October 2000
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January 1992Ausstossung und Verfolgung
Eine historische Theorie des Sündenbocks
by Girard, René / Französisch Mainberger-Ruh, Elisabeth
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Trusted Partner
August 2002Ich sah den Satan vom Himmel fallen wie einen Blitz
Eine kritische Apologie des Christentums
by Girard, René / Nachwort von Sloterdijk, Peter; Übersetzt von Mainberger-Ruh, Elisabeth
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Trusted Partner
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Trusted Partner
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Trusted Partner
September 2008Schlaf in tödlicher Ruh
Weihnachtliche Kriminalgeschichten
by Carolin Bunk, Hans Sarkowicz
Weihnachten ist die Zeit des Friedens, der Harmonie und des familiären Glücks. Kann sein, muß aber nicht. Denn das Verbrechen macht nie Pause, auch nicht am Heiligen Abend. Welche beängstigenden Abgründe sich gerade beim Fest der Liebe auftun können, ahnen wir nur. Krimiautoren dagegen machen daraus spannende Geschichten. Dann verwandelt sich die o so fröhliche Weihnachtszeit in einen Albtraum. Also: Vorsicht ist geboten, gerade wenn die Glocken am süßesten klingen und die Engelchöre die „Stille Nacht“ besingen. Mit Texten von Dorothy L. Sayers, Ruth Rendell, Dan Brown, Ingrid Noll, Patricia Highsmith, u. a.
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Trusted Partner
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Trusted Partner
March 2019»Träume deine Träume in Ruh«
Gedichte der Stille
by Clara Paul
Immer mehr Menschen sehnen sich in unserer lärmumtosten Welt nach den stillen Momenten, die ganz ihnen gehören und in denen sie wieder zu sich selbst zurückfinden. Eine schöne Möglichkeit, Stille zu erfahren, ist, Gedichte zu lesen – Gedichte wie diese hier, die mit Ruhe und Gelassenheit erfüllen; Gedichte, die mit großer Suggestivkraft und Intensität das »Herz der Dinge« offenbaren und die Schönheit, die uns umgibt; Gedichte, die ermutigen, die eigenen, »inneren Worte« zu finden, und die uns bestärken, achtsam dem Leben zu begegnen. Mit Gedichten von Rose Ausländer, Gottfried Benn, Paul Celan, Joseph von Eichendorff, Hans Magnus Enzensberger, Eugen Gomringer, Lars Gustafsson, Zbigniew Herbert, Mascha Kaléko, Michael Krüger, Günter Kunert, Christian Lehnert, Czesław Miłosz, Rainer Maria Rilke, Joachim Ringelnatz, Antoine de Saint-Exupéry, Eva Strittmatter, Wisława Szymborska, Georg Trakl, Jan Wagner u.v.a.
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Trusted Partner
January 2020Gute Nacht überall
by Mont, Annelie
Jetzt sagen alle Gute Nacht - in einem traumhaft schönen Pappbilderbuch mit kleinen Reimen und zahlreichen Klappen. Auf dem Bauernhof gehen die Tiere schlafen. Schlaft gut, liebe Tiere! Auf der Baustelle bleiben die Fahrzeuge stehen. Ruh dich aus, kleiner Bagger! Im Wald wird es still, und selbst die Stadt sagt Gute Nacht. Psst, leise, damit niemand aufwacht! Denn auch die Kinder schlafen ein …
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Trusted Partner
September 2014Gute Besserung!
Geschichten zum Gesundwerden
by Matthias Reiner
Ihren Arzt oder Apotheker brauchen Sie nicht zu fragen, wenn Sie dieses Buch im Krankenzimmer zu den Medikamenten und der Schale mit dem Obst legen: Die Dosierung ist nicht festgelegt, die Einnahme muss nicht regelmäßig erfolgen. Nebenwirkungen sind erwünscht, und den Beipackzettel mit den Genesungswünschen können Sie selbst verfassen. Die ausnahmslos natürlichen Heilmittel (Lebenserinnerungen, Gedichte, Geschichten) werden von Axel Hacke, Luis Buñuel, Theodor Fontane, Silvia Bovenschen und vielen anderen verabreicht: Placeboeffekte nicht ausgeschlossen. »Mein Körper rät mir: / Ruh dich aus! / Ich sage: Mach ich, / Altes Haus! / Denk aber: Ach, der / Sieht's ja nicht! / Und schreibe heimlich / Dies Gedicht.«
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Trusted Partner
April 2003Spectaculum 74
Fünf moderne Theaterstücke
by Thomas Bernhard, Tankred Dorst, Martin Heckmanns, Henning Mankell, Einar Schleef, Hansjörg Betschart, Ursula Ehler
Thomas Bernhard: Über allen Gipfeln ist Ruh Der berühmte Dichter Moritz Meister hat sein Opus magnum, seine Tetralogie, abgeschlossen. Mit seiner Frau und seinen Gästen erwartet er nun den Verleger, den bedeutendsten, den besten, den wir haben, der nur die Besten verlegt. Eine Komödie.Tankred Dorst: Kupsch Der beliebte und erfolgreiche Kupsch neigt keineswegs zur Phantasterei und hat sein Leben ganz nach praktischen Gesichtspunkten geordnet. Nun auf einmal glaubt er, daß in ihm noch ein anderer sei.Martin Heckmanns: Schieß doch, Kaufhaus! »Jetzt / Wo alle immer alles hören können / Wo jeder mit jedem reden kann / Könnte da nicht endlich entschieden werden: / Schluss.« Ätz, Fetz, Klar, Kling und Knax, deren Namen gleichzeitig Zustandsbeschreibun gen sind, zappen sich durch ihr Leben, auf der Suche nach Verbindlichkeit, nach einem echten Leben.Henning Mankell: Antilopen Vierzehn Jahre hat der schwedische Entwicklungshelfer Lars mit seiner Frau in der afrikanischen Wildnis gearbeitet, ohne Erfolg. Von ihrer Motivation ist nichts mehr übrig, zynisch sind beide geworden. Am Vorabend ihrer Abreise eskaliert die Situation.Einar Schleef: Gertrud »Meine Kindheit fiel ins Kaiserreich, der Sportplatz in die Weimaraner, die Ehe auf Hitler unds Alter in die DDR. Wohin mein Kopp. Viermal Deutsches Reich, das 5. ist 2 Meter lang. Das l000jährige Gottes erleb ich nimmer.« Einar Schleef hat aus dem Prosatext Gertrud, in dessen Zentrum seine Mutter steht, dieses Stück erarbeitet.
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FictionJanuary 2020Bu şehirde kimse yokmu
Azerbaycanlı yazar, filozof-doğu bilimci, psikolog 26 kitabın yazarı Rövşen Abdullaoğlu'nun ilk romanı.
by Rövşen Abdullaoğlu
Eskiden basarili bir sporcuydu Willy... Simdilerdeyse kösesine çekilmis bir temizlik görevlisi... Ölümcül bir hastalikla mücadele etmekte olan Lübnanli bir göçmenle tanisir çalistigi yerde... Günden güne ölüme bir adim daha yaklasan Wisman'in her seye ragmen yasama simsiki ve sevgiyle tutunmayi basarabiliyor olmasi, etrafindaki herkesi, hayati ve ölümü yeniden sorgulamaya iter. Willy ve Wisman arasindaki arkadaslik, insanin anlam arayisina yepyeni bir pencere açacaktir. Azerbaycan'da iki yil boyunca çok satanlar listesinde yer bulanBu Sehirde Kimse Yok mu? umuda, hayata ve anlama dair nahif ama güçlü bir hikâye...
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Computer scienceOctober 2011Data Modeling Made Simple with PowerDesigner
by Steve Hoberman, George McGeachie
Data Modeling Made Simple with PowerDesigner will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with PowerDesigner. Hear one of the authors, Steve Hoberman, talk about this book. You'll build many PowerDesigner data models along the way, increasing your skills first with the fundamentals and later with more advanced feature of PowerDesigner. This book combines real-world experience and best practices to help you master the following ten objectives: This book has ten key objectives for you, the reader: You will know when a data model is needed and which PowerDesigner models are the most appropriate for each situation You will be able to read a data model of any size and complexity with the same confidence as reading a book You will know when to apply and how to make use of all the key features of PowerDesigner You will be able to build, step-by-step in PowerDesigner, a pyramid of linked data models, including a conceptual data model, a fully normalized relational data model, a physical data model, and an easily navigable dimensional model You will be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design You will improve data governance and modeling consistency within your organization by leveraging features such as PowerDesigner’s reference models, Glossary, domains, and model comparison and model mapping techniques You will know how to utilize dependencies and traceability links to assess the impact of change You will know how to integrate your PowerDesigner models with externally-managed files, including the import and export of data using Excel and Requirements documents You will know where you can take advantage of the entire PowerDesigner model set, to increase the success rate of corporate-wide initiatives such as business intelligence and enterprise resource planning (ERP) You will understand the key differentiators between PowerDesigner and other data modeling tools you may have used before
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Computer scienceAugust 2011Data Modeling Made Simple with CA ERwin Data Modeler r8
With Ca Erwin Data Modeler R8
by Donna Burbank, Steve Hoberman
Hear one of the authors, Steve Hoberman, talk about this book. Data Modeling Made Simple with CA ERwin Data Modeler r8 will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You’ll build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. This book combines real-world experience and best practices with down to earth advice, humor, and even cartoons to help you master the following ten objectives: Understand the basics of data modeling and relational theory, and how to apply these skills using CA ERwin Data Modeler Read a data model of any size and complexity with the same confidence as reading a book Understand the difference between conceptual, logical, and physical models, and how to effectively build these models using CA ERwin’s Data Modelers Design Layer Architecture Apply techniques to turn a logical data model into an efficient physical design and vice-versa through forward and reverse engineering, for both ‘top down’ and bottom-up design Learn how to create reusable domains, naming standards, UDPs, and model templates in CA ERwin Data Modeler to reduce modeling time, improve data quality, and increase enterprise consistency Share data model information with various audiences using model formatting and layout techniques, reporting, and metadata exchange Use the new workspace customization features in CA ERwin Data Modeler r8 to create a workflow suited to your own individual needs Leverage the new Bulk Editing features in CA ERwin Data Modeler r8 for mass metadata updates, as well as import/export with Microsoft Excel Compare and merge model changes using CA ERwin Data Modelers Complete Compare features Optimize the organization and layout of your data models through the use of Subject Areas, Diagrams, Display Themes, and more
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Food & beverage technologySeptember 2020Food Process Engineering and Technology
by Aakash Pare & B.L.Mandhyan
"Food Process Engineering focuses on the design, operation and maintenance of chemical and other process manufacturing activities. The development of "Agro Processing" will spur agricultural diversification. There are several benefits of promoting small scale agro-processing units rather large scale for the promotion of rural entrepreneurship. Appropriate post harvest management and value addition to agricultural products, in their production catchments, will lead to employment and income generation in the rural sector and minimize the losses of harvested biomass. Adoption of suitable technology plays a vital role in fixing the cost of the final product and consequently makes the venture, a profitable one. It is observed that imported agro-processing machines or their imitations are used for preparing food products. Actually, the working of these machines should be critically studied in context of the energy input and the quality of the finished product."
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September 2012Data Modeling Master Class Training Manual 4th Edition
by Steve Hoberman
This is the fourth edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete course on requirements elicitation and data modeling, containing three days of practical techniques for producing solid relational and dimensional data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard. You will know not just how to build a data model, but also how to build a data model well. Two case studies and many exercises reinforce the material and enable you to apply these techniques in your current projects. By the end of the course, you will know how to… Explain data modeling building blocks and identify these constructs by following a question-driven approach to ensure model precision Demonstrate reading a data model of any size and complexity with the same confidence as reading a book Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard Apply requirements elicitation techniques including interviewing and prototyping Build relational and dimensional conceptual, logical, and physical data models through two case studies Practice finding structural soundness issues and standards violations Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous Use a series of templates for capturing and validating requirements, and for data profiling Express how to write clear, complete, and correct definitions Leverage the Grain Matrix, enterprise data model, and available industry data models for a successful enterprise architecture. Steve Hoberman is the most requested data modeling instructor in the world. Introduced at over 50 international conferences as everything from a "data modeling guru" to "data modeling rock star", he balances the formality and precision of data modeling with the realities of building software systems with severe time, budget, and people constraints. In his consulting and teaching, he focuses on templates, tools, and guidelines to reap the benefits of data modeling with minimal investment. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then, spanning every continent except Africa and Antarctica. Steve is the recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, is the chair of the Data Modeling Zone conference, and is the author of five books on data modeling, including the bestseller Data Modeling Made Simple.
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Data warehousingFebruary 2012Data and Reality
A Timeless Perspective On Perceiving & Managing Information in Our Imprecise World -- 3rd Edition
by William Kent
Let's step back to the year 1978. Sony introduces hip portable music with the Walkman, Illinois Bell Company releases the first mobile phone, Space Invaders kicks off the video game craze, and William Kent writes Data and Reality. We have made amazing progress in the last four decades in terms of portable music, mobile communication, and entertainment, making devices such as the original Sony Walkman and suitcase-sized mobile phones museum pieces today. Yet remarkably, the book Data and Reality is just as relevant to the field of data management today as it was in 1978. Data and Reality gracefully weaves the disciplines of psychology and philosophy with data management to create timeless takeaways on how we perceive and manage information. Although databases and related technology have come a long way since 1978, the process of eliciting business requirements and how we think about information remains constant. This book will provide valuable insights whether you are a 1970s data-processing expert or a modern-day business analyst, data modeler, database administrator, or data architect.This third edition of Data and Reality differs substantially from the first and second editions. Data modeling thought leader Steve Hoberman has updated many of the original examples and references and added his commentary throughout the book, including key points at the end of each chapter. The important takeaways in this book are rich with insight yet presented in a conversational writing style. Here are just a few of the issues this book tackles:Has "business intelligence" replaced "artificial intelligence"?Why is a map's geographic landscape analogous to a data model's information landscape?Where do forward and reverse engineering fit in our thought process?Why are we all becoming "data archeologists"?What causes the communication chasm between the business professional and the information technology professional, and how can the logical data model bridge this gap?Why do we invest in hardware and software to solve business problems before determining what the business problems are in the first place?What is the difference between oneness, sameness, and categories?Why does context play a role in every design decision?Why do the more important attributes become entities or relationships?Why do symbols speak louder than words?What's the difference between a data modeler, a philosopher, and an artist?Why is the 1975 dream of mapping all attributes still a dream today?What influence does language have on our perception of reality? Can we distinguish between naming and describing?From Graeme Simsion's foreword:While such fundamental issues remain unrecognized and unanswered, Data and Reality, with its lucid and compelling elucidation of the questions, needs to remain in print. I read the book as a database administrator in 1980, as a researcher in 2002, and just recently as the manuscript for the present edition. On each occasion I found something more, and on each occasion I considered it the most important book I had read on data modeling. It has been on my recommended reading list forever. The first chapter in particular should be mandatory reading for anyone involved in data modeling. In publishing this new edition, Steve Hoberman has not only ensured that one of the key books in the data modeling canon remains in print, but has added his own comments and up-to-date examples, which are likely to be helpful to those who have come to data modeling more recently. Don't do any more data modeling work until you've read it. About William: William Kent (1936-2005) was a renowned researcher in the field of data modeling. Author of Data and Reality, he wrote scores of papers and spoke at conferences worldwide, posing questions about database design and the management of information that remain unanswered today. Though he earned a bachelor's degree in chemical engineering and a master's in mathematics, he had no formal training in computer science. Kent worked at IBM and later at Hewlett-Packard Laboratories, where he helped develop prototype database systems. He also served on or chaired several international standards committees. Kent lived in New York City and later Menlo Park, Calif., before retiring to Moab, Utah, to pursue his passions of outdoor photography and protecting the environment. About Steve: Steve is currently a data modeling consultant and instructor. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then. Steve balances the formality and precision of data modeling with the realities of building software systems with severe time, budget, and people constraints. In his consulting and teaching, he focuses on templates, tools, and guidelines to reap the benefits of data modeling with minimal investment. Steve is the author of five books on data modeling, the founder of the Design Challenges group, and inventor of the Data Model Scorecard.
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Computer scienceApril 2009Data Modeling for the Business
A Handbook for Aligning the Business With It Using High-level Data Models
by Steve Hoberman, Donna Burbank, Chris Bradley
Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. Hear one of the authors, Steve Hoberman, talk about this book. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization. This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours. Building a high-level data model following the ten step approach you’ll read about is a great way to ensure you will retain the new skills you learn in this book. As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general. This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization—between both businesspeople and IT. One of the most critical systems issues is aligning business with IT and fulfilling business needs using data models. The authors of Data Modeling for the Business do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements. Len Silverston, author of The Data Model Resource Book series
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Business & managementJune 2014Data Modeling for MongoDB
by Steve Hoberman
Learn how to capture and precisely document business requirements to create an efficient MongoDB design. Watch Steve Hoberman talk about his book. Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application’s release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future. Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions. Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together. This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. About the Author: Steve Hoberman is the most requested data modeling instructor in the world. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then. Steve is the author of seven books on data modeling, the founder of the Design Challenges group, inventor of the Data Model Scorecard, Conference Chair of the Data Modeling Zone conference, and recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award. Steve can be reached at me@stevehoberman.com, @DataMdlRockStar on Twitter, or through Steve Hoberman on Linked-In.
