the data for carrying out key decisions that ensure competitiveness of the business. In this paper we focus on the Business Intelligence system as a main toolset that helps in carrying out complex decisions and which requires multidimensional view of data for this purpose. We propose a novel experimental approach to the design a multidimensional data model that uses principles of the anchor modeling technique. The proposed approach is expected to bring several benefits like better query execution performance, better support for temporal querying and several others. We provide assessment of this approach mainly from the query execution performance perspective in this paper. The emphasis is placed on the assessment of this technique as a potential innovative approach for the field of the data warehousing with some implicit principles that could make the process of the design, implementation and maintenance of the data warehouse more effective. The query performance testing was performed in the row-oriented database environment using a sample of 10 star queries executed in the environment of 10 sample multidimensional data models. -
Translating Data Between Xml Schema and 6Nf Conceptual Models
Georgia Southern University Digital Commons@Georgia Southern Electronic Theses and Dissertations Graduate Studies, Jack N. Averitt College of Spring 2012 Translating Data Between Xml Schema and 6Nf Conceptual Models Curtis Maitland Knowles Follow this and additional works at: https://digitalcommons.georgiasouthern.edu/etd Recommended Citation Knowles, Curtis Maitland, "Translating Data Between Xml Schema and 6Nf Conceptual Models" (2012). Electronic Theses and Dissertations. 688. https://digitalcommons.georgiasouthern.edu/etd/688 This thesis (open access) is brought to you for free and open access by the Graduate Studies, Jack N. Averitt College of at Digital Commons@Georgia Southern. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons@Georgia Southern. For more information, please contact [email protected]. 1 TRANSLATING DATA BETWEEN XML SCHEMA AND 6NF CONCEPTUAL MODELS by CURTIS M. KNOWLES (Under the Direction of Vladan Jovanovic) ABSTRACT Sixth Normal Form (6NF) is a term used in relational database theory by Christopher Date to describe databases which decompose relational variables to irreducible elements. While this form may be unimportant for non-temporal data, it is certainly important for data containing temporal variables of a point-in-time or interval nature. With the advent of Data Warehousing 2.0 (DW 2.0), there is now an increased emphasis on using fully-temporalized databases in the context of data warehousing, in particular with approaches such as the Anchor Model and Data Vault. In this work, we will explore the concepts of temporal data, 6NF conceptual database models, and their relationship with DW 2.0. Further, we will evaluate the Anchor Model and Data Vault as design methods in which to capture temporal data. -
Assessment of Helical Anchors Bearing Capacity for Offshore Aquaculture Applications
The University of Maine DigitalCommons@UMaine Electronic Theses and Dissertations Fogler Library Summer 8-23-2019 Assessment of Helical Anchors Bearing Capacity for Offshore Aquaculture Applications Leon D. Cortes Garcia University of Maine, [email protected] Follow this and additional works at: https://digitalcommons.library.umaine.edu/etd Recommended Citation Cortes Garcia, Leon D., "Assessment of Helical Anchors Bearing Capacity for Offshore Aquaculture Applications" (2019). Electronic Theses and Dissertations. 3094. https://digitalcommons.library.umaine.edu/etd/3094 This Open-Access Thesis is brought to you for free and open access by DigitalCommons@UMaine. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of DigitalCommons@UMaine. For more information, please contact [email protected]. ASSESSMENT OF HELICAL ANCHORS BEARING CAPACITY FOR OFFSHORE AQUACULTURE APPLICATIONS By Leon D. Cortes Garcia B.S. National University of Colombia, 2016 A THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Civil Engineering) The Graduate School The University of Maine August 2019 Advisory Committee: Aaron P. Gallant, Professor of Civil Engineering, Co‐Advisor Melissa E. Landon, Professor of Civil Engineering, Co‐Advisor Kimberly D. Huguenard, Professor of Civil Engineering Copyright 2019 Leon D. Cortes Garcia All Rights Reserved ii ASSESSMENT OF HELICAL ANCHORS BEARING CAPACITY FOR OFFSHORE AQUACULTURE APPLICATIONS. By Leon Cortes Garcia Thesis Advisor(s): Dr. Aaron Gallant, Dr. Melissa Landon An Abstract of the Thesis Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Civil Engineering) August 2019 Aquaculture in Maine is an important industry with expected growth in the coming years to provide food in an ecological and environmentally sustainable way. -
Chapter 7 Multi Dimensional Data Modeling
Chapter 7 Multi Dimensional Data Modeling Fundamentals of Business Analytics” Content of this presentation has been taken from Book “Fundamentals of Business Analytics” RN Prasad and Seema Acharya Published by Wiley India Pvt. Ltd. and it will always be the copyright of the authors of the book and publisher only. Basis • You are already familiar with the concepts relating to basics of RDBMS, OLTP, and OLAP, role of ERP in the enterprise as well as “enterprise production environment” for IT deployment. In the previous lectures, you have been explained the concepts - Types of Digital Data, Introduction to OLTP and OLAP, Business Intelligence Basics, and Data Integration . With this background, now its time to move ahead to think about “how data is modelled”. • Just like a circuit diagram is to an electrical engineer, • an assembly diagram is to a mechanical Engineer, and • a blueprint of a building is to a civil engineer • So is the data models/data diagrams for a data architect. • But is “data modelling” only the responsibility of a data architect? The answer is Business Intelligence (BI) application developer today is involved in designing, developing, deploying, supporting, and optimizing storage in the form of data warehouse/data marts. • To be able to play his/her role efficiently, the BI application developer relies heavily on data models/data diagrams to understand the schema structure, the data, the relationships between data, etc. In this lecture, we will learn • About basics of data modelling • How to go about designing a data model at the conceptual and logical levels? • Pros and Cons of the popular modelling techniques such as ER modelling and dimensional modelling Case Study – “TenToTen Retail Stores” • A new range of cosmetic products has been introduced by a leading brand, which TenToTen wants to sell through its various outlets. -
ER/Studio Enterprise Data Modeling
ER/Studio Enterprise Data Modeling ER/Studio®, a model-driven data architecture and database design solution, helps companies discover, document, and reuse data assets. With round-trip database support, data architects have the power to thoroughly analyze existing data sources as well as design and implement high quality databases that reflect business needs. The highly-readable visual format enhances communication across job functions, from business analysts to application developers. ER/Studio Enterprise also enables team and enterprise collaboration with its repository. • Enhance visibility into your existing data assets • Effectively communicate models across the enterprise Related Products • Improve data consistency • Trace data origins and whereabouts to enhance data integration and accuracy ER/Studio Viewer View, navigate and print ER/Studio ENHANCE VISIBILITY INTO YOUR EXISTING DATA ASSETS models in a view-only environ- ment. As data volumes grow and environments become more complex corporations find it increasingly difficult to leverage their information. ER/Studio provides an easy- Describe™ to-use visual medium to document, understand, and publish information about data assets so that they can be harnessed to support business objectives. Powerful Design, document, and maintain reverse engineering of industry-leading database systems allow a data modeler to enterprise applications written in compare and consolidate common data structures without creating unnecessary Java, C++, and IDL for better code duplication. Using industry standard notations, data modelers can create an infor- quality and shorter time to market. mation hub by importing, analyzing, and repurposing metadata from data sources DT/Studio® such as business intelligence applications, ETL environments, XML documents, An easy-to-use visual medium to and other modeling solutions. -
Advantages of Dimensional Data Modeling
Advantages of Dimensional Data Modeling 2997 Yarmouth Greenway Drive Madison, WI 53711 (608) 278-9964 www.sys-seminar.com Advantages of Dimensional Data Modeling 1 Top Ten Reasons Why Your Data Model Needs a Makeover 1. Ad hoc queries are difficult to construct for end-users or must go through database “gurus.” 2. Even standard reports require considerable effort and detail knowledge of the database. 3. Data is not integrated or is inconsistent across sources. 4. Changes in data values or in data sources cannot be handled gracefully. 5. The structure of the data does not mirror business processes or business rules. 6. The data model limits which BI tools can be used. 7. There is no system for maintaining change history or collecting metadata. 8. Disk space is wasted on redundant values. 9. Users who might benefit from the data don’t use it. 10.Maintenance is tedious and ad hoc. 2 Advantages of Dimensional Data Modeling Part 1 3 Part 1 - Data Model Overview •What is data modeling and why is it important? •Three common data models: de-normalized (SAS data sets) normalized dimensional model •Benefits of the dimensional model 4 What is data modeling? • The generalized logical relationship among tables • Usually reflected in the physical structure of the tables • Not tied to any particular product or DBMS • A critical design consideration 5 Why is data modeling important? •Allows you to optimize performance •Allows you to minimize costs •Facilitates system documentation and maintenance • The dimensional data model is the foundation of a well designed data mart or data warehouse 6 Common data models Three general data models we will review: De-normalized Expected by many SAS procedures Normalized Often used in transaction based systems such as order entry Dimensional Often used in data warehouse systems and systems subject to ad hoc queries. -
Anastacia Not That Kind Mp3, Flac, Wma
Anastacia Not That Kind mp3, flac, wma DOWNLOAD LINKS (Clickable) Genre: Electronic / Funk / Soul / Pop Album: Not That Kind Country: Asia Released: 2000 Style: Rhythm & Blues, Downtempo MP3 version RAR size: 1246 mb FLAC version RAR size: 1454 mb WMA version RAR size: 1884 mb Rating: 4.6 Votes: 518 Other Formats: AAC DTS XM RA MP1 VQF ASF Tracklist Hide Credits Not That Kind Arranged By – Ric Wake, Richie JonesArranged By [Backing Vocals] – Anastacia, BeBe 1 WinansBacking Vocals – BeBe WinansDrums – Richie JonesEngineer [Mix Assistant 3:20 Engineer] – Joe ErnstGuitar – Eric KupperGuitar [Guitar Solo] – Chris GoerckeRecorded By [Vocals] – Thomas R. YezziWritten-By – Marvin Young, Will Wheaton I'm Outta Love 2 4:03 Drums – Louis Biancaniello, Sam WattersGuitar – Vernon Black 3 Cowboys & Kisses 4:41 4 Who's Gonna Stop The Rain 5:00 Love Is Alive Backing Vocals – Audrey L. Wheeler*, Keith Fluitt, Nicky Richards*, Rob MathesEngineer [Assistant] – Chris Brooke, Jim Annunziatto*, Ron Last, Ryan HewittEngineer [Pro-tools] – Jim Annunziatto*Guitar, Keyboards – Russ DeSalvoOrgan [Hammond B-3] – Loris 5 HollandPiano – Leon PendarvisProducer [Additional Production] – Richie JonesProducer 4:06 [Production Coordinator] – David Barratt Programmed By [Additional Programming] – Pat Carroll*, Russ DeSalvoProgrammed By [Percussion], Drum Programming – Richie JonesRecorded By [Lead Vocals Assistant Recorder] – Dave PolerRecorded By [Track] – Bob Cadway, Dan HetzelWritten-By – Gary Wright I Ask Of You 6 4:27 Guitar – Chris Camozzi Wishing Well Arranged By -
SOUL ASYLUM Biography by Arthur Levy
SOUL ASYLUM biography by Arthur Levy “It’s a crazy mixed up world out there, Someone’s always got a gun and it’s all about money You live with loneliness, or you live with somebody who’s crazy It’s just a crazy mixed up world …” (“Crazy Mixed Up World”) Chapter 1. Every Cloud Has One Renewed and revitalized, Soul Asylum founders Dave Pirner and Dan Murphy return to rock’s front line with THE SILVER LINING, their first new studio release since 1998’s Candy From a Stranger. That album inadvertently kicked off a seven-year sabbatical for the group, which telescoped into the death of bassist Karl Mueller in June 2005, the other founding member of the triumvirate that has steered Soul Asylum through rock’s white water for the past two decades plus. The re-emergence of the group on THE SILVER LINING is as much a reaffirmation of Soul Asylum’s commitment to the music as it is a dedication to Karl, who worked and played on the album right up until the end. They were joined in the studio by not-so-new heavyweight Minneapolis drummer Michael Bland (who has played with everyone from Paul Westerberg to Prince). The band is now complemented by Tommy Stinson on bass, a member of fellow Twin Cities band the Replacements since age 13, and a pal of Dan’s since he was in high school and Tommy in junior high. Tommy was the only friend that Karl could endorse to replace himself in the band. This hard-driving lineup was introduced for the first time in October 2005, when they played sold-out showcase dates at First Avenue in Minneapolis and the Bowery Ballroom in New York – within three days.