Apache TinkerPop serves as an Apache governed, vendor-agnostic, open source initiative providing a standard interface and query language for both OLTP- and OLAP-based graph systems. This presentation will outline the means by which vendors implement TinkerPop and then, in turn, how the Gremlin graph traversal language is able to process the vendor's underlying graph structure. The material will be presented from the perspective of the DSEGraph team's use of Apache TinkerPop in enabling graph computing features for DataStax Enterprise customers.
About the Speaker
Marko Rodriguez Director of Engineering, DataStax
Dr. Marko A. Rodriguez is the co-founder of Apache TinkerPop and creator of the Gremlin graph traversal language. Gremlin is leveraged by numerous graph system vendors including DataStax's DSEGraph. Currently, Marko is a Director of Engineering at DataStax focusing his time and effort on graphs in general and Apache TinkerPop in particular.
This document summarizes the key concepts and components of Gremlin's graph traversal machinery:
- Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains.
- Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine.
- The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal.
- Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.
Building Machine Learning Algorithms on Apache Spark: Scaling Out and Up with...Databricks
There are lots of reasons why you might want to implement your own machine learning algorithms on Spark: you might want to experiment with a new idea, try and reproduce results from a recent research paper, or simply to use an existing technique that isn’t implemented in MLlib.
In this talk, we’ll walk through the process of developing a new machine learning algorithm for Spark. We’ll start with the basics, by considering how we’d design a scale-out parallel implementation of our unsupervised learning technique. The bulk of the talk will focus on the details you need to know to turn an algorithm design into an efficient parallel implementation on Spark.
We’ll start by reviewing a simple RDD-based implementation, show some improvements, point out some pitfalls to avoid, and iteratively extend our implementation to support contemporary Spark features like ML Pipelines and structured query processing. We’ll conclude by briefly examining some useful techniques to complement scale-out performance by scaling our code up, taking advantage of specialized hardware to accelerate single-worker performance.
You’ll leave this talk with everything you need to build a new machine learning technique that runs on Spark.
1. The document discusses a parametric facade system inspired by the stomata membrane in plant leaves.
2. It describes the development of a geometric pattern to mimic stomata and make the facade adaptable to environmental conditions.
3. Pseudocode and functions are provided to map the geometric pattern onto a surface and divide it, with the goal of creating a parametric skin that can store water, admit light, and generate wind energy.
OLC assembly involves three main steps:
1. Overlap - Compute all overlaps between reads to construct an overlap graph
2. Layout - Bundle stretches of the overlap graph into contigs
3. Consensus - Pick the most likely nucleotide sequence for each contig by determining consensus from the underlying reads
This document outlines a talk on using category theory concepts in functional programming. It begins by introducing the definition of a category from category theory and using Scala examples to demonstrate how types and functions in Scala satisfy this definition. It then defines functors, natural transformations, and monads from category theory and provides examples showing how options, lists, and functions in Scala form a monad. It proves that the category theory definition of a monad is equivalent to the definition used in functional programming. The document suggests category theory concepts help formalize design patterns and make code more refactorable.
APassengerKnockOnDelayModelForTimetableOptimisation_beamerPeter Sels
The document presents a model for optimizing passenger travel time in train timetabling by accounting for knock-on delays. It develops a stochastic goal function to minimize expected passenger transfer time considering primary delays and knock-on effects. Graph-based approaches are used to derive knock-on time and linearize it for optimization. Results show the optimized schedule reduces expected passenger time by 2.44% compared to the original planned schedule.
This document summarizes the key concepts and components of Gremlin's graph traversal machinery:
- Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains.
- Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine.
- The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal.
- Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.
Building Machine Learning Algorithms on Apache Spark: Scaling Out and Up with...Databricks
There are lots of reasons why you might want to implement your own machine learning algorithms on Spark: you might want to experiment with a new idea, try and reproduce results from a recent research paper, or simply to use an existing technique that isn’t implemented in MLlib.
In this talk, we’ll walk through the process of developing a new machine learning algorithm for Spark. We’ll start with the basics, by considering how we’d design a scale-out parallel implementation of our unsupervised learning technique. The bulk of the talk will focus on the details you need to know to turn an algorithm design into an efficient parallel implementation on Spark.
We’ll start by reviewing a simple RDD-based implementation, show some improvements, point out some pitfalls to avoid, and iteratively extend our implementation to support contemporary Spark features like ML Pipelines and structured query processing. We’ll conclude by briefly examining some useful techniques to complement scale-out performance by scaling our code up, taking advantage of specialized hardware to accelerate single-worker performance.
You’ll leave this talk with everything you need to build a new machine learning technique that runs on Spark.
1. The document discusses a parametric facade system inspired by the stomata membrane in plant leaves.
2. It describes the development of a geometric pattern to mimic stomata and make the facade adaptable to environmental conditions.
3. Pseudocode and functions are provided to map the geometric pattern onto a surface and divide it, with the goal of creating a parametric skin that can store water, admit light, and generate wind energy.
OLC assembly involves three main steps:
1. Overlap - Compute all overlaps between reads to construct an overlap graph
2. Layout - Bundle stretches of the overlap graph into contigs
3. Consensus - Pick the most likely nucleotide sequence for each contig by determining consensus from the underlying reads
This document outlines a talk on using category theory concepts in functional programming. It begins by introducing the definition of a category from category theory and using Scala examples to demonstrate how types and functions in Scala satisfy this definition. It then defines functors, natural transformations, and monads from category theory and provides examples showing how options, lists, and functions in Scala form a monad. It proves that the category theory definition of a monad is equivalent to the definition used in functional programming. The document suggests category theory concepts help formalize design patterns and make code more refactorable.
APassengerKnockOnDelayModelForTimetableOptimisation_beamerPeter Sels
The document presents a model for optimizing passenger travel time in train timetabling by accounting for knock-on delays. It develops a stochastic goal function to minimize expected passenger transfer time considering primary delays and knock-on effects. Graph-based approaches are used to derive knock-on time and linearize it for optimization. Results show the optimized schedule reduces expected passenger time by 2.44% compared to the original planned schedule.
This document provides an overview of MATLAB, including its common uses in engineering fields like rocket design, its basic commands and functions for mathematics, matrices, polynomials, and more. Key features of MATLAB covered include its command window, editor, predefined math functions, matrix commands using colons, reading and writing files, and basic programming statements.
The Ring programming language version 1.3 book - Part 26 of 88Mahmoud Samir Fayed
The List Class provides methods for manipulating list data in Ring. Key methods include Add() to add an item, Delete() to remove an item by index, Item() to access an item by index, FindInColumn() to search for an item in a list column, and Sort() to return a new sorted list. Lists can be concatenated using the + operator and initialized from arrays or other lists.
Mikhail Khristophorov "Introduction to Regular Expressions"LogeekNightUkraine
Regular expressions (regexes) are patterns used to match character combinations in strings. This document discusses the history and use of regexes, provides an overview of regex vocabulary including special characters, constructs, and quantifiers, and gives examples of using regexes in Java programs for tasks like validation, splitting strings, and find-and-replace operations. It concludes with examples of validating an address and parsing a properties file using regexes.
Building Machine Learning Algorithms on Apache Spark with William BentonSpark Summit
There are lots of reasons why you might want to implement your own machine learning algorithms on Spark: you might want to experiment with a new idea, try and reproduce results from a recent research paper, or simply to use an existing technique that isn’t implemented in MLlib. In this talk, we’ll walk through the process of developing a new machine learning model for Spark. We’ll start with the basics, by considering how we’d design a parallel implementation of a particular unsupervised learning technique. The bulk of the talk will focus on the details you need to know to turn an algorithm design into an efficient parallel implementation on Spark: we’ll start by reviewing a simple RDD-based implementation, show some improvements, point out some pitfalls to avoid, and iteratively extend our implementation to support contemporary Spark features like ML Pipelines and structured query processing. You’ll leave this talk with everything you need to build a new machine learning technique that runs on Spark.
This program writes a C code to shear a cuboid. It includes graphics header files and uses the Bresenham's line algorithm to draw lines. The program defines a function called 'bress' to draw lines using the Bresenham algorithm. It takes coordinates of two points as input and uses conditions on the slope to determine the increment, endpoint and direction of line drawing. This function is used to draw the individual lines of the cuboid before and after shearing.
This document discusses using digital twins and machine learning to achieve adaptation in uncertain systems. It provides an overview of enterprise system project failures and introduces digital twins as a new approach for design and control. It then describes a conceptual model and approach for developing digital twins for uncertain systems using goals, domain modeling, and agent-based modeling. Finally, it discusses research challenges in using this approach, including validation, verification, modeling expertise, efficiency, explainability and unknown unknowns.
This document provides an introduction to financial modeling in R. It begins with basic R commands for calculations, vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs, distributions, correlations, and linear regression. More advanced topics include non-linear regression, graphics packages, downloading stock data, and estimating volatility and value at risk. Practical exercises are provided to work with financial data, estimate distributions, correlations, and models.
The document defines limits and discusses different methods for solving limits as the variable approaches a number or infinity. It introduces limit notation and explains how to solve limits by substitution, using sum and product laws, and handling undefined and zero cases. When taking a limit as the variable approaches infinity, it provides approaches such as determining the limit based on the highest polynomial term, and whether the numerator or denominator increases faster for rational functions.
Description of the Reactive Collections framework for event-driven, reactive and distributed programming, and the real-time game engine use case. See the game demos on YouTube:
http://www.youtube.com/channel/UCoyqnhi_BdpLrBVMvkNIMMw
ggplot2: An Extensible Platform for Publication-quality GraphicsClaus Wilke
Talk given at the Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019, organized by the American Statistical Association and Interface Foundation of North America.
The document describes several graph layout programs including dot, neato, twopi, circo, fdp, and sfdp. These programs take graph files as input and output drawings of the graphs in various formats like PostScript, SVG, and bitmap images. The programs use different algorithms to determine the layout, such as hierarchies for dot, springs for neato and fdp, radial layouts for twopi, and circular layouts for circo. The document provides details on the command line syntax, input graph file format, and attributes that control the graph drawing output.
This document provides an overview of using R for financial modeling. It covers basic R commands for calculations, vectors, matrices, lists, data frames, and importing/exporting data. Graphical functions like plots, bar plots, pie charts, and boxplots are demonstrated. Advanced topics discussed include distributions, parameter estimation, correlations, linear and nonlinear regression, technical analysis packages, and practical exercises involving financial data analysis and modeling.
This document discusses writing a domain specific language (DSL) for data transformations using applicative functors in Scala. It introduces the concepts of Picker, Reader, and Result to parse heterogeneous data formats into a common format. Reader is defined as an applicative functor to allow combining multiple readers. Later, Reader is enhanced to take type parameters for both input and output to avoid reparsing data and support XML parsing. Type lambdas are used to make Reader work as an applicative functor.
This document discusses graphing sine and cosine functions. It defines periodic functions and notes that sine and cosine have a period of 2π. The properties of sine are described as having a domain of (-∞, ∞), range from -1 to 1, and amplitude equal to the absolute value of A. Similarly, cosine has a period of 2π, domain of (-∞, ∞) and range from -1 to 1. Translating sine and cosine functions is also discussed, noting that amplitude and period can change based on coefficients of x and constants added. Examples are provided to demonstrate stating the amplitude and period of various sine and cosine graphs.
This document provides an overview and introduction to using the statistical programming language R. It begins with basic commands for performing calculations and creating vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs and statistical distributions, correlations, linear and nonlinear regression, advanced graphics, and accessing financial data packages. The document concludes with proposing practical tasks for workshop participants to work with financial data in R.
This talk covers how to integrate D3 with SVG & Angular to create awesome visualisations, leveraging the modularity of D3 and it's data binding, with angular data binding and the reusability of directives.
Source code for this talk:
https://github.com/adamkleingit/d3-svg-angular
The document discusses recursion and functional programming concepts like immutability, iteration with looping constructs, Scheme data types like atoms and lists, primitive list functions like car, cdr, cons, null?, atom?, eq?, member?, and defines recursive functions for list operations like member?, remove, insertR, insertL, insert using a higher-order function pattern. It also covers numeric functions like addition, subtraction, multiplication, exponents defined recursively and adding tuples recursively.
This document is a lecture on advanced MATLAB methods. It discusses probability and statistics, data structures like cells and structs, images and animation, debugging techniques, and online resources. Specific topics covered include random number generation, histograms, cells vs matrices, initializing and accessing structs, figure handles, reading/writing images, creating animations, using the debugger, and the MATLAB File Exchange website.
The document contains 20 MATLAB programs demonstrating various plotting and data visualization techniques including:
- Plotting sinusoidal waves and applying half/full wave rectification
- Converting between polar and Cartesian coordinates and plotting circles
- Generating and plotting cylinder surfaces
- Using EZ functions like ezplot, ezsurf, and ezpolar to plot functions
- Taking derivatives and plotting functions and their derivatives
- Plotting noise signals and calculating rate of change over time
- Generating 2D and 3D surfaces from gridded data
- Plotting polar plots and converting to Cartesian coordinates
- Applying FFT and filtering to decompose a signal into frequency components
- Using pie charts and 3D pie plots to
An array is a collection of data that holds a fixed number of values of the same type. Arrays can be one-dimensional or multidimensional. Array elements are accessed using indices starting from 0. Arrays can be passed to functions by passing the array name, which passes the base address. Strings in C are implemented as character arrays terminated by a null character. Common string functions like strcpy(), strcat(), strlen() etc. are used to manipulate strings.
DataStax | Network Analysis Adventure with DSE Graph, DataStax Studio, and Ti...DataStax
Ride along as we use network analysis techniques to derive insights from our graph. We will begin by using exploratory analysis techniques to develop a high level understanding of our data. After gaining familiarity in the aggregate, we will select key elements of the graph for detailed inspection and graph visualization.
We will explore fundamental techniques that bridge the gap between academic network analysis concepts and pragmatic problem solving approaches for real-world property graphs at scale.
Prior network analysis expertise is not required. Source code and reproducibles will be made publicly available. Please try this at home.
About the Speaker
Bob Briody Software Engineer, DataStax
Bob is a diverse developer with over 10 years of experience across the stack. He joined DataStax as part of the Aurelius acquisition in 2015. Since then he has contributed to the design and development of DataStax Studio, with a focus on graph interaction and visualization. Bob is also a contributor to the Apache TinkerPop project.
Intro to Graph Databases Using Tinkerpop, TitanDB, and GremlinCaleb Jones
A quick overview of the history, motivation, and uses of graph modeling and graph databases in various industries. Covers a brief introduction to graph databases with an emphasis on the Tinkerpop stack and Gremlin query language. These concepts are then solidified through a hands-on lab modeling a blog engine using Titan and Gremlin.
See more at http://allthingsgraphed.com.
This document provides an overview of MATLAB, including its common uses in engineering fields like rocket design, its basic commands and functions for mathematics, matrices, polynomials, and more. Key features of MATLAB covered include its command window, editor, predefined math functions, matrix commands using colons, reading and writing files, and basic programming statements.
The Ring programming language version 1.3 book - Part 26 of 88Mahmoud Samir Fayed
The List Class provides methods for manipulating list data in Ring. Key methods include Add() to add an item, Delete() to remove an item by index, Item() to access an item by index, FindInColumn() to search for an item in a list column, and Sort() to return a new sorted list. Lists can be concatenated using the + operator and initialized from arrays or other lists.
Mikhail Khristophorov "Introduction to Regular Expressions"LogeekNightUkraine
Regular expressions (regexes) are patterns used to match character combinations in strings. This document discusses the history and use of regexes, provides an overview of regex vocabulary including special characters, constructs, and quantifiers, and gives examples of using regexes in Java programs for tasks like validation, splitting strings, and find-and-replace operations. It concludes with examples of validating an address and parsing a properties file using regexes.
Building Machine Learning Algorithms on Apache Spark with William BentonSpark Summit
There are lots of reasons why you might want to implement your own machine learning algorithms on Spark: you might want to experiment with a new idea, try and reproduce results from a recent research paper, or simply to use an existing technique that isn’t implemented in MLlib. In this talk, we’ll walk through the process of developing a new machine learning model for Spark. We’ll start with the basics, by considering how we’d design a parallel implementation of a particular unsupervised learning technique. The bulk of the talk will focus on the details you need to know to turn an algorithm design into an efficient parallel implementation on Spark: we’ll start by reviewing a simple RDD-based implementation, show some improvements, point out some pitfalls to avoid, and iteratively extend our implementation to support contemporary Spark features like ML Pipelines and structured query processing. You’ll leave this talk with everything you need to build a new machine learning technique that runs on Spark.
This program writes a C code to shear a cuboid. It includes graphics header files and uses the Bresenham's line algorithm to draw lines. The program defines a function called 'bress' to draw lines using the Bresenham algorithm. It takes coordinates of two points as input and uses conditions on the slope to determine the increment, endpoint and direction of line drawing. This function is used to draw the individual lines of the cuboid before and after shearing.
This document discusses using digital twins and machine learning to achieve adaptation in uncertain systems. It provides an overview of enterprise system project failures and introduces digital twins as a new approach for design and control. It then describes a conceptual model and approach for developing digital twins for uncertain systems using goals, domain modeling, and agent-based modeling. Finally, it discusses research challenges in using this approach, including validation, verification, modeling expertise, efficiency, explainability and unknown unknowns.
This document provides an introduction to financial modeling in R. It begins with basic R commands for calculations, vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs, distributions, correlations, and linear regression. More advanced topics include non-linear regression, graphics packages, downloading stock data, and estimating volatility and value at risk. Practical exercises are provided to work with financial data, estimate distributions, correlations, and models.
The document defines limits and discusses different methods for solving limits as the variable approaches a number or infinity. It introduces limit notation and explains how to solve limits by substitution, using sum and product laws, and handling undefined and zero cases. When taking a limit as the variable approaches infinity, it provides approaches such as determining the limit based on the highest polynomial term, and whether the numerator or denominator increases faster for rational functions.
Description of the Reactive Collections framework for event-driven, reactive and distributed programming, and the real-time game engine use case. See the game demos on YouTube:
http://www.youtube.com/channel/UCoyqnhi_BdpLrBVMvkNIMMw
ggplot2: An Extensible Platform for Publication-quality GraphicsClaus Wilke
Talk given at the Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019, organized by the American Statistical Association and Interface Foundation of North America.
The document describes several graph layout programs including dot, neato, twopi, circo, fdp, and sfdp. These programs take graph files as input and output drawings of the graphs in various formats like PostScript, SVG, and bitmap images. The programs use different algorithms to determine the layout, such as hierarchies for dot, springs for neato and fdp, radial layouts for twopi, and circular layouts for circo. The document provides details on the command line syntax, input graph file format, and attributes that control the graph drawing output.
This document provides an overview of using R for financial modeling. It covers basic R commands for calculations, vectors, matrices, lists, data frames, and importing/exporting data. Graphical functions like plots, bar plots, pie charts, and boxplots are demonstrated. Advanced topics discussed include distributions, parameter estimation, correlations, linear and nonlinear regression, technical analysis packages, and practical exercises involving financial data analysis and modeling.
This document discusses writing a domain specific language (DSL) for data transformations using applicative functors in Scala. It introduces the concepts of Picker, Reader, and Result to parse heterogeneous data formats into a common format. Reader is defined as an applicative functor to allow combining multiple readers. Later, Reader is enhanced to take type parameters for both input and output to avoid reparsing data and support XML parsing. Type lambdas are used to make Reader work as an applicative functor.
This document discusses graphing sine and cosine functions. It defines periodic functions and notes that sine and cosine have a period of 2π. The properties of sine are described as having a domain of (-∞, ∞), range from -1 to 1, and amplitude equal to the absolute value of A. Similarly, cosine has a period of 2π, domain of (-∞, ∞) and range from -1 to 1. Translating sine and cosine functions is also discussed, noting that amplitude and period can change based on coefficients of x and constants added. Examples are provided to demonstrate stating the amplitude and period of various sine and cosine graphs.
This document provides an overview and introduction to using the statistical programming language R. It begins with basic commands for performing calculations and creating vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs and statistical distributions, correlations, linear and nonlinear regression, advanced graphics, and accessing financial data packages. The document concludes with proposing practical tasks for workshop participants to work with financial data in R.
This talk covers how to integrate D3 with SVG & Angular to create awesome visualisations, leveraging the modularity of D3 and it's data binding, with angular data binding and the reusability of directives.
Source code for this talk:
https://github.com/adamkleingit/d3-svg-angular
The document discusses recursion and functional programming concepts like immutability, iteration with looping constructs, Scheme data types like atoms and lists, primitive list functions like car, cdr, cons, null?, atom?, eq?, member?, and defines recursive functions for list operations like member?, remove, insertR, insertL, insert using a higher-order function pattern. It also covers numeric functions like addition, subtraction, multiplication, exponents defined recursively and adding tuples recursively.
This document is a lecture on advanced MATLAB methods. It discusses probability and statistics, data structures like cells and structs, images and animation, debugging techniques, and online resources. Specific topics covered include random number generation, histograms, cells vs matrices, initializing and accessing structs, figure handles, reading/writing images, creating animations, using the debugger, and the MATLAB File Exchange website.
The document contains 20 MATLAB programs demonstrating various plotting and data visualization techniques including:
- Plotting sinusoidal waves and applying half/full wave rectification
- Converting between polar and Cartesian coordinates and plotting circles
- Generating and plotting cylinder surfaces
- Using EZ functions like ezplot, ezsurf, and ezpolar to plot functions
- Taking derivatives and plotting functions and their derivatives
- Plotting noise signals and calculating rate of change over time
- Generating 2D and 3D surfaces from gridded data
- Plotting polar plots and converting to Cartesian coordinates
- Applying FFT and filtering to decompose a signal into frequency components
- Using pie charts and 3D pie plots to
An array is a collection of data that holds a fixed number of values of the same type. Arrays can be one-dimensional or multidimensional. Array elements are accessed using indices starting from 0. Arrays can be passed to functions by passing the array name, which passes the base address. Strings in C are implemented as character arrays terminated by a null character. Common string functions like strcpy(), strcat(), strlen() etc. are used to manipulate strings.
DataStax | Network Analysis Adventure with DSE Graph, DataStax Studio, and Ti...DataStax
Ride along as we use network analysis techniques to derive insights from our graph. We will begin by using exploratory analysis techniques to develop a high level understanding of our data. After gaining familiarity in the aggregate, we will select key elements of the graph for detailed inspection and graph visualization.
We will explore fundamental techniques that bridge the gap between academic network analysis concepts and pragmatic problem solving approaches for real-world property graphs at scale.
Prior network analysis expertise is not required. Source code and reproducibles will be made publicly available. Please try this at home.
About the Speaker
Bob Briody Software Engineer, DataStax
Bob is a diverse developer with over 10 years of experience across the stack. He joined DataStax as part of the Aurelius acquisition in 2015. Since then he has contributed to the design and development of DataStax Studio, with a focus on graph interaction and visualization. Bob is also a contributor to the Apache TinkerPop project.
Intro to Graph Databases Using Tinkerpop, TitanDB, and GremlinCaleb Jones
A quick overview of the history, motivation, and uses of graph modeling and graph databases in various industries. Covers a brief introduction to graph databases with an emphasis on the Tinkerpop stack and Gremlin query language. These concepts are then solidified through a hands-on lab modeling a blog engine using Titan and Gremlin.
See more at http://allthingsgraphed.com.
Graph Processing with Apache TinkerPopJason Plurad
This document discusses Apache TinkerPop, a graph computing framework. It provides an overview of TinkerPop and the graph landscape, describes common graph domains and the Gremlin property graph model. It also demonstrates hands-on examples with Titan and Spark/Giraph and discusses using graphs to analyze dependency management and the NPM registry. The document emphasizes that TinkerPop allows seamless use of OLTP and OLAP graphs via Gremlin and supports graph-based thinking for multi-model data.
Recommendation and personalization systems are an important part of many modern websites. Graphs provide a natural way to represent the behavioral data that is the core input to many recommendation algorithms. Thomas Pinckney and his colleagues at Hunch (recently acquired by eBay) built a large scale recommendation system, and then ported the technology to eBay. Thomas will be discussing how his team uses Cassandra to provide the high I/O storage of their fifty billion edge graphs and how they generate new recommendations in real time as users click around the site.
Bloor Research & DataStax: How graph databases solve previously unsolvable bu...DataStax
This webinar covered graph databases and how they can solve problems that were previously difficult for traditional databases. It included presentations on why graph databases are useful, common use cases like recommendations and network analysis, different types of graph databases, and a demonstration of the DataStax Enterprise graph database. There was also a question and answer session where attendees could ask about graph databases and DataStax Enterprise graph.
The document discusses graphs and graph databases. It introduces the concept of property graphs and how they can intuitively model complex relationships between entities. It discusses how graph traversal enables expressive querying and numerous analyses of graph data. The document uses examples involving Greek mythology to illustrate graph concepts and traversal queries.
This document discusses building a knowledge graph using DSE Graph to answer questions about employees, projects, customers, and skills. It demonstrates creating a graph schema and indexes, inserting sample data as vertices and edges, and using Gremlin traversals to query the graph. Examples include finding employees with required expertise not working on a project, employees who worked for a customer, and the skills most requested by customers. The implementation is developed using DataStax Studio with features like content assist, profiling, and Markdown documentation. Future extensions mentioned include adding more connection types like events and blog posts.
This document discusses using HBase for online transaction processing (OLTP) workloads. It provides background on SQL-on-Hadoop and transaction processing with snapshot isolation. It then describes challenges in adding transactions directly to HBase, including using additional system tables to coordinate transactions. Examples are given for implementing transactions in HBase, along with issues like rollback handling. Finally, it discusses using SQL interfaces like Apache Phoenix or Drill on top of HBase, as well as open questions around the future of OLTP and OLAP processing on Hadoop versus traditional databases.
Graph databases are a solution for storing highly scalable semi-structured connected data. Apache Tinkerpop provides a unified API for graph databases to avoid vendor-specific code. Tinkerpop includes Gremlin for querying graphs and integrates with Titan, a scalable distributed graph database that can use backends like BerkeleyDB, HBase, or Cassandra for storage. This allows Titan graphs to scale linearly based on storage needs.
Adding Value through graph analysis using Titan and FaunusMatthias Broecheler
In this presentation we discuss how graph analysis can add value to your data and how to use open source tools like Titan and Faunus to build scalable graph processing systems.
This presentation gives an update on the development status of Titan and Faunus with a preview of what is to come.
A graph is a data structure consisting of vertices and edges connecting the vertices. Graphs can be directed or undirected. Depth-first search (DFS) and breadth-first search (BFS) are algorithms for traversing graphs by exploring neighboring vertices. DFS uses a stack and explores as far as possible along each branch before backtracking, while BFS uses a queue and explores all neighbors at each depth level first before moving to the next level.
Graphs are everywhere! Distributed graph computing with Spark GraphXAndrea Iacono
This document discusses GraphX, a graph processing system built on Apache Spark. It defines what graphs are, including vertices and edges. It explains that GraphX uses Resilient Distributed Datasets (RDDs) to keep data in memory for iterative graph algorithms. GraphX implements the Pregel computational model where each vertex can modify its state, receive and send messages to neighbors each superstep until halting. The document provides examples of graph algorithms and notes when GraphX is well-suited versus a graph database.
Cassandra Summit - What's New In Apache TinkerPop?Stephen Mallette
The document provides an overview of Apache TinkerPop, an open source graph computing framework. It discusses new features in recent versions of TinkerPop, including support for both imperative and declarative querying in Gremlin 3.0. It also demonstrates how to load and query graph data stored in HDFS using TinkerPop and Spark, and how to visualize subgraphs in Gephi.
The document compares Neo4j, Titan, and Cassandra graph databases. It provides details on each database such as Neo4j using the Cypher query language, Cassandra being highly distributed and able to scale linearly, and Titan running on Cassandra or HBase but not supporting Cypher queries. It also gives a 15 point comparison of Cassandra vs Neo4j and examples of querying the same data in Gremlin, Cypher, and SQL. The conclusion recommends a graph database like Neo4j for recommendation queries and only using Titan for very large graphs or high loads.
This presentation was given on January 17, 2016 at the GraphDay conference in Austin, Texas. The slides demonstrate the use of wave dynamics in graph structures. Moreover, they demonstrate how to implement quantum processes on graph structures.
There is an associated article available at http://arxiv.org/abs/1511.06278 (Quantum Walks with Gremlin).
Real Time Business Intelligence with Cassandra, Kafka and Hadoop - A Real Sto...DataStax
What did I sell yesterday and how much of my plan did I fulfill today? How do my clients use our offer? What configuration combinations are in demand and what trends are emerging? How can I improve the user experience? These and other questions are frequently asked by board members and stakeholders and must be answered within a short period of time. Especially in companies that provide configurable products, it is important to support the product and pricing managers in short-term and competition-related matters with all the important data in a timely manner. In our use case, Cassandra, Kafka and Flink will take up this challenge. In this session, we will present a reference architecture based on selected use cases and demonstrate what applications arise for companies. We also take a closer look to information privacy and give some words about data visualisation.
About the Speakers
Alexandra Klimova Big Data Architect, Allianz Deutschland AG
Alexandra has 10 years of experience in both programing and operations. For the last 4 years she has focused on design and integration of Big Data Systems into enterprise platforms. She is working on data processing pipelines, distributed systems, realtime processing and data science. Alexandra holds a degree in Computer Science from the Technical University Munich. She is certified Hortonworks Hadoop Trainer and Big Data Architect at metafinanz.
Dominique Ronde Big Data Architect, Allianz Deutschland AG
Dominique Ronde is Big Data Architect at Allianz Deutschland AG and focused on the cassandra plattform. He also enjoys the part of data analytics with Flink and Spark. As a real java nerd since 2002 Dominique is familiar with the programming part, too. He is certified DataStax Solution Architect
DataStax | Graph Data Modeling in DataStax Enterprise (Artem Chebotko) | Cass...DataStax
The graph data model is the most versatile and flexible data model supported by DataStax Enterprise. With the right of choice comes the responsibility to choose. This talk provides a necessary background on property graphs, their usability and common use cases, and dives deeper into techniques for graph data modeling. We generalize and demonstrate how graph schema design fits into the conventional conceptual-logical-physical schema design framework using concrete examples and visual diagrams. Finally, we discuss how to optimize a graph data model for performance.
About the Speaker
Artem Chebotko Solutions Architect, DataStax
Dr. Artem Chebotko is a Solutions Architect at DataStax. His core expertise is in data modeling, data management, data mining, and data analytics. For over 10 years, he has been leading and participating in R&D projects on NoSQL, Graph, XML, Relational, and Provenance databases. He is the inventor of the Big Data Modeling Methodology for Apache Cassandra and the author of over 50 research and technical papers published in international journals and conference proceedings.
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This document provides a summary of MATLAB fundamentals including:
1. Basics such as defining and changing variables, arithmetic operations, elementary functions, complex numbers, constants, and numerics.
2. Graphics and plotting capabilities including different plot types.
3. Programming methods like functions, relational and logical operations, control structures like if/else statements and loops, and special topics like polynomials, interpolation, differential equations, and optimization.
4. Descriptive statistics, discrete math functions, and random number generation.
- The lecture covered graphics math topics including homogeneous coordinates and projective transformations.
- Homework 2 was due and an in-class quiz was given. Details on Project 1 were announced.
- The final exam date was moved and last class will be a review session. Daily quiz solutions will be provided.
- Office hours and last lecture topics were reviewed to introduce the current lecture on further graphics math concepts.
Grouped data frames allow dplyr functions to manipulate each group separately. The group_by() function creates a grouped data frame, while ungroup() removes grouping. Summarise() applies summary functions to columns to create a new table, such as mean() or count(). Join functions combine tables by matching values. Left, right, inner, and full joins retain different combinations of values from the tables.
The document discusses key concepts related to rates of change:
- Average rate of change is defined as the change in y over the change in x. Instantaneous rate of change is the limit of this ratio as the change approaches zero.
- Average velocity is the total distance traveled divided by the total time. Instantaneous velocity is the limit of the change in position over the change in time as the time interval approaches zero.
- The slope of a curve at a point equals the slope of the tangent line at that point, which can be determined by taking the limit of the difference quotient as the change approaches zero.
Differentiation full detail presentationxavev33334
This document provides an introduction to differentiation. It explains that differentiation is used to find the gradient function of a formula, which gives the gradient at any point on a curve. Examples are provided of differentiating simple polynomial functions and more complex expressions. The document also demonstrates how to find the gradient of a curve at a given point, find where two curves intersect, and determine the equation of a tangent line to a curve at a specified point.
Computer Graphics in Java and Scala - Part 1bPhilip Schwarz
First see the Scala program from Part 1 translated into Java.
Then see the Scala program modified to produce a more intricate drawing.
Java Code: https://github.com/philipschwarz/computer-graphics-50-triangles-java
Scala Code: https://github.com/philipschwarz/computer-graphics-chessboard-with-a-great-many-squares-scala
Here are the key steps to find the instantaneous rate of change using a graphing calculator:
1. Graph the function over the appropriate domain.
2. Use the arrow keys to move the cursor to the point where you want to find the instantaneous rate of change.
3. Press the TRACE button and select the tangent option.
4. The calculator will display the slope of the tangent line, which is the instantaneous rate of change at that point.
5. For example, if finding the IROC at x=1 for the function f(x) = x3, you would:
a) Graph f(x) = x3
b) Use arrows to move cursor
ImplementDijkstra’s algorithm using the graph class you implemente.pdfgopalk44
Implement
Dijkstra’s algorithm using the graph class you implemented in
HW
#5 and
a priority
queue
you have
implemented
earlier this semester
.
Y
ou
r
program will read the graph from a text file like
what we did in
HW
#
5
. You can use
graph.txt from HW
#5
to test your program. Make the header of
the method as Dijsktra(G, v), where v is the starting vertex.
Further, write a method that prints the
shortest path betw
een any two vertices. Example: s
hortestPath(G,
x
,
y
).
Write a main method t
o test
your program.
Instructions:
1.
Y
ou can use C#
or Java programming languages. No other language is allowed or accepted
HW#5 Question
Q1)
(6
0 points)
Implement a
graph ADT by defining a class “Graph” with the operations
below. You
r
ADT should accept either a directed or undirected graph.
isDirect():
tests if the graph is a digraph
. Returns Boolean value.
adjacent(v,
u): tests whether there is an edge from the vertex v to u. returns Boolean value.
n
eighbors(v): returns the list of all vertices that are a destination of an edge from v.
addVertex(v): adds the vertex v to the graph if it is not already in the
graph, otherwise an error
message to be thrown.
removeVertex(v): removes vertex v from the graph, if it is there.
When a vertex is removed, all
edges associated with that vertex should be deleted as well.
addEdge(v,
u): adds the edge that starts from v and
ends at u.
addEdge(v, u, w):
adds the edge t
hat starts from v and ends at u with weight w.
removeEdge(v,
u): remove the edge that connects v and u.
getWeight
(v,
u): returns the weight of the edge from v to u.
setWeight
(v,
u): sets the weight of the edge
from v to u.
isEmpty(): check whether the graph is empty or not.
isComplete(): checks whether the graph is complete or not.
vertices():returns the list of vertices in the graph (i.e
.
, array, vector,..)
edges(): returns the list of edges in the graph.
d
egree(v): return the degree of vertex v.
size(): returns the number of vertices in the graph.
nEdges(): returns the number of edges in the graph.
c
lear(): Reinitializes the graph to be empty, freeing any heap storage.
vertexExists(v): tests whether a verte
x is in the graph or not. Returns true or false.
p
rint(): displays the list of vertices, edges and their weights if the graph is weighted.
Your ADT should contain at least these constructors:
Graph(), creates a graph with zero vertices.
Graph(n), creates
a graph with n vertices.
Graph(n, digraph), where digraph is a Boolean value if true means
a
directed graph.
Q2) (50 points)
Write a main method that r
ead
s
the graph.txt file that contains the information of
directed weighted
graph.
The file is formatted
as the following:
First line is the number of vertices in the graph.
Second line contains the vertices in the graph.
Each
following line contains the edges and the weights. For example: a b 4, means an ed
ge from a to be with
weight = 4.
After
reading the f
ile and creating
the graph perform the following operations in the same orde.
The document provides an overview of Calculus I taught by Professor Matthew Leingang at New York University. It outlines key topics that will be covered in the course, including different classes of functions, transformations of functions, and compositions of functions. The first assignments are due on January 31 and February 2, with first recitations on February 3. The document uses examples to illustrate concepts like linear functions, other polynomial functions, and trigonometric functions. It also explains how vertical and horizontal shifts can transform the graph of a function.
Lesson 2: A Catalog of Essential Functions (slides)Matthew Leingang
This document provides an overview of different types of functions including: linear, polynomial, rational, power, trigonometric, and exponential functions. It discusses representing functions verbally, numerically, visually, and symbolically. Key topics covered include transformations of functions through shifting graphs vertically and horizontally, as well as composing multiple functions.
* Graph functions using vertical and horizontal shifts.
* Graph functions using reflections about the x-axis and the y-axis.
* Determine whether a function is even, odd, or neither from its graph.
* Graph functions using compressions and stretches.
* Combine transformations.
This document provides an overview of abstract machines for evaluating lambda calculus expressions and arithmetic expressions. It discusses the SECD machine, which evaluates lambda calculus using a stack, environment, code, and dump. It also discusses the K machine and tail call optimization. The document explains evaluation schemes and transitions for abstract machines and provides examples of evaluating expressions using different machines.
This document provides an overview of JavaFX and its capabilities for creating rich user interfaces. It discusses that JavaFX offers developers an attractive combination of cross-platform support, powerful features, and high performance. JavaFX allows creating interfaces for desktop, browser, and mobile applications using the same codebase and tools.
Website Designing Company is fastest growing company in the IT market in the world for the website design and website layout. we are best website designing company in India as well as in USA we are based in Noida and Delhi NCR. Website designing company is powered by Css Founder.com
The document discusses convolution and its applications in imaging. Convolution describes how one function is modified by another function. In imaging, the point spread function (PSF) describes how a point object is blurred by the imaging system. The image formation process can be described as the convolution of the object with the PSF plus noise. Fourier analysis is useful because convolution in the spatial domain is equivalent to multiplication in the frequency domain. This allows filtering techniques to be used to modify images. Examples are provided of using Fourier analysis to determine the PSF and modulation transfer function of an imaging system.
1. Partial derivatives describe how a function changes with respect to one variable while holding other variables constant. The partial derivative of Z with respect to x is denoted as ∂Z/∂x or fχ.
2. Optimization problems in calculus involve finding the maximum or minimum values of functions, which can be used to determine the best way to do something.
3. A function has a global/absolute maximum at c if it is greater than or equal to the function values at all other points, and a global/absolute minimum if it is less than or equal to all other points.
This document summarizes a presentation about graph databases and the Gremlin graph traversal language. It begins with introductions to property graphs, graph databases, and TinkerPop's Gremlin language. It then demonstrates various Gremlin traversal techniques like adding and updating vertices and edges, traversing relationships, filtering, and computing metrics. Later sections discuss linking graph data to the Linked Data cloud and envisioning a "Global Graph" of metadata on the internet.
This document discusses monads and continuations in functional programming. It provides examples of using monads like Option and List to handle failure in sequences of operations. It also discusses delimited continuations as a low-level control flow primitive that can implement exceptions, concurrency, and suspensions. The document proposes using monads to pass implicit state through programs by wrapping computations in a state transformer (ST) monad.
A function of two variables is defined similar to a function of one variable. It has a domain (in the plane) and a range. The graph of such a function is a surface in space and we try to sketch some.
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
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1. Gremlin’s Graph Traversal Machinery
Dr. Marko A. Rodriguez
Director of Engineering at DataStax, Inc.
Project Management Committee, Apache TinkerPop
http://tinkerpop.apache.org
2. f : X ! X
The function f is a process that maps a structure of type X to a structure of type X.
3. f(x1) = x2
The function f maps the object x1 (from the set of X) to the object x2 (from the set of X).
x1 2 X x2 2 X
7. 90°
A traverser wraps a value of type V.
class Traverser<V> {
V value;
}
class Traverser<V> {
V value;
}
8. 90°
The step maps an integer traverser to an integer traverser.
class Traverser<V> {
V value;
}
class Traverser<V> {
V value;
}
Traverser<Integer> Traverser<Integer>
9. 90°
A traverser of with a rotation of 0° becomes a traverser with a rotation of 90°.
Traverser(0) Traverser(90)
class Traverser<V> {
V value;
}
class Traverser<V> {
V value;
}
14. A traverser can have a bulk which denotes how many V values it represents.
90°
class Traverser<V> {
V value;
long bulk;
}
class Traverser<V> {
V value;
long bulk;
}
15. 4
4
Bulking groups identical traversers to reduce the number of evaluations of a step.
90°
class Traverser<V> {
V value;
long bulk;
}
class Traverser<V> {
V value;
long bulk;
}
16. A variegated stream of input traversers yields a variegated stream of output traversers.
90°
17. 1
2
1 1
1
2
Bulking can reduce the size of the stream.
90°
class Traverser<V> {
V value;
long bulk;
}
class Traverser<V> {
V value;
long bulk;
}
55. g.V().has(“name”,”gremlin”).
out(“knows”).values(“age”).
groupCount()
one graph to many vertices
(flatMap)
one vertex
to that vertex or no vertex
(filter)
one vertex
to many friend vertices
(flatMap)
one vertex to
one age value
(map)
many age values
to an age distribution
(map — reducer)
?
…
37 [37:2, 41:1,
24:1, 35:4]37
37
24
35
35
35
35 41
name=gremlin
57. a b c
a b c
Traversal creation via
step composition
Step parameterization via
traversal and constant nesting
a().b().c()
a(b().c()).d(x)d(x)
function
com
position
function
nesting
fluent m
ethods
m
ethod
argum
ents
Any language that supports function composition and function nesting can host Gremlin.
Gremlin Traversal Language
58. class Traverser<V> {
V value;
long bulk;
}
class Step<S,E> {
Traverser<E> processNextStart();
}
f(x)
class Traversal<S,E> implements Iterator<E> {
E next();
Traverser<E> nextTraverser();
}
The fundamental constructs of Gremlin’s machinery.
Gremlin Traversal Machine
interface TraversalStrategy {
void apply(Traversal traversal);
Set<TraversalStrategy> applyPrior();
Set<TraversalStrategy> applyPost();
}
a db c
a de
≣
63. Python 2.7.2 (default, Oct 11 2012, 20:14:37)
[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from gremlin_python.structure.graph import Graph
>>> from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
Gremlin-Python
CPython
64. Python 2.7.2 (default, Oct 11 2012, 20:14:37)
[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from gremlin_python.structure.graph import Graph
>>> from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
>>> graph = Graph()
>>> g = graph.traversal().withRemote(DriverRemoteConnection('ws://localhost:8182','g'))
Gremlin-Python
DriverRemoteConnection
CPython
65. Python 2.7.2 (default, Oct 11 2012, 20:14:37)
[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from gremlin_python.structure.graph import Graph
>>> from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
>>> graph = Graph()
>>> g = graph.traversal().withRemote(DriverRemoteConnection('ws://localhost:8182','g'))
# nested traversal with Python slicing and attribute interception extensions
>>> g.V().hasLabel("person").repeat(both()).times(2).name[0:2].toList()
[u'marko', u'marko']
Gremlin-Python
Bytecode
DriverRemoteConnection
Gremlin Traversal Machine
CPython JVM
66. Python 2.7.2 (default, Oct 11 2012, 20:14:37)
[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from gremlin_python.structure.graph import Graph
>>> from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
>>> graph = Graph()
>>> g = graph.traversal().withRemote(DriverRemoteConnection('ws://localhost:8182','g'))
# nested traversal with Python slicing and attribute interception extensions
>>> g.V().hasLabel("person").repeat(both()).times(2).name[0:2].toList()
[u'marko', u'marko']
# a complex, nested multi-line traversal
>>> g.V().match(
... as_(“a”).out("created").as_(“b”),
... as_(“b”).in_(“created").as_(“c”),
... as_(“a”).out("knows").as_(“c”)).
... select("c").
... union(in_(“knows"),out("created")).
... name.toList()
[u'ripple', u'marko', u'lop']
>>>
Gremlin-Python
Bytecode
DriverRemoteConnection
Gremlin Traversal Machine
CPython JVM
67. Cypher
Bytecode
Distinct query languages (not only Gremlin language variants) can generate bytecode for
evaluation by any OLTP/OLAP TinkerPop-enabled graph system.
Gremlin Traversal Machine
71. Most OLTP graph systems have a traversal strategy that combines
[V,has*]-sequences into a single global index-based flatMap-step.
g.V().has(“name”,”gremlin”).
out(“knows”).values(“age”).
groupCount()
one graph to many vertices using index lookup
(flatMap)
GraphStepStrategy
one graph to many vertices
(flatMap)
one vertex to that vertex or no vertex
(filter)
?
…
compiles
optimizes
name=gremlin
DataStax
Enterprise Graph
72. Most OLAP graph systems have a traversal strategy that bypasses Traversal semantics
and implements reducers using the native API of the system.
g.V().count()
one graph to long
(map — reducer)
rdd.count() 12,146,934
compiles
one graph to many vertices
(flatMap)
many vertices to long
(map — reducer)
… 12,146,934
optimizes
SparkInterceptorStrategy
…
73. Physical Machine
DataProgram Traversal
Heap/DiskMemory Memory
Memory/Graph System
Physical Machine
Java
Virtual Machine
bytecode
steps
DataProgram
Memory/DiskMemory
Physical Machine
instructions
Java
Virtual Machine
Gremlin
Traversal Machine
From the physical computing machine to the Gremlin traversal machine.
75. Stakeholders
Application Developers
One query language for
all OLTP/OLAP systems.
GremlinG = (V, E)
Real-time and analytic queries are represented in Gremlin.
Graph
Database
OLTP
Graph
Processor
OLAP
77. Stakeholders
Application Developers
One query language for
all OLTP/OLAP systems.
No vendor lock-in.
Gremlin is embedded in
the developer’s language.
Iterator<String> result =
g.V().hasLabel(“person”).
order().by(“age”).
limit(10).values(“name”)
vs.
ResultSet result = statement.executeQuery(
“SELECT name FROM People n” +
“ ORDER BY age n” +
“ LIMIT 10”)
Grem
lin-Java
SQL
in
Java
No “fat strings.” The developer writes their graph database/processor
queries in their native programming language.
78. Stakeholders
Language Providers
Gremlin Language Variant
Distinct Query Language
Easy to generate bytecode.
GraphTraversal.getMethods()
.findAll { GraphTraversal.class == it.returnType }
.collect { it.name }
.unique()
.each {
pythonClass.append(
""" def ${it}(self, *args):
self.bytecode.add_step(“${it}”, *args)
return self
“””)}
Gremlin-Python’s source code is
programmatically generated using Java reflection.
79. Stakeholders
Language Providers
Gremlin Language Variant
Distinct Query Language
Easy to generate bytecode.
Bytecode executes against
TinkerPop-enabled systems.
Language providers write a translator for the Gremlin traversal machine,
not a particular graph database/processor.
DataStax
Enterprise Graph
80. Graph
Database
OLTP
Graph
Processor
OLAP
Stakeholders
Language Providers
Gremlin Language Variant
Distinct Query Language
Easy to generate bytecode.
Bytecode executes against
TinkerPop-enabled systems.
Provider can focus on design,
not evaluation.
Gremlin Traversal Machine
The language designer does not have to concern themselves with
OLTP or OLAP execution. They simply generate bytecode and the
Gremlin traversal machine handles the rest.
82. Provider supports all
provided languages.
Easy to implement core
interfaces.
Graph System Providers
Stakeholders
OLAP Provider
OLTP Provider
The provider automatically supports all query languages
that have compilers that generate Gremlin bytecode.
83. OLTP providers can leverage
existing OLAP systems.
Provider supports all
provided languages.
Easy to implement core
interfaces.
Graph System Providers
Stakeholders
OLAP Provider
OLTP Provider
DSE Graph leverages SparkGraphComputer for OLAP processing.
DataStax
Enterprise Graph
84. Stakeholders
Language Providers
Gremlin Language Variant
Distinct Query Language
Application Developers Graph System Providers
OLAP Provider
OLTP Provider
One query language for
all OLTP/OLAP systems.
No vendor lock-in.
Gremlin is embedded in
the developer’s language.
Easy to generate bytecode.
Bytecode executes against
TinkerPop-enabled systems.
Provider can focus on design,
not evaluation.
Easy to implement core
interfaces.
Provider supports all
provided languages.
OLTP providers can leverage
existing OLAP systems.