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Emergency Department Visits for Chest Pain and Abdominal Pain

Emergency Department Visits for Chest Pain and Abdominal Pain: United States, 1999–2008 Farida A. Bhuiya, M.P.H.; Stephen R. Pitts, M.D., M.P.H., F.A.C.E.P.; and Linda F. McCaig, M.P.H., Division of Health Care Statistics Key findings Data from the National Hospital Ambulatory Medical Care Survey: 1999–2008 • The number of noninjury emergency department (ED) visits in which abdominal pain was the primary reason for the visit increased 31.8%. • The percentage of ED visits for which chest pain was the primary reason decreased 10.0%. • Use of advanced medical imaging increased strongly for ED visits related to abdominal pain (122.6%) and chest pain (367.6%). Chest and abdominal pain are the most common reasons that persons aged 15 years and over visit the emergency department (ED) (1). Because EDs provide both emergency and nonemergency care (2,3), visits for these symptoms may vary in their acuity. Advanced medical imaging is often ordered to assist in both diagnosing and ruling out serious illness associated with these symptoms (4,5). This report describes trends in visits for chest and abdominal pain in adults and the seriousness of illness and use of imaging in these visits. All data shown are for persons aged 18 and over whose visit was not injury related. Keywords: National Hospital Ambulatory Medical Care Survey • advanced medical imaging • reason for visit Are ED visits for chest or abdominal pain increasing?

packaging design as resource for the construction of brand identity

Copyright © 2006 Ulrich R. Orth and Keven Malkewitz All rights reserved Ulrich R. Orth, Prof. Dr. habil. (primary contact) Agribusiness & Food Marketing Professor College of Business Oregon State University Bexell Hall 330, Corvallis, OR 97331-2603 Phone: (503) 678 1264, x44 Fax: (503) 678 5986 E-mail: Keven Malkewitz, PhD Assistant Professor of Marketing College of Business Oregon State University 410 Bexell Hall, Corvallis, OR 97331-2603 Phone: (541) 737 3688 E-mail: The authors wish to thank Andrea Marks, Jay Thompson, and Joseph Cote for their comments during this research, Cindy Lederer for providing access to the Oregon Consumer Panel and numerous professional designers for their input. Financial support and assistance in collecting the data was provided in part by Willamette Valley Vineyards, particularly Jim Bernau, Shelby Zadow, and Jon Mason. Please direct all inquiries to the first author.

Packaging design as a Marketing tool and Desire to ... - Theseus

Ksenia Polyakova Packaging design as a Marketing tool and Desire to purchase, 72 pages, 2 appendices Saimaa University of Applied Science Faculty of Business Administration, Lappeenranta Degree Programme in International Business Bachelor’s Thesis 2013 Instructor: Mr. Riku Hytönen Senior Lecturer, Saimaa University of Applied Sciences The purpose of the study was to examine the consumer perception on different design elements of a milk package and to provide essential information for the companies about the consumer attraction and importance of design attributes from the consumer point of view. The theoretical framework was based on the secondary data (articles and books) and included core concepts of packaging, packaging design, consumer behavior, consumer perception, and consumer attraction. The mixed method was selected for acquiring and analyzing the research results. Quantitative data was collected from 30 questionnaire responses and was analyzed with the computer program Excel. Qualitative data was obtained from two interviews conducted with the companies, Valio Ltd and Tetra Pak Ltd. The results of the study revealed the importance of packaging design in consumer buying behavior. By examining the consumer perception, it was found out that packaging design elements such as graphics, color, and product information play a key role in decision making and ensure consumer’s attention. Based on the findings, it was defined that successful milk packaging design could be created by the cooperation between the consumer and the company. Further research could investigate other product packages’ design elements.

Scalable SQL and NoSQL Data Stores - Rick Cattell Home Page

In this paper, we examine a number of SQL and socalled “NoSQL” data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability. Note: Bibliographic references for systems are not listed, but URLs for more information can be found in the System References table at the end of this paper. Caveat: Statements in this paper are based on sources and documentation that may not be reliable, and the systems described are “moving targets,” so some statements may be incorrect. Verify through other sources before depending on information here. Nevertheless, we hope this comprehensive survey is useful! Check for future corrections on the author’s web site Disclosure: The author is on the technical advisory board of Schooner Technologies and has a consulting business advising on scalable databases.

Ultra-High Performance NoSQL Benchmarking - Aerospike

As companies deal with ever larger amounts of data and increasingly demanding workloads, a new class of databases has taken hold. Dubbed “NoSQL”, these databases trade some of the features used by traditional relational databases in exchange for increased performance and/or partition tolerance. But as NoSQL solutions have proliferated and differentiated themselves (into key-value stores, document databases, graph databases, and “NewSQL”), trying to evaluate the database landscape for a particular class of problem becomes more and more difficult. In this paper we attempt to answer this question for one specific, but critical, class of functionality – applications that need the highest possible raw performance for a reliable storage engine. There have been a few attempts to provide standardized tools to measure performance or other characteristics, but these have been hobbled by the lack of a clear mandate on exactly what they’re testing, plus an inability to measure load at the highest volumes. In addition, there is an implicit tradeoff between the consistency and durability requirements of an application and the maximum throughput that can be processed. What is needed is not an attempt to quantify every NoSQL solution into one artificial bucket, but a more systemic analysis of how some of these databases can achieve under assumptions that mirror real-world application needs. We attempted to provide a comprehensive answer to one specific set of use cases for NoSQL databases -- consumer-facing applications which require extremely high throughput and low latency, and whose information can be represented using a key-value schema. In particular, we look at two common scenarios.

An Introduction to Stretchr: The intelligent Datastack impact to nosql ...

CUSTOMER TESTIMONIAL: FRONT PORCH DIGITAL’S RAPID APPLICATION DEVELOPMENT Front Porch Digital, Inc. is a world leader in digital asset workflow management serving global leaders in the entertainment industry. Front Porch Digital recently integrated Stretchr into its application development processes. “Stretchr has fundamentally changed the way we approach data systems development. Today’s data comes in so many shapes and sizes and is always changing, requiring you to spend a huge amount of time designing and editing schemas in traditional databases or developing expertise in NoSQL technology. With Stretchr all of that time and complexity goes away. You simply acquire the data, from any source and in any form. Stretchr then organizes the data for you based on how your users consume it – it couldn’t be simpler. Our first integration with Stretchr took an afternoon, and was effectively the insertion of one line of code into our existing application. So happy are we with the way Stretchr works and performs that we are tightly integrating our newest products with Stretchr, cutting development times significantly”.

Solving Data Integration Challenges with SQL and NoSQL - FOSE

Many organizations today would seemingly be content with having achieved an information architecture that features a broad-scope enterprise resource management environment feeding data in batch for reporting and analytics to a robust data warehouse environment. As a bonus, the data warehouses in this post-operational environment may consist of solid-state components and automated archival abilities. Irreversibly, the environment also has been inundated with data marts fed from original source and from the data warehouse itself. There is frequently a multidimensional database in the mix. Or a hundred. If there is any contentment with such an architecture, it will be short-lived. With information the “new gold” for companies, each shop must do everything it can to nurture, protect, make available and otherwise exploit the information asset. This will frequently mean venturing into new technology domains for the management of the asset. One may be tempted to consider the NoSQL movement as the epitome of these new technology domains. However, many possibilities have been laid on the table by the vendor community in the years prior to NoSQL. Most have merit in an enterprise today. We clearly need to get away from the winner-take-all mentality where every workload – sometimes whether it is analytical or operational – will be solved the same way as the last one. Frequently, that way was with a data integration operation with the data warehouse followed by the deployment of more reports in the business intelligence tool. Force-fitting a workload into a technology that it was not designed for creates more problems than it solves.

Static and Dynamic Semantics of NoSQL Languages - PPS

that is deduced directly from the structure of the program even in the absence of any explicit type declaration or annotation. We present a calculus for processing semistructured data that spans differences of application area among several novel query languages, broadly categorized as “NoSQL”. This calculus lets users define their own operators, capturing a wider range of data processing capabilities, whilst providing a typing precision so far typical only of primitive hard-coded operators. The type inference algorithm is based on semantic type checking, resulting in type information that is both precise, and flexible enough to handle structured and semistructured data. We illustrate the use of this calculus by encoding a large fragment of Jaql, including operations and iterators over JSON, embedded SQL expressions, and co-grouping, and show how the encoding directly yields a typing discipline for Jaql as it is, namely without the addition of any type definition or type annotation in the code.

Will NoSQL Databases Live Up to Their Promise? - Leavitt ...

Organizations that collect large amounts of unstructured data are increasingly turning to nonrelational databases, now frequently called NoSQL databases. M any organizations collect vast amounts of customer, scientific, sales, and other data for future analysis. Traditionally, most of these organizations have stored structured data in relational databases for subsequent access and analysis. However, a growing number of developers and users have begun turning to various types of nonrelational—now frequently called NoSQL—databases. Nonrelationa l dat a ba ses— including hierarchical, graph, and object-oriented databases—have been around since the late 1960s. However, new types of NoSQL databases are being developed. And only now are they beginning to gain market traction. Different NoSQL databases take different approaches. What they have in common is that they’re not relational. Their primary advantage is that, unlike relational databases, they handle unstructured data such as word-processing files, e-mail, multimedia, and social media efficiently. They are also easier to work with for the many developers not familiar 12 r2tec.indd 12 computer with the structured query language. SQL is the programming language used for querying and updating relational databases. Some NoSQL databases can function in a distributed setting. Users could thus scale a single database by running it across additional inexpensive machines rather than by having to run it on a single more powerful and costly machine.

PostgreSQL as a Schemaless Database - The Build

PostgreSQL as a Schemaless Database. Christophe Pettus PostgreSQL Experts, Inc. OSCON 2013 Welcome! • I’m Christophe. • PostgreSQL person since 1997. • Consultant with PostgreSQL Experts, Inc. • • • @xof on Twitter. What’s on the menu? • What is a schemaless database? • How can you use PostgreSQL to store schemaless data? • How does do the various schemaless options perform? A note on NoSQL. • Worst. Term. Ever. • It’s true that all modern schemaless databases do not use SQL, but… • Neither did Postgres before it became PostgreSQL. (Remember QUEL?) • The defining characteristic is the lack of a fixed schema. Schematic. • A schema is a fixed (although mutable over time) definition of the data. • Database to schema (unfortunate term) to table to field/column/attribute. • Individual fields can be optional (NULL). • Adding new columns requires a schema change.

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