Osoby które kupowały "The Practitioner's Guide to Graph Data. Applying Graph Thinking and Graph Technologies to Solve Complex Problems", wybierały także:

• NLP. Kurs video. Analiza danych tekstowych w j 149,00 zÅ‚, (59,60 zÅ‚ -60%)
• Web scraping. Kurs video. Zautomatyzowane pozyskiwanie danych z sieci 139,00 zÅ‚, (55,60 zÅ‚ -60%)
• Data Science w Pythonie. Kurs video. Algorytmy uczenia maszynowego 199,00 zÅ‚, (79,60 zÅ‚ -60%)
• Microsoft Excel. Kurs video. Wykresy i wizualizacja danych 199,00 zÅ‚, (89,55 zÅ‚ -55%)
• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zÅ‚, (67,05 zÅ‚ -55%)

Spis treści

The Practitioner's Guide to Graph Data. Applying Graph Thinking and Graph Technologies to Solve Complex Problems eBook -- spis treści

• Preface
  • Who Should Read This Book
  • Goals of This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Graph Thinking
  • Why Now? Putting Database Technologies in Context
    • 1960s1980s: Hierarchical Data
    • 1980s2000s: Entity-Relationship
    • 2000s2020s: NoSQL
    • 2020s?: Graph
      • Why the 2020s?
      • Connecting the dots
  • What Is Graph Thinking?
    • Complex Problems and Complex Systems
    • Complex Problems in Business
  • Making Technology Decisions to Solve Complex Problems
    • Question 1: Does your problem need graph data?
    • Question 2: Do relationships within your data help you understand your problem?
    • Common missteps in understanding your data
    • So You Have Graph Data. Whats Next?
      • Question 3: What are you going to do with the relationships in your data?
      • Question 4: What do you need the results for?
      • Break it down and try again
    • Seeing the Bigger Picture
  • Getting Started on Your Journey with Graph Thinking
• 2. Evolving from Relational to Graph Thinking
  • Chapter Preview: Translating Relational Concepts to Graph Terminology
  • Relational Versus Graph: Whats the Difference?
    • Data for Our Running Example
  • Relational Data Modeling
    • Entities and Attributes
    • Building Up to an ERD
  • Concepts in Graph Data
    • Fundamental Elements of a Graph
    • Adjacency
    • Neighborhoods
    • Distance
    • Degree
      • Implications of vertex degree
  • The Graph Schema Language
    • Vertex Labels and Edge Labels
    • Properties
    • Edge Direction
    • Self-Referencing Edge Labels
    • Multiplicity of Your Graph
      • Modeling multiplicity in the GSL
    • Full Example Graph Model
  • Relational Versus Graph: Decisions to Consider
    • Data Modeling
    • Understanding Graph Data
    • Mixing Database Design with Application Purpose
  • Summary
• 3. Getting Started: A Simple Customer 360
  • Chapter Preview: Relational Versus Graph
  • The Foundational Use Case for Graph Data: C360
    • Why Do Businesses Care About C360?
  • Implementing a C360 Application in a Relational System
    • Data Models
    • Relational Implementation
    • Example C360 Queries
      • Query: Which credit cards does this customer use?
      • Query: Which accounts does this customer own?
      • Query: Which loans does this customer owe?
      • Query: What do we know about this customer?
  • Implementing a C360 Application in a Graph System
    • Data Models
    • Graph Implementation
      • Creating your graphs schema
      • Inserting your graph data
      • Graph traversals
    • Example C360 Queries
      • Query: Which credit cards does this customer use?
      • Query: Which accounts does this customer own?
      • Query: Which loans does this customer owe?
      • Query: What do we know about this customer?
  • Relational Versus Graph: How to Choose?
    • Relational Versus Graph: Data Modeling
    • Relational Versus Graph: Representing Relationships
    • Relational Versus Graph: Query Languages
    • Relational Versus Graph: Main Points
  • Summary
    • Why Not Relational?
    • Making a Technology Choice for Your C360 Application
• 4. Exploring Neighborhoods in Development
  • Chapter Preview: Building a More Realistic Customer 360
  • Graph Data Modeling 101
    • Should This Be a Vertex or an Edge?
    • Lost Yet? Let Us Walk You Through Direction
      • An evolution of modeling transactions in a graph
      • When do we use properties?
    • A Graph Has No Name: Common Mistakes in Naming
    • Our Full Development Graph Model
    • Before We Start Building
    • Our Thoughts on the Importance of Data, Queries, and the End User
  • Implementation Details for Exploring Neighborhoods in Development
    • Generating More Data for Our Expanded Example
  • Basic Gremlin Navigation
    • Query 1: What are the most recent 20 transactions involving Michaels account?
    • Query 2: In December 2020, at which vendors did Michael shop, and with what frequency?
    • Query 3: Find and update the transactions that Jamie and Aaliyah most value: their payments from their account to their mortgage loan.
      • Query 3a: Find Aaliyahs transactions that are loan payments
      • Query 3b: Find and update the transactions that Jamie and Aaliyah most value: their payments from their checking account to their mortgage, loan_18
      • Query 3c: Verify that we didnt update every transaction
  • Advanced Gremlin: Shaping Your Query Results
    • Shaping Query Results with the project(), fold(), and unfold() Steps
    • Removing Data from the Results with the where(neq()) Pattern
    • Planning for Robust Result Payloads with the coalesce() Step
  • Moving from Development into Production
• 5. Exploring Neighborhoods in Production
  • Chapter Preview: Understanding Distributed Graph Data in Apache Cassandra
  • Working with Graph Data in Apache Cassandra
    • The Most Important Topic to Understand About Data Modeling: Primary Keys
    • Partition Keys and Data Locality in a Distributed Environment
      • Partitioning graph data according to access pattern
      • Partitioning according to unique key
      • Final thoughts on partitioning strategies
    • Understanding Edges, Part 1: Edges in Adjacency Lists
    • Understanding Edges, Part 2: Clustering Columns
      • Synthesizing concepts: Edge location in a distributed cluster
    • Understanding Edges, Part 3: Materialized Views for Traversals
      • Materialized views for bidirectional edges
      • How far down do you want to go?
      • Where are we going from here?
  • Graph Data Modeling 201
    • Finding Indexes with an Intelligent Index Recommendation System
  • Production Implementation Details
    • Materialized Views and Adding Time onto Edges
    • Our Final C360 Production Schema
    • Bulk Loading Graph Data
      • Loading vertex data with DataStax Bulk Loader
      • Loading edge data with DataStax Bulk Loader
    • Updating Our Gremlin Queries to Use Time on Edges
      • Query 1: What are the most recent 20 transactions involving Michaels account?
      • Query 2: In December, at which vendors did Michael shop, and with what frequency?
      • Query 3: Find and update the transactions that Jamie and Aaliyah most value: their payments from their account to their mortgage loan.
  • Moving On to More Complex, Distributed Graph Problems
    • Our First 10 Tips to Get from Development to Production
• 6. Using Trees in Development
  • Chapter Preview: Navigating Trees, Hierarchical Data, and Cycles
  • Seeing Hierarchies and Nested Data: Three Examples
    • Hierarchical Data in a Bill of Materials
    • Hierarchical Data in Version Control Systems
    • Hierarchical Data in Self-Organizing Networks
    • Why Graph Technology for Hierarchical Data?
  • Finding Your Way Through a Forest of Terminology
    • Trees, Roots, and Leaves
    • Depth in Walks, Paths, and Cycles
  • Understanding Hierarchies with Our Sensor Data
    • Understand the Data
      • Seeing hierarchies in data: From the bottom up
      • Seeing hierarchies in data: From the top down
      • Understanding edges in the sensor hierarchies
    • Conceptual Model Using the GSL Notation
    • Implement Schema
      • Loading vertex data with DataStax Bulk Loader
      • Loading edge data with DataStax Bulk Loader
    • Before We Build Our Queries
  • Querying from Leaves to Roots in Development
    • Where Has This Sensor Sent Information To?
    • From This Sensor, What Was Its Path to Any Tower?
      • Using the path() step and manipulating its data structure
        • How to assign labels with as()
        • How to shape path() results with by()
    • From Bottom Up to Top Down
  • Querying from Roots to Leaves in Development
    • Setup Query: Which Tower Has the Most Sensor Connections So That We Could Explore It for Our Example?
    • Which Sensors Have Connected Directly to Georgetown?
    • Find All Sensors That Connected to Georgetown
    • Depth Limiting in Recursion
  • Going Back in Time
• 7. Using Trees in Production
  • Chapter Preview: Understanding Branching Factor, Depth, and Time on Edges
  • Understanding Time in the Sensor Data
    • Understanding time in hierarchies of data: From the bottom up
    • Valid and invalid paths from the bottom up
    • Understanding time in hierarchies of data: From the top down
    • Valid and invalid paths from the top down
    • Final Thoughts on Time Series Data in Graphs
  • Understanding Branching Factor in Our Example
    • What Is Branching Factor?
    • How Do We Get Around Branching Factor?
  • Production Schema for Our Sensor Data
    • Loading data with DataStax Bulk Loader
  • Querying from Leaves to Roots in Production
    • Where Has This Sensor Sent Information to, and at What Time?
    • From This Sensor, Find All Trees up to a Tower by Time
    • From This Sensor, Find a Valid Tree
    • Advanced Gremlin: Understanding the where().by() Pattern
      • Understanding a common Gremlin mistake: Overloading has()
      • Resolution: The where().by() pattern
  • Querying from Roots to Leaves in Production
    • Which Sensors Have Connected to Georgetown Directly, by Time?
    • What Valid Paths Can We Find from Georgetown Down to All Sensors?
  • Applying Your Queries to Tower Failure Scenarios
    • Get a list of sensors that connected with Georgetown in any time window
    • For each at-risk sensor, find all towers it communicated with
    • Applying the Final Results of Our Complex Problem
  • Seeing the Forest for the Trees
• 8. Finding Paths in Development
  • Chapter Preview: Quantifying Trust in Networks
  • Thinking About Trust: Three Examples
    • How Much Do You Trust That Open Invitation?
    • How Defensible Is an Investigators Story?
    • How Do Companies Model Package Delivery?
  • Fundamental Concepts About Paths
    • Shortest Paths
    • Depth-First Search and Breadth-First Search
    • Learning to See Application Features as Different Path Problems
  • Finding Paths in a Trust Network
    • Source Data
    • A Brief Primer on Bitcoin Terminology
    • Creating Our Development Schema
    • Loading Data
    • Exploring Communities of Trust
  • Understanding Traversals with Our Bitcoin Trust Network
    • Which Addresses Are in the First Neighborhood?
    • Which Addresses Are in the Second Neighborhood?
    • Which Addresses Are in the Second Neighborhood, but Not the First?
    • Evaluation Strategies with the Gremlin Query Language
      • Barrier steps in Gremlin
    • Pick a Random Address to Use for Our Example
  • Shortest Path Queries
    • Finding Paths of a Fixed Length
    • Finding Paths of Any Length
      • Connecting concepts: BFS and traversal strategies
    • Augmenting Our Paths with the Trust Scores
      • Using sack() to aggregate trust ratings
    • Do You Trust This Person?
• 9. Finding Paths in Production
  • Chapter Preview: Understanding Weights, Distance, and Pruning
  • Weighted Paths and Search Algorithms
    • Shortest Weighted Path Problem Definition
    • Shortest Weighted Path Search Optimizations
      • Supernodes in graphs
      • Theoretical limits of supernodes
      • Pseudocode for the search algorithm we will implement
  • Normalization of Edge Weights for Shortest Path Problems
    • Normalizing the Edge Weights
      • Step 1: Shift the scale to the interval [0,1]
      • Step 2: Frame the new scale as a shortest path problem
      • Step 3: Decide how to handle modeling infinity
    • Updating Our Graph
    • Exploring the Normalized Edge Weights
      • Find all paths of length 2, sorted by total trust
      • Find the 15 shortest paths by path length, sorted by total trust
      • Interpreting path distance to total trust with the normalized edge weights
    • Some Thoughts Before Moving On to Shortest Weighted Path Queries
  • Shortest Weighted Path Queries
    • Building a Shortest Weighted Path Query for Production
      • 1) Swap two steps and change our limit
      • 2) Add an object to track the shortest weighted path to a visited vertex
      • 3) Remove a traverser if its path is longer than one already discovered to that vertex
      • 4) Remove traversers for custom reasons, such as to avoid supernodes
        • The and() step in Gremlin
        • sideEffect() in Gremlin
        • Interpreting the results of our shortest weighted path
  • Weighted Paths and Trust in Production
• 10. Recommendations in Development
  • Chapter Preview: Collaborative Filtering for Movie Recommendations
  • Recommendation System Examples
    • How We Give Recommendations in Healthcare
    • How We Experience Recommendations in Social Media
    • How We Use Deeply Connected Data for Recommendations in Ecommerce
  • An Introduction to Collaborative Filtering
    • Understanding the Problem and Domain
    • Collaborative Filtering with Graph Data
    • Recommendations via Item-Based Collaborative Filtering with Graph Data
    • Three Different Models for Ranking Recommendations
      • Path counting
      • Net Promoter Scoreinspired metric
      • Normalized Net Promoter Scores
  • Movie Data: Schema, Loading, and Query Review
    • Data Model for Movie Recommendations
    • Schema Code for Movie Recommendations
    • Loading the Movie Data
      • Loading the vertices
      • Loading the edges
    • Neighborhood Queries in the Movie Data
      • Grouping a users movie ratings by liked, disliked, or neutral
    • Tree Queries in the Movie Data
    • Path Queries in the Movie Data
  • Item-Based Collaborative Filtering in Gremlin
    • Model 1: Counting Paths in the Recommendation Set
    • Model 2: NPS-Inspired
    • Model 3: Normalized NPS
    • Choosing Your Own Adventure: Movies and Graph Problems Edition
• 11. Simple Entity Resolution in Graphs
  • Chapter Preview: Merging Multiple Datasets into One Graph
  • Defining a Different Complex Problem: Entity Resolution
    • Seeing the Complex Problem
  • Analyzing the Two Movie Datasets
    • MovieLens Dataset
      • 1) Links
      • 2) Movies
      • 3) Ratings
      • 4) Tags
      • 5) Tag genome
    • Kaggle Dataset
      • Movie details
      • Actors and casting details
    • Development Schema
  • Matching and Merging the Movie Data
    • Our Matching Process
  • Resolving False Positives
    • False Positives Found in the MovieLens Dataset
    • Additional Errors Discovered in the Entity Resolution Process
    • Final Analysis of the Merging Process
    • The Role of Graph Structure in Merging Movie Data
• 12. Recommendations in Production
  • Chapter Preview: Understanding Shortcut Edges, Precomputation, and Advanced Pruning Techniques
  • Shortcut Edges for Recommendations in Real Time
    • Where Our Development Process Doesnt Scale
    • How We Fix Scaling Issues: Shortcut Edges
    • Seeing What We Designed to Deliver in Production
    • Pruning: Different Ways to Precompute Shortcut Edges
      • Pruning by score thresholds
      • Pruning with hard limits on total recommendations
      • Pruning by applying domain knowledge filters
    • Considerations for Updating Your Recommendations
  • Calculating Shortcut Edges for Our Movie Data
    • Breaking Down the Complex Problem of Precalculating Shortcut Edges
      • Schema required for calculating shortcut edges on our movie data
      • Collaborative-filtering query to calculate shortcut edges
      • Using simple parallelism to divide up the work
    • Addressing the Elephant in the Room: Batch Computation
      • Examples of when batch computation may be better for your environment
      • Examples of when transactional queries may be better for your environment
  • Production Schema and Data Loading for Movie Recommendations
    • Production Schema for Movie Recommendations
    • Production Data Loading for Movie Recommendations
  • Recommendation Queries with Shortcut Edges
    • Confirming Our Edges Loaded Correctly
    • Production Recommendations for Our User
      • Query 1: The top three recommendations for the most recent rating by our user
      • Query 2: The top recommendation for the three most recent ratings by our user
      • Query 3: The top three recommendations for each of the three most recent ratings by our user
      • What are we ignoring that you need to consider?
    • Understanding Response Time in Production by Counting Edge Partitions
      • Partitions traversed for Query 1: The top three recommendations for the most recent rating by a user
      • Partitions traversed for Query 2: The top recommendation for the three most recent ratings by a user
      • Partitions traversed for Query 3: The top three recommendations for each of the three most recent ratings by our user
    • Final Thoughts on Reasoning About Distributed Graph Query Performance
• 13. Epilogue
  • Where to Go from Here?
    • Graph Algorithms
    • Distributed Graphs
    • Graph Theory
    • Network Theory
  • Stay in Touch
• Index