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Front cover IBM TotalStorage Enterprise Storage Server Model 800 Learn about the characteristics of the new ESS Model 800 Know the powerful functions available in the ESS Discover the advanced architecture of the ESS Gustavo Castets Peter Crowhurst Stephen Garraway Guenter Rebmann ibm.com/redbooks International Technical Support Organization IBM TotalStorage Enterprise Storage Server Model 800 October 2002 SG24-6424-01 Note: Before using this information and the product it supports, read the information in “Notices” on page xvii. Second Edition (October 2002) This edition applies to the IBM TotalStorage Enterprise Storage Server Model 800 (ESS Model 800) — IBM 2105-800. © Copyright International Business Machines Corporation 2002. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.
Oracle NoSQL Database (ONDB) provides network-accessible multi-terabyte distributed key/value pair storage with predictable latency. Data is stored in a very flexible key-value format, where the key consists of the combination of a major and minor key (represented as a string) and an associated value (represented as a JSON data format or opaque set of bytes). It offers full Create, Read, Update and Delete (CRUD) operations, with adjustable durability and consistency guarantees. It also provides powerful and flexible transactional model that eases the application development. Oracle NoSQL Database is designed to be a highly available and extremely scalable system, with predictable levels of throughput and latency, while requiring minimal administrative interaction. Oracle NoSQL Database is built upon the proven Oracle Berkeley DB Java Edition high-availability storage engine, which is in widespread use in enterprises across industries. In addition to that it adds a layer of services for use in distributed environments. The resulting solution provides distributed, highly available key/value storage that is well suited to large-volume, latency-sensitive applications. High Availability and No-Single Point of Failure Oracle NoSQL Database provides single-master, multi-replica database replication. Transactional data is delivered to all replica nodes with flexible durability policies per transaction. In the event the master replica node fails, a PAXOS-based automated fail-over election process minimizes downtime. This allows for scalability, fail-over, and hot-standby.
Why NoSQL? Three trends disrupting the database status quo Interactive applications have changed dramatically over the last 15 years. In the late ‘90s, large web companies emerged with dramatic increases in scale on many dimensions: • The number of concurrent users skyrocketed as applications increasingly became accessible via the web (and later on mobile devices). • The amount of data collected and processed soared as it became easier and increasingly valuable to capture all kinds of data. • The amount of unstructured or semi-structured data exploded and its use became integral to the value and richness of applications. Dealing with these issues was more and more difficult using relational database technology. The key reason is that relational databases are essentially architected to run a single machine and use a rigid, schema-based approach to modeling data. Google, Amazon, Facebook, and LinkedIn were among the first companies to discover the serious limitations of relational database technology for supporting these new application requirements. Commercial alternatives didn’t exist, so they invented new data management approaches themselves. Their pioneering work generated tremendous interest because a growing number of companies faced similar problems. Open source NoSQL database projects formed to leverage the work of the pioneers, and commercial companies associated with these projects soon followed. Today, the use of NoSQL technology is rising rapidly among Internet companies and the enterprise. It’s increasingly considered a viable alternative to relational databases, especially as more organizations recognize that operating at scale is more effectively achieved running on clusters of standard, commodity servers, and a schema-less data model is often a better approach for handling the variety and type of data most often captured and processed today.
Interactive software (software with which a person iteratively interacts in real time) has changed in fundamental ways over the last 35 years. The “online” systems of the 1970s have, through a series of intermediate transformations, evolved into today’s web and mobile applications. These systems solve new problems for potentially vastly larger user populations, and they execute atop a computing infrastructure that has changed even more radically over the years. The architecture of these software systems has likewise transformed. A modern web application can support millions of concurrent users by spreading load across a collection of application servers behind a load balancer. Changes in application behavior can be rolled out incrementally without requiring application downtime by gradually replacing the software on individual servers. Adjustments to application capacity are easily made by changing the number of application servers. But database technology has not kept pace. Relational database technology, invented in the 1970s and still in widespread use today, was optimized for the applications, users and infrastructure of that era. In some regards, it is the last domino to fall in the inevitable march toward a fully-distributed software architecture. While a number of band aids have extended the useful life of the technology (horizontal and vertical sharding, distributed caching and data denormalization), these tactics nullify key benefits of the relational model while increasing total system cost and complexity. In response to the lack of commercially available alternatives, organizations such as Google and Amazon were, out of necessity, forced to invent new approaches to data management. These “NoSQL” or non-relational database technologies are a better match for the needs of modern interactive software systems. But not every company can or should develop, maintain and support its own database technology. Building upon the pioneering research at these and other leading-edge organizations, commercial suppliers of NoSQL database technology have emerged to offer database technology purpose-built to enable the cost-effective management of data behind modern web and mobile applications.
Relational database management systems (RDMBSs) today are the predominant technology for storing structured data in web and business applications. Since Codds paper “A relational model of data for large shared data banks“ [Cod70] from 1970 these datastores relying on the relational calculus and providing comprehensive ad hoc querying facilities by SQL (cf. [CB74]) have been widely adopted and are often thought of as the only alternative for data storage accessible by multiple clients in a consistent way. Although there have been diﬀerent approaches over the years such as object databases or XML stores these technologies have never gained the same adoption and market share as RDBMSs. Rather, these alternatives have either been absorbed by relational database management systems that e. g. allow to store XML and use it for purposes like text indexing or they have become niche products for e. g. OLAP or stream processing. In the past few years, the ”one size ﬁts all“-thinking concerning datastores has been questioned by both, science and web aﬃne companies, which has lead to the emergence of a great variety of alternative databases. The movement as well as the new datastores are commonly subsumed under the term NoSQL, “used to describe the increasing usage of non-relational databases among Web developers” (cf. [Oba09a]). This paper’s aims at giving a systematic overview of the motives and rationales directing this movement (chapter 2), common concepts, techniques and patterns (chapter 3) as well as several classes of NoSQL databases (key-/value-stores, document databases, column-oriented databases) and individual products...
T he JFMIP System Requirements for Managerial Cost Accounting document is one of a series of JFMIP publications on federal financial management system requirements. All of these documents should be considered together when determining how best to use information technology and supporting services to meet a federal agency’s financial management needs. The Framework for Federal Financial Management Systems describes the basic elements of a model for integrated financial management systems, the relationships between the model elements, and specific considerations in developing and implementing integrated financial management systems. Each of the other documents in the series, beginning with Core Financial System Requirements, describes the functional requirements for a particular type of system. This particular document is called System Requirements for Managerial Cost Accounting, rather than Managerial Cost Accounting System Requirements, because cost accounting functions may be supported by many types of systems, such as the core financial system, inventory and fixed asset systems, programmatic systems, and others, in addition to systems dedicated to cost accounting. This System Requirements for Managerial Cost Accounting document is intended for systems analysts, systems accountants, and systems developers as well as program managers and other users who are defining requirements that software supporting managerial cost accounting functions in their organizations must meet. This document builds upon, and provides a means to implement, requirements related to cost accounting set forth in the Chief Financial Officers Act (CFO Act), Government Performance and Results Act (GPRA), Statements of Federal Financial Accounting Standards (SFFAS), Office of Management and Budget (OMB) circulars, and other sources. It accomplishes this by specifying information and functional processing requirements for accumulating and analyzing cost data consistent with governmentwide guidance. Laws and policies affecting systems for managerial cost accounting are identified in Appendix A. Glossaries of terms relating to managerial cost accounting may be found in SFFAS Number 4, Managerial Cost Accounting Concepts and Standards for the Federal Government, and the Managerial Cost Accounting Implementation Guide prepared by the Governmentwide Cost Accounting Committee of the Chief Financial Officers Council. As shown in Illustration 1, standards and system requirements assist agencies in selecting effective and efficient systems. The requirements in this document are intended to facilitate the acquisition, development, and enhancement of systems that provide information useful in managing and controlling the cost of government. The document establishes the standard, governmentwide system requirements that an agency should consider for systems supporting managerial cost accounting functions, but also allows flexibility to address agency-specific requirements, such as those associated with the choice of costing methodology (e.g., activity-based costing).
Course Description: This course is designed for advanced undergraduate students at chemistry department of Florida International University. Our aim is to provide the basic training in biochemical laboratory for our students. Textbook: Fundamental Laboratory Approaches for Biochemistry and Biotechnology by Alexander J. Ninfa and David P. Ballou. Grading: Your grade will depend on your experimental results, your lab reports, and your performance in each class. Your preparation of experiments, understanding of each experiment, and answers to the instructor’s questions in the class will also contribute to your final grade. SUMMARY OF TEST PRINCIPLE AND CLINICAL RELEVANCE The 22 analytes described in this method constitute the routine biochemistry profile. The analyses are performed with a Hitachi Model 917 multichannel analyzer (Roche Diagnostics, Indianapolis, IN). Each analyte is described separately within each pertinent section of this document. NOTE: Glucose, cholesterol, and triglycerides were analyzed as part of this profile, but the results do not replace the formalized reference methods data from NHANES 1999–2000 samples analyzed at other institutions. Alanine Aminotransferase (ALT) α-Ketoglutarate reacts with L-alanine in the presence of ALT to form L-glutamate plus pyruvate. The pyruvate is used in the indicator reaction for a kinetic determination of the reduced form of nicotinamide adenine dinucleotide (NADH) consumption. The International Federation of Clinical Chemistry (IFCC) has now recommended standardized procedures for ALT determination, including 1) optimization of substrate concentrations, 2) the use of Tris buffers, 3) preincubation of a combined buffer and serum solution to allow side reactions with NADH to occur, 4) substrate start (αketoglutarate), and 5) optimal pyridoxal phosphate activation. As a group, the transaminases catalyze the interconversion of amino acids and α-keto acids by transferring the amino groups. The enzyme ALT been found to be in highest concentration in the liver, with decreasing concentrations found in kidney, heart, skeletal muscle, pancreas, spleen, and lung tissue. Alanine aminotransferase measurements are used in the diagnosis and treatment of certain liver diseases (e.g., viral hepatitis and cirrhosis) and heart diseases. Elevated levels of the transaminases can indicate myocardial infarction, hepatic disease, muscular dystrophy, or organ damage. Serum elevations of ALT activity are rarely observed except in parenchymal liver disease, since ALT is a more liver-specific enzyme than asparate aminotransferase (AST) (1).
WELCOME TO THE BIOCHEMISTRY LABORATORY! This Biochemistry laboratory seeks to model work performed in a biochemical research laboratory. The course will guide you through basic lab techniques, investigations into DNA and enzyme kinetics, an intensive purification and characterization of an unreported protein, and will culminate in a formal research paper in the format of an article published in Biochemistry. Module 1 is concerned with basic lab skills. In these labs, we will learn how scientists think and write about biochemistry and perform experiments. We will also learn to accurately and precisely measure small volume of liquid while avoiding sample contamination. Lastly, we will learn to compute and create buffer solutions—a cornerstone of biochemistry. Module 2 will allow us to purify the protein cytochrome c from a yeast species (Saccharomyces cerevisiae) using various fractionation techniques including homogenization, centrifugation, and column chromatography. We will characterize our products using biochemical methods including gel electrophoresis, UV-Vis spectroscopy, and electrochemistry. Using modeling software on the computer, the structure and function of model, comparison cytochrome c proteins will be investigated. As a result of this project, we will determine the molecular weight, the approximate number and type of aromatic residues, characteristic UV-Vis spectra, and denaturation/renaturation properties of cyctochrome c. Module 3 looks into the processes used to isolate, purify, amplify, and characterize DNA. We will isolate and purify DNA from a bacterial source, and design and then use then use the polymerase chain reaction (PCR) to amplify a DNA region of interest to ascertain the nature of the DNA we purified. Finally, we will perform in silico studies of DNA cloning, followed by DNA restriction and ligation for transformation into a bacterial expression system—molecular cloning. Module 4 is focused on enzyme kinetics, the measurement of the extent and mechanism by which enzymes catalyze biological reactions. We will investigate these processes by looking at the activity of tyrosinase found in mushrooms, which catalyze oxidation of various substrates. We will also investigate the effect of enzyme inhibitors of these reactions. The emphasis of the lab is on learning to perform complex biochemical techniques, as well as on analyzing and interpreting data and using graphing programs. Lab instructions and report expectations are explained in the pages that follow.
Contents About iOS App Programming 8 At a Glance 8 Translate Your Initial Idea into an Implementation Plan 9 UIKit Provides the Core of Your App 9 Apps Must Behave Differently in the Foreground and Background 9 iCloud Affects the Design of Your Data Model and UI Layers 9 Apps Require Some Specific Resources 10 Apps Should Restore Their Previous UI State at Launch Time 10 Many App Behaviors Can Be Customized 10 Apps Must Be Tuned for Performance 10 The iOS Environment Affects Many App Behaviors 11 How to Use This Document 11 Prerequisites 11 See Also 11 App Design Basics 13 Doing Your Initial Design 13 Learning the Fundamental iOS Design Patterns and Techniques 14 Translating Your Initial Design into an Action Plan 14 Starting the App Creation Process 15 Best Practices for Maintaining User Privacy 18 Core App Objects 21 The Core Objects of Your App 21 The Data Model 24 Defining a Custom Data Model 25 Defining a Structured Data Model Using Core Data 28 Defining a Document-Based Data Model 28 Integrating iCloud Support Into Your App 30 The User Interface 30 Building an Interface Using UIKit Views 31 Building an Interface Using Views and OpenGL ES 33 The App Bundle 34 2013-10-23 | Copyright © 2013 Apple Inc. All Rights Reserved.
Contents About Windows and Views 7 At a Glance 7 Views Manage Your Application’s Visual Content 7 Windows Coordinate the Display of Your Views 8 Animations Provide the User with Visible Feedback for Interface Changes 8 The Role of Interface Builder 8 See Also 9 View and Window Architecture 10 View Architecture Fundamentals 10 View Hierarchies and Subview Management 11 The View Drawing Cycle 12 Content Modes 13 Stretchable Views 15 Built-In Animation Support 16 View Geometry and Coordinate Systems 17 The Relationship of the Frame, Bounds, and Center Properties 18 Coordinate System Transformations 20 Points Versus Pixels 21 The Runtime Interaction Model for Views 23 Tips for Using Views Effectively 25 Views Do Not Always Have a Corresponding View Controller 25 Minimize Custom Drawing 26 Take Advantage of Content Modes 26 Declare Views as Opaque Whenever Possible 26 Adjust Your View’s Drawing Behavior When Scrolling 26 Do Not Customize Controls by Embedding Subviews 27 Windows 28 Tasks That Involve Windows 28 Creating and Configuring a Window 29 Creating Windows in Interface Builder 29 Creating a Window Programmatically 30 Adding Content to Your Window 30 Changing the Window Level 31 Monitoring Window Changes 31 Displaying Content on an External Display 32 Handling Screen Connection and Disconnection Notifications 33 Configuring a Window for an External Display 35 Configuring the Screen Mode of an External Display 37 2013-10-22 | Copyright © 2013 Apple Inc. All Rights Reserved.