...Table of Contents Setting up the subject area (if required) 2 Bringing in the tables 4 Assigning appropriate Domains 5 The logical Model 6 Identifying a many to many relationship 7 Replacing a mapping table 7 Renaming Logical Model 15 Entity Names 15 Relationship Names 16 Attribute Names 16 Revisiting Physical Model 19 Relationship Naming 20 FINAL PRODUCT 21 Logical VS Physical 21 Setting up the subject area (if required) 1. Open the target and source Erwin models in the same instance of Erwin. 2. If required create a new subject area in the target model where the tables are going to be copied. Figure 1 – Creating a new subject area 3. Change the default theme to ‘Classic Theme’ (right click on the diagram page and click properties ER Diagram Editor will open up. Change the Theme on the ‘General’ tab) Figure 2 – Selecting a Theme Bringing in the tables 1. Select the tables and relationships (if applicable) from the source model file and paste them in the target model while both models are in Physical mode. Figure 3 – Importing/copying the tables 2. Verify that all the tables you need are copied in the Erwin target model. 3. You can close the source model at this stage (recommended) Assigning appropriate Domains 1. Right click on the table and select ‘column properties’. 2. Assign the correct domain parent to all the columns. Figure 4 Assigning Domains to the columns 3. Once you have assigned the...
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...introduction to data modeling 3.1 Introduction: The importance of conceptual models same: understand the problem before you start constructing a solution. There are two important things to keep in mind when learning about and doing data modeling: 1. Data modeling is first and foremost a tool for communication.Their is no single “right” model. Instead, a valuable model highlights tricky issues, allows users, designers, and implementors to discuss the issues using the same vocabulary, and leads to better design decisions. 2. The modeling process is inherently iterative: you create a model, check its assumptions with users, make the necessary changes, and repeat the cycle until you are sure you understand the critical issues. In this background lesson, you are going to use a data modeling technique—specifically, EntityRelationship Diagrams (ERDs)—to model the business scenario from Lesson 2. The data model you create in this lesson will form the foundation of the database that you use throughout the remaining lessons. Before you sit down in front of the keyboard and start creating a database application, it is critical that you take a step back and consider your business problem—in this case, the kitchen supply scenario presented in Lesson 2— from a conceptual point of view. To facilitate this process, a number of conceptual modeling techniques have been developed by computer scientists, psychologists, and consultants. ? For our purposes, we can think of a conceptual model as a picture...
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...Version 7.0 August 10, 2011 INTRODUCTION This is a compilation of data lifecycle models and concepts assembled in part to fulfill Committee on Earth Observation Satellites (CEOS) Working Group on Information Systems and Services (WGISS) and the U.S. Geological Survey (USGS) Community for Data Integration Data Management Best Practices needs. It is intended to be a living document, which will evolve as new information is discovered. CONTENTS 1. Digital Curation Centre (DCC) Lifecycle Model 2. Ellyn Montgomery, USGS, Data Lifecycle Diagram 3. FGDC Stages of the Geospatial Data Lifecycle pursuant to OMB Circular A–16 4. University of Oxford Research Data Management Chart 5. NOAA Environmental Data Life Cycle Functions 6. Open Archival Information System (OAIS) Framework 7. USGS Scientific Information Management Workshop Vocabulary 8. Peter Fox Lifecycle Diagrams 9. National Science Foundation 10. NDIIPP Preserving Our Digital Heritage 11. What Researchers Want 12. EPA Project Life Cycle 13. IWGDD’s Digital Data Life Cycle Model 14. Scientific Data Management Plan Guidance 15. Linear Data Life Cycle 16. Generic Science Data Lifecycle 17. Cassandra Ladino Hybrid Data Lifecycle Model 18. Ray Obuch Data Management – A Lifecycle Approach 19. USGS Data Management Plan Framework (DMPf) – Smith, Tessler, and McHale 20. BLM Data Management Handbook 21. ARL Joint Task Force on Library Support for...
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...Quality Function Development 10 3.4.1 Normal Requirements 11 3.4.2 Expected Requirements 11 3.5. Case Scenario 12 3.5.1. Registering Online 12 3.5.2. Admission 12 3.5.3 Student database generation 13 3.5.4 Attendance sheet generation 13 3.5.5 Result sheet generation 13 3.5.6 Annual report generation 13 Chapter 4 14 4.1 What is usecase diagram? 14 4.2 Usecase Diagram 15 4.3 Activity Diagram and Swimlane Diagram 31 4.3.1 Activity Diagram 31 4.3.2 Swimlane Diagram 39 Chapter 5 47 5.1 Data Modeling Concept 47 5.3 Data Relationship Diagram 51 5.4Entity Relationship(ER) Diagram 52 5.4 Table Schema Diagram 54 Chapter 6 56 6.1 Class Based Modeling Concept 56 6.2 Identifying Analysis Classes 56 6.3 Class Schema Diagram 58 6.4 Class Responsibility Collaboration (CRC) 59 6.5 Class Card 60 Chapter 7 62 7.1 Introduction 62 7.2 Data Flow Design (DFD) 62...
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...Karakkunnel, Valavoor.P.O Pala, Kottayam (D) Pin: 686635 E-mail id - abinaramachandran@gmail.com ABINA.K.R Ph.no: 9447238171 Career Objectives Seeking a challenging job in HR department, that offers opportunity to use my people skills and apply innovative ideas for achieving results. Academic Qualifications * MBA in international Business management (2011-2012). (Birmingham Graduate School, affiliated to University of Wales) * Graduation in BA English Literature (2008-2011). (St. Stephens College Uzhavoor, affiliated to MJ University) Personal Skills and Abilities * Positive thinker. * Ability to take challenges.. * Confident person. * I have good communication skills. * Trustworthy. * Good problem solving skill. * I have the ability to build relationship easily...
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...DRAMATURGICAL MODEL OF THE PRODUCTION OF PERFORMANCE DATA System informasi mengandung berbagai macam informasi yang sangat berguna dalam menjalankan suatu bisnis, baik untuk mengetahui suatu keadaan atau dalam hal pengambilan keputusan. Berdasarkan artikel ini, penelitian menunjukkan bahwa para pemimpin menggunakan system informasi untuk mengetahui keadaan pasar dan menentukan strategi pasar yang sebaiknya digunakan. Artikel juga menyebutkan bahwa manajer menggunakan system informasi untuk menyediakan laporan secara rinci mengenai kinerja karyawan kepada para pemimpin, selain itu para pemimpin juga menggunakannya untuk mengetahui keadaan perusahaan melalui laporan akuntansi dan juga untuk melihat kinerja para manajer yang berpotensi kelak. Jadi dengan kata lain, para manajer berkeinginan untuk memperlihatkan yang terbaik yang mereka mampu kepada para pemimpin tetapi para manajer harus membuktikan kembali bahwa mereka berpotensi namun keadaan ini membuat kekhawatiran di antara para manajer. Inilah yang kemudian disebut sebagai model dramaturgi dari produksi data kinerja yang nantinya para manajer gunakan untuk mengesankan para pemimpin terhadap kinerja manajer. Dari artikel ini dapat disimpulkan bahwa system informasi digunakan oleh para pemimpin untuk mengetahui keadaan perusahaan dan untuk memberikan penilaian terhadap kinerja karyawannya, termasuk pada manajer. Sedangkan di lain sisi, para manajer menggunakan system informasi untuk memanipulasi data dengan...
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...What are metadata? 11. Simply put, metadata is data about data. It is descriptive information about a particular data set, object, or resource, including how it is formatted, and when and by whom it was collected. Explain why database design is important. 12. Database Design is the database structure that will be used as plan to store and manage data. If one were to have a database that did not have a structure to it, it would convelude data access, security access, and possibly other services involved with the database. It avoids redundant data. What are the potential costs of implementing a database system? 13. Who will implement it? Will the work be contracted out or will you employ people to do the work. Hardware: You need something for it to live on. This could either be bought, or hired. Maintenance: The system will at some point require updating - costs of this will need to be considered. There will also be essential maintenance from time to time although this may be covered under your contract if you get an external company to do it for you. If you own the hardware then you will also have to consider staffing to do the maintenance part - does your team have the skills or do you need another. Software: Lots of free software lots of expensive software - what are you going to use and whats best for the job. Location: If you own everything where will it be physically located? Do you need a db on each continent or is one in your office...
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...Big Data and NoSQL Abstract The combination between Big Data and NoSQL is one made of inevitability. As data grows larger and larger, the weaknesses in the relational data model are exacerbated. NoSQL technologies grew out of the need for fast query speed and real-time analytics from data sources too large for traditional SQL. Introduction A web site running with a large number of users/members will experience the dreaded Big Data Performance Inconsistency. When you need the web site to respond more quickly to a successful, it slackens. Sites like Facebook, Twitter, and others have struggled with this problem for years as they’ve grown from thousands to millions and now hundreds of millions of users. Inundated by huge amounts of user data, they took advantage of data store technologies like Memcached and Redis to make their sites run fast. But for sites without the engineering resources of companies like Facebook, adopting these technologies has been challenging. Big data and NoSQL Big Data company for example Garantia Data addresses above issue. Garantia Data’s cloud-based, in-memory NoSQL solutions make web site run faster. That’s why a number of companies are beta testing Garantia Data’s offering. NoSQL is often used for storing Big Data. This is a new type of database which is becoming more and more popular among web companies today. Proponents of NoSQL solutions state that they provide simpler scalability and improved performance relative to traditional relational...
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...The Trusted Information Payoff: Productivity, Performance, and Profits Building an information framework to ensure effective data management produces information that is true, has integrity, and can be trusted. This leads to a continuous improvement culture that can increase employee productivity, improve operational performance, and grow profitability. Karim N. Sidi and Dale A. Hutchinson L arge organizations, especially those that have grown through consolidation, mergers, and acquisitions, are often fraught with incompatible systems and data sources that are costly and difficult to manage. The systems usually do not avail efficient extraction, aggregation, and sharing of data within or across the boundaries of the business process. To address this problem, organizations can turn to an information management framework that facilitates managing raw data to create useful information that can be shared across the organization. SEPTEMBER/OCTOBER 2013 INFORMATIONMANAGEMENT 35 sist of a mix of home-grown, functionspecific applications and third-party systems built by disconnected teams without a shared reference for data definition. The solution – forethought and planning to create well-defined data standards – may appear obvious from an architectural perspective but may not be so easy to accomplish. The steps described below will help. Establish Processes, Rules, Policies Identifying the “truth” first and foremost requires that business processes, rules, and policies...
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...Big Data [Name of Writer] [Name of Institution] Introduction The term Big Data is gaining more followers and popularity. However, despite this trend, not all organizations are clear about how to face the challenge to store, organize, display and analyze large volumes of data. The term Big Data is gaining more followers and popularity. However, despite this trend so evident, not all organizations are clear about how to face the challenge to store, organize, display and analyze large volumes of data. There are multiple techniques in terms of huge database storing approaches that can store petabytes, exabytes and may be zetabytes data. These options are Cassendara, Mongodb and HBase. We will discuss about them one by one and in a proper research method and will compare them in order to contrast their difference and efficiency. Research Background One problem in understanding the phenomenon is that the size of these data sets the volume greatly exceeds the Data warehouse. A plane collects 10 terabytes of information from sensors every 30 minutes flight, while the Stock Exchange of New York collects structured information 1 TB per day. In the context of Big Data, volumes are reaching peta bytes, exa bytes and then soon to zeta bytes. For instance, Apple has just announced that 7 trillion send daily notifications to iOS devices. The explosion of information in social networks, blogs, and emails is characterized the presence of data key...
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...Processing is a powerful framework for the analysis of organizational data. OLAP is often supported by a logical structure known as a data cube, a multidimen- sional data model that offers an intuitive array-based per- spective of the underlying data. Supporting efficient index- ing facilities for multi-dimensional cube queries is an issue of some complexity. In practice, the difficulty of the in- dexing problem is exacerbated by the existence of attribute hierarchies that sub-divide attributes into aggregation layers of varying granularity. In this paper, we present a hierar- chy and caching framework that supports the efficient and transparent manipulation of attribute hierarchies within a parallel ROLAP environment. Experimental results verify that, when compared to the non-hierarchical case, very little overhead is required to handle streams of arbitrary hierar- chical queries. Categories and Subject Descriptors H.2.7.b [Database Management]: Data Warehouse and Repository; H.2.2.a [DatabaseManagement]: AccessMeth- ods General Terms Algorithms Design Performance Keywords Hierarchies, Caching, Data Cubes, Aggregation, Indexing, OLAP, Granularity, Materialization, Parallelization 1. INTRODUCTION Online Analytical Processing (OLAP) has become an im- portant component of contemporary Decision Support Sys- tems (DSS). Central to OLAP is the data cube, a multidi- mensional data model that presents an intuitive cube-like Permission to make digital or hard...
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...SECTION- A PART ONE:- 1) b. Information 2) b. Relevance 3) a. Produce pay checks and payrolls reports 4) c. Development of financial budgets and projected financial statements 5) a. Executive Information System 6) b. Who are members of the same company or organization 7) d. Telegram 8) a. Online Transactional Processing 9) b. Localization 10) b. Activity control PART TWO:- 1) The concept of strategic information systems (SIS) was first introduced into the field of information systems in 1982-83 by Dr. Charles Wiseman, President of a newly formed consultancy called "Competitive Applications," (cf. NY State records for consultancies formed in 1982) who gave a series of public lectures on SIS in NYC sponsored by the Datamation Institute, a subsidiary of Datamation Magazine. In 1985 Wiseman published an article on this subject (co-authored by Prof. Ian MacMillan) in the Journal of Business Strategy (Journal of Business Strategy, fall, 1984) In 1985 he published the first book on SIS called "Strategy and Computers: Information Systems as Competitive Weapons" (Dow-Jones Irwin, 1985; translated into French by Bertrand Kaulek and into Italian by Professor Fabio Corno of Bocconi University). In 1988 an expanded version of this book called "Strategic Information Systems" was published by Richard D. Irwin. This book was translated into Japanese byProfessor Shinroki Tsuji and published by Diamond Publishing. Over 50,000 copies have been sold...
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...AIT632 –SPRING 2015 Assignment 1 Entity Relationship Diagram Due is 02/26 /2015 by 4:30 PM Student Name: Ujwal Rai Submitted to: Dr. Jinie Pak Instruction: Please use a drawing tool for data modeling (Q2 and Q3). After completing ERDs, save them as images and paste to MS Word doc.(maybe use this file). The ERDs should be clear enough to understand the context. For submission, use MS Word doc. (e.g. AIT632_A1_Name.docx). You can use any CASE tool to draw the ERDs, but when you save them, use image file extensions ( JPEG, GIFF , etc) or PDF file extension. 1. Given the conceptual model in the Figure: Write the business rules that are reflected in it. Identify all of the cardinalities. Research Instructor 0..* Assigned 1..1 Advanced Course 1..1 Teaches 1..* Training Session 1..* Provides 1..1 TeachingTeam 1..1 IsPartOf 0..2 Trainee 1..* Attends 1..* Research Instructor 0..* Assigned 1..1 Advanced Course 1..1 Teaches 1..* Training Session 1..* Provides 1..1 TeachingTeam 1..1 IsPartOf 0..2 Trainee 1..* Attends 1..* 1. An instructor is assigned zero or more Researches, and a Research is assigned to one and only one instructor. 2. An instructor is part of zero or two teaching teams, and a teaching team consists of one and only one instructor. 3. A teaching team teaches one or many advanced courses, and an advanced course is taught by one and only one teaching...
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...MILESTONE 4 – DATA MODELING Synopsis D ata modeling is a technique for organizing and documenting a system’s data. Data is viewed as a resource to be shared by as many processes as possible. As a result, data must be organized in a way that is flexible and adaptable to unanticipated business requirements – and that is the purpose of data modeling. In this milestone you will first discover those entities in the system that are or might be described by data. With each entity we identify, we will define it in respect to the business. Then, we will construct a Context Data Model that graphically depicts each of the entities and the relationships they have with each other. Next, we will refine the context data model to include primary and foreign keys. The resulting model is called a Key-Based Data Model. Finally, we refine the key-based data model to include any hierarchies and attributes, and this model is referred to as the Fully Attributed Data Model. Objectives After completing this milestone, you should be able to: Understand and perform the techniques for entity discovery. Define each entity with respect to the business and complete an entity/definition matrix. Perform the necessary data modeling techniques to organize and document the data requirements for the proposed system. Construct the Context, Key-Based, and Fully Attributed data models. Prerequisites Before starting this milestone the following topics should be covered: 1. Data modeling...
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...Incorrect .1) | Which of the following is NOT a primary function performed by a telecommunications network? | | | | A. | transmission of voice | | B. | network control | | C. | tracking of out-of-service devices (Your Answer) | | D. | transmission of data | | E. | All of the above are primary functions of telecommunication (Correct Answer) | Incorrect | | | Q.2) | Which of the following most accurately describes the INTERFACE function of telecommunications? | | | | A. | checking for errors and putting the communicaiton into a standardized format | | B. | handing interactions between users and the network (Correct Answer) | | C. | keeping track of the status of the network (Your Answer) | | D. | choosing the most efficient path for a message to be sent over the Internet | | E. | changing coding system or speed when moving data between devices on the network | Incorrect | | | Q.3) | Which of the following is NOT a characterisitic of fiber-optic transmission? | | | | A. | faster transmission than twisted pair of wires | | B. | more secure than other media because it does not emit radiation | | C. | requires much less space because the fiber-optic cable is very small in diameter (Your Answer) | | D. | easy to work with the tiny fiber and require much cheaper equipment (Correct Answer) | | E. | not affected by power-line surges or electromagnetic...
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