Hashing - Shopping2000.com

Datenbanken (eBook, PDF)

Architekturprinzipien und Datenstrukturen moderner Datenbanksysteme Algorithmen und optimierte Anfragen für Datenbankoperationen Transaktionsmodelle sowie Transaktionsverwaltung im Mehrbenutzerbetrieb Datenbankmanagementsysteme (DBMS) bilden häufig die Kernkomponente von Informationssystemen und ermöglichen die integrierte Speicherung von großen Datenbeständen, auf die mehrere Anwendungen gleichzeitig zugreifen können. Bei der Implementierung dieser Systeme müssen einige Anforderungen berücksichtigt werden: Effiziente Speicherung und schnelles Wiederauffinden der Daten Datenunabhängigkeit Zuverlässiger Mehrbenutzerbetrieb Wiederherstellung der Daten nach Systemausfällen Kompatibilität zu verschiedenen Rechnerarchitekturen Die Autoren behandeln die wichtigsten Konzepte und Techniken der Implementierung von DBMS, wobei der Schwerpunkt auf den Konzepten und Basistechnologien kommerzieller, meist relationaler Datenbanksysteme liegt: Architektur, Datenorganisation, Anfragebearbeitung, Synchronisation im Mehrbenutzerbetrieb und Recovery. Darüber hinaus gehen die Autoren auch auf aktuelle Entwicklungen bei Speichermedien, alternativen Speichermodellen, der Bearbeitung von Data-Warehouse-Anfragen, Anfrageoptimierern und Transaktionsmodellen ein. Angaben zu vertiefender Literatur sowie Übungen am Ende der Kapitel helfen beim Vertiefen des Gelernten sowie bei Selbststudium und Prüfungsvorbereitung. Zum Verständnis des Buches sind Grundkenntnisse der theoretischen Grundlagen von DBMS wie Relationenalgebra sowie Basiskenntnisse in SQL notwendig. Aus dem Inhalt: Externspeicher- und Pufferverwaltung Speicherhierarchie und -medien Seiten, Datensätze und ihre Adressierung Row Stores und Column Stores Seitenersetzungsstrategien Dateiorganisation und Indexstrukturen B-Bäume Partitionierung Dynamisches Hashing Mehrdimensionale und geometrische Indexstrukturen Bitmap-Indexe Anfrageverarbeitung und -optimierung Anfrageoperatoren Logische und physische Optimierung Kostenmodelle und Statistiken in DBMS Transaktionsverwaltung und Recovery Serialisierbarkeit Sperrprotokolle und nichtsperrende Verfahren Commit-Protokolle Logging und Recovery-Strategien

Shop: buecher
Price: 3.99 EUR excl. shipping

Art of Computer Programming, The (eBook, PDF)

The bible of all fundamental algorithms and the work that taught many of today's software developers most of what they know about computer programming. -Byte, September 1995 I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me! I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up. -Charles Long If you think you're a really good programmer... read [Knuth's] Art of Computer Programming... You should definitely send me a resume if you can read the whole thing. -Bill Gates It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers. -Jonathan Laventhol The first revision of this third volume is the most comprehensive survey of classical computer techniques for sorting and searching. It extends the treatment of data structures in Volume 1 to consider both large and small databases and internal and external memories. The book contains a selection of carefully checked computer methods, with a quantitative analysis of their efficiency. Outstanding features of the second edition include a revised section on optimum sorting and new discussions of the theory of permutations and of universal hashing. Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP),http://msp.org

Shop: buecher
Price: 31.95 EUR excl. shipping

Merkmalskonstruktion für Machine Learning

Die Merkmalskonstruktion, auch Feature Engineering genannt, ist ein entscheidender Arbeitsschritt bei der Datenaufbereitung für das maschinelle Lernen, der die Leistung der Modelle stark beeinflusst. In diesem praxisnahen Buch lernen Sie Techniken, um Merkmale - numerische Repräsentationen eines bestimmten Aspekts von Rohdaten - zu gewinnen und mit maschinellen Lernmodellen nutzbar zu machen. Jedes Kapitel führt Sie durch eine spezifische Aufgabe der Datenanalyse wie etwa die Darstellung von Text- oder Bilddaten. Diese Beispiele veranschaulichen die wichtigsten Prinzipien der Merkmalskonstruktion. Statt diese Prinzipien nur zu beschreiben, legen die Autorinnen Alice Zheng und Amanda Casari im gesamten Buch den Schwerpunkt auf die praktische Anwendung mit Übungen. Das Schlusskapitel vertieft das Gelernte, indem es verschiedene Techniken der Merkmalskonstruktion auf einen realen, strukturierten Datensatz anwendet. In den Beispielen werden Python-Pakete wie numpy, Pandas, scikit-learn und Matplotlib verwendet.Aus dem Inhalt:- Merkmalskonstruktion an numerischen Daten: Filter, Klasseneinteilung, Skalierung, logarithmische und Potenz-Transformationen- Techniken für natürlichen Text: Bag-of-Words-Modelle, n-Gramme und Phrasenerkennung- Frequenzfilterung und Merkmalsskalierung zum Entfernen aussageloser Merkmale- Kodierungstechniken für Kategorievariablen, darunter Merkmals-Hashing und Klassenzählung- Modellgesteuerte Merkmalskonstruktion mit der Hauptkomponentenanalyse- Das Konzept der Modellkombination mit dem k-Means-Algorithmus als Technik zur Merkmalserzeugung- Gewinnung von Bildmerkmalen anhand manueller und Deep-Learning-Techniken "Datenaufbereitung und Merkmalskonstruktion haben sich in vielen Anwendungen als die wichtigsten Einflussfaktoren für die Leistungsfähigkeit der Modelle erwiesen. Ich freue mich, dass es endlich ein Buch gibt, das sich nur diesem Thema widmet. Alice und Amanda erklären sehr detailliert die Feinheiten vieler verbreiteter Techniken."- Andreas C. MüllerDozent für Machine Learning an der Universität von Columbia und Kernentwickler bei scikit-learn

Shop: buecher
Price: 35.90 EUR excl. shipping

Practical Forensic Imaging

Forensic image acquisition is an important part of postmortem incident response and evidence collection. Digital forensic investigators acquire, preserve, and manage digital evidence to support civil and criminal cases; examine organizational policy violations; resolve disputes; and analyze cyber attacks. Practical Forensic Imaging takes a detailed look at how to secure and manage digital evidence using Linux-based command line tools. This essential guide walks you through the entire forensic acquisition process and covers a wide range of practical scenarios and situations related to the imaging of storage media. You'll learn how to: -Perform forensic imaging of magnetic hard disks, SSDs and flash drives, optical discs, magnetic tapes, and legacy technologies -Protect attached evidence media from accidental modification -Manage large forensic image files, storage capacity, image format conversion, compression, splitting, duplication, secure transfer and storage, and secure disposal -Preserve and verify evidence integrity with cryptographic and piecewise hashing, public key signatures, and RFC-3161 timestamping -Work with newer drive and interface technologies like NVME, SATA Express, 4K-native sector drives, SSHDs, SAS, UASP/USB3x, and Thunderbolt -Manage drive security such as ATA passwords; encrypted thumb drives; Opal self-encrypting drives; OS-encrypted drives using BitLocker, FileVault, and TrueCrypt; and others -Acquire usable images from more complex or challenging situations such as RAID systems, virtual machine images, and damaged media With its unique focus on digital forensic acquisition and evidence preservation, Practical Forensic Imaging is a valuable resource for experienced digital forensic investigators wanting to advance their Linux skills and experienced Linux administrators wanting to learn digital forensics. This is a must-have reference for every digital forensics lab.

Shop: buecher
Price: 45.99 EUR excl. shipping

How Cybersecurity Really Works

How Cybersecurity Really Works is an engaging introduction to the field of cybersecurity. You'll learn how attackers operate, as well as how to defend yourself and organizations against online attacks.How Cybersecurity Really Works is the perfect introduction to cybersecurity. Whether you re a computer science student or a business professional, it will teach you the basics without all the jargon.This beginners guide covers different types of attacks, common tactics used by online adversaries, and defensive strategies you can use to protect yourself. You ll learn what security professionals do, what an attack looks like from a cybercriminal s viewpoint, and how to implement sophisticated cybersecurity measures on your own devices.In addition, you ll find explanations of topics like malware, phishing, and social engineering attacks, coupled with real-world examples and hands-on exercises to help you apply what you ve learned. You ll explore ways to bypass access controls, prevent infections from worms and viruses, and protect your cloud accounts from attackers.You ll also learn how to:Analyze emails to detect phishing attempts Use SQL injection to attack a website Examine malware from the safety of a sandbox environment Use the command line to evaluate and improve your computer and network security Deploy encryption and hashing to protect your files Create a comprehensive risk management plan You can t afford to ignore cybersecurity anymore, but attackers won t wait while you read a long technical manual. That s why How Cybersecurity Really Works teaches you just the essentials you need to think beyond antivirus and make the right decisions to keep the online monsters at bay.

Shop: buecher
Price: 16.99 EUR excl. shipping

The Algorithm Design Manual

"My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ... graph problems are -- they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 - pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types." (Steve Yegge, Get that Job at Google)"Steven Skiena's Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. ... Every programmer should read this book, and anyone working in the field should keep it close to hand. ... This is the best investment ... a programmer or aspiring programmer can make." (Harold Thimbleby, Times Higher Education)"It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!" (Cory Bart, University of Delaware)"The is the most approachable book on algorithms I have." (Megan Squire, Elon University)---This newly expanded and updated third edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficiency. It serves as the primary textbook of choice for algorithm design courses and interview self-study, while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students.The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography. NEW to the third edition: -- New and expanded coverage of randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing -- Provides full online support for lecturers, including an improved website component with lecture slides and videos -- Full color illustrations and code instantly clarify difficult concepts -- Includes several new "war stories" relating experiences from real-world applications-- Over 100 new problems, including programming-challenge problems from LeetCode and Hackerrank. -- Provides up-to-date links leading to the best implementations available in C, C++, and JavaAdditional Learning Tools: -- Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them -- Exercises include "job interview problems" from major software companies -- Highlighted "take home lessons" emphasize essential concepts -- The "no theorem-proof" style provides a uniquely accessible and intuitive approach to a challenging subject -- Many algorithms are presented with actual code (written in C) -- Provides comprehensive references to both survey articles and the primary literatureWritten by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this substantially enhanced third e

Shop: buecher
Price: 48.99 EUR excl. shipping

The Algorithm Design Manual (eBook, PDF)

"My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ... graph problems are -- they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 - pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types." (Steve Yegge, Get that Job at Google)"Steven Skiena's Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. ... Every programmer should read this book, and anyone working in the field should keep it close to hand. ... This is the best investment ... a programmer or aspiring programmer can make." (Harold Thimbleby, Times Higher Education)"It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!" (Cory Bart, University of Delaware)"The is the most approachable book on algorithms I have." (Megan Squire, Elon University)---This newly expanded and updated third edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficiency. It serves as the primary textbook of choice for algorithm design courses and interview self-study, while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography. NEW to the third edition: -- New and expanded coverage of randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing -- Provides full online support for lecturers, including an improved website component with lecture slides and videos -- Full color illustrations and code instantly clarify difficult concepts -- Includes several new "war stories" relating experiences from real-world applications -- Over 100 new problems, including programming-challenge problems from LeetCode and Hackerrank. -- Provides up-to-date links leading to the best implementations available in C, C++, and Java Additional Learning Tools: -- Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them -- Exercises include "job interview problems" from major software companies -- Highlighted "take home lessons" emphasize essential concepts -- The "no theorem-proof" style provides a uniquely accessible and intuitive approach to a challenging subject -- Many algorithms are presented with actual code (written in C) -- Provides comprehensive references to both survey articles and the primary literature Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this substantially enhanced third edition of The Algorithm Design Manual is an essential learning tool for students and professionals needed a solid grounding in algorithms. Professor Skiena is also the author of the popular Springer texts, The Data Science Design Manual and Programming Challenges: The Programming Contest Training Manual.

Shop: buecher
Price: 48.95 EUR excl. shipping

Algorithm Design: Pearson New International Edition

August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age. Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. Features + Benefits Focus on problem analysis and design techniques. Discussion is grounded in concrete problems and examples rather than abstract presentation of principles, with representative problems woven throughout the text. Over 200 well crafted problems from companies such as Yahoo!® and Oracle®. Each problem has been class tested for usefulness and accuracy in the authors' own undergraduate algorithms courses. Broad coverage of algorithms for dealing with NP-hard problems and the application of randomization, increasingly important topics in algorithms. Algorithm DesignJon Kleinberg and Eva TardosTable of Contents 1 Introduction: Some Representative Problems 1.1 A First Problem: Stable Matching 1.2 Five Representative Problems Solved ExercisesExcercisesNotes and Further Reading2 Basics of Algorithms Analysis 2.1 Computational Tractability 2.2 Asymptotic Order of Growth Notation 2.3 Implementing the Stable Matching Algorithm using Lists and Arrays 2.4 A Survey of Common Running Times 2.5 A More Complex Data Structure: Priority Queues Solved Exercises Exercises Notes and Further Reading3 Graphs 3.1 Basic Definitions and Applications 3.2 Graph Connectivity and Graph Traversal 3.3 Implementing Graph Traversal using Queues and Stacks 3.4 Testing Bipartiteness: An Application of Breadth-First Search 3.5 Connectivity in Directed Graphs 3.6 Directed Acyclic Graphs and Topological Ordering Solved Exercises Exercises Notes and Further Reading 4 Divide and Conquer 4.1 A First Recurrence: The Mergesort Algorithm 4.2 Further Recurrence Relations 4.3 Counting Inversions 4.4 Finding the Closest Pair of Points 4.5 Integer Multiplication 4.6 Convolutions and The Fast Fourier Transform Solved Exercises Exercises Notes and Further Reading5 Greedy Algorithms 5.1 Interval Scheduling: The Greedy Algorithm Stays Ahead 5.2 Scheduling to Minimize Lateness: An Exchange Argument 5.3 Optimal Caching: A More Complex Exchange Argument 5.4 Shortest Paths in a Graph 5.5 The Minimum Spanning Tree Problem 5.6 Implementing Kruskal's Algorithm: The Union-Find Data Structure 5.7 Clustering 5.8 Huffman Codes and the Problem of Data Compression*5.9 Minimum-Cost Arborescences: A Multi-Phase Greedy Algorithm Solved Exercises Excercises Notes and Further Reading 6 Dynamic Programming 6.1 Weighted Interval Scheduling: A Recursive Procedure 6.2 Weighted Interval Scheduling: Iterating over Sub-Problems 6.3 Segmented Least Squares: Multi-way Choices 6.4 Subset Sums and Knapsacks: Adding a Variable 6.5 RNA Secondary Structure: Dynamic Programming Over Intervals 6.6 Sequence Alignment 6.7 Sequence Alignment in Linear Space 6.8 Shortest Paths in a Graph 6.9 Shortest Paths and Distance Vector Protocols *6.10 Negative Cycles in a Graph Solved ExercisesExercisesNotes and Further Reading7 Network Flow 7.1 The Maximum Flow Problem and the Ford-Fulkerson Algorithm 7.2 Maximum Flows and Minimum Cuts in a Network 7.3 Choosing Good Augmenting Paths *7.4 The Preflow-Push Maximum Flow Algorithm 7.5 A First Application: The Bipartite Matching Problem 7.6 Disjoint Paths in Directed and Undirected Graphs 7.7 Extensions to the Maximum Flow Problem 7.8 Survey Design 7.9 Airline Scheduling 7.10 Image Segmentation 7.11 Project Selection 7.12 Baseball Elimination *7.13 A Further Direction: Adding Costs to the Matching Problem Solved ExercisesExercisesNotes and Further Reading 8 NP and Computational Intractability 8.1 Polynomial-Time Reductions 8.2 Reductions via "Gadgets": The Satisfiability Problem 8.3 Efficient Certification and the Definition of NP 8.4 NP-Complete Problems 8.5 Sequencing Problems 8.6 Partitioning Problems 8.7 Graph Coloring8.8 Numerical Problems 8.9 Co-NP and the Asymmetry of NP8.10 A Partial Taxonomy of Hard Problems Solved Exercises Exercises Notes and Further Reading9 PSPACE: A Class of Problems Beyond NP9.1 PSPACE 9.2 Some Hard Problems in PSPACE 9.3 Solving Quantified Problems and Games in Polynomial Space9.4 Solving the Planning Problem in Polynomial Space9.5 Proving Problems PSPACE-Complete Solved ExercisesExercisesNotes and Further Reading 10 Extending the Limits of Tractability 10.1 Finding Small Vertex Covers 10.2 Solving NP-Hard Problem on Trees 10.3 Coloring a Set of Circular Arcs *10.4 Tree Decompositions of Graphs *10.5 Constructing a Tree Decomposition Solved Exercises Exercises Notes and Further Reading11 Approximation Algorithms 11.1 Greedy Algorithms and Bounds on the Optimum: A Load Balancing Problem 11.2 The Center Selection Problem 11.3 Set Cover: A General Greedy Heuristic 11.4 The Pricing Method: Vertex Cover 11.5 Maximization via the Pricing method: The Disjoint Paths Problem 11.6 Linear Programming and Rounding: An Application to Vertex Cover *11.7 Load Balancing Revisited: A More Advanced LP Application 11.8 Arbitrarily Good Approximations: the Knapsack Problem Solved ExercisesExercisesNotes and Further Reading 12 Randomized Algorithms 12.1 A First Application: Contention Resolution 12.2 Finding the Global Minimum Cut 12.3 Random Variables and their Expectations 12.4 A Randomized Approximation Algorithm for MAX 3-SAT 12.5 Randomized Divide-and-Conquer: Median-Finding and Quicksort 12.6 Hashing: A Randomized Implementation of Dictionaries 12.7 Finding the Closest Pair of Points: A Randomized Approach 12.8 Randomized Caching 12.9 Chernoff Bounds 12.10 Load Balancing *12.11 Packet Routing 12.12 Background: Some Basic Probability DefinitionsSolved ExercisesExercisesNotes and Further Reading 13 Local Search 13.1 The Landscape of an Optimization Problem 13.2 The Metropolis Algorithm and Simulated Annealing 13.3 An Application of Local Search to Hopfield Neural Networks 13.4 Maximum Cut Approximation via Local Search 13.5 Choosing a Neighbor Relation *13.6 Classification via Local Search 13.7 Best-Response Dynamics and Nash EquilibriaSolved ExercisesExercisesNotes aAugust 6, 2009 Author, Jon Kleinberg, was recently cited in the for his statistical analysis research in the Internet age. Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

Shop: buecher
Price: 94.99 EUR excl. shipping