The DBMS market trends focused on the Graph DBMS. The benefit of the Graph Database and its forecasted the growth rate. The Advice from the renowned market research institute.
A Talk on the Graph Database with tutorials
Introduction to the Graph databases and Cypher Query Language
Comparison of the SQL and the Cypher implementations
Introduction to Nebula Graph, an Open-Source Distributed Graph DatabaseNebula Graph
This deck introduces Nebula Graph, the open-source distributed graph database, in a thorough manner. The agenda includes:
1. The founder and CEO, Sherman Ye, who is a graph database forerunner once working for Facebook and Ant Financial
2. The development team of Nebula Graph, who collectively has years of experience in the graph database field
3. What is a graph database
4. Why Nebula Graph is open sourced
5. The architecture of Nebula Graph
6. The advantages of Nebula Graph as a distributed graph database:
1) In architecture - shared-nothing distributed architecture and the separation of storage and computation for real horizontal scalability
2) In data amount that can be processed - From one of our users in their production environment: 150TB, one trillion edges/connections, an hourly update of 10 billion connections
3) In performance - Meituan, Tencent Cloud, and 360 Digitech have all conducted benchmarking test of Nebula Graph against other competitors such as Neo4j, Dgraph, and JanusGraph. You can see the results in the deck.
7. Adopters. Currently Nebula Graph has been deployed by internet giants like Tencent, Meituan, and Xiaohongshu in their production environments for various use cases: real-time recommendation, risk control, knowledge graph, etc.
8. Roadmap of Nebula Graph
Nebula Graph nMeetup in Shanghai - Meet with Graph Technology EnthusiastsNebula Graph
This is a speech given by Nebula Graph during the offline meetup with a bunch of graph technology enthusiasts. The slides mainly include the following info:
1. A brief introduction to the graph theory and graph database category
2. The Nebula Graph team's thoughts on the graph technology and the development of the graph database industry in recent years, including advantages and challenges
3. The architecture of Nebula Graph based on the thoughts
4. Q&A
The DBMS market trends focused on the Graph DBMS. The benefit of the Graph Database and its forecasted the growth rate. The Advice from the renowned market research institute.
A Talk on the Graph Database with tutorials
Introduction to the Graph databases and Cypher Query Language
Comparison of the SQL and the Cypher implementations
Introduction to Nebula Graph, an Open-Source Distributed Graph DatabaseNebula Graph
This deck introduces Nebula Graph, the open-source distributed graph database, in a thorough manner. The agenda includes:
1. The founder and CEO, Sherman Ye, who is a graph database forerunner once working for Facebook and Ant Financial
2. The development team of Nebula Graph, who collectively has years of experience in the graph database field
3. What is a graph database
4. Why Nebula Graph is open sourced
5. The architecture of Nebula Graph
6. The advantages of Nebula Graph as a distributed graph database:
1) In architecture - shared-nothing distributed architecture and the separation of storage and computation for real horizontal scalability
2) In data amount that can be processed - From one of our users in their production environment: 150TB, one trillion edges/connections, an hourly update of 10 billion connections
3) In performance - Meituan, Tencent Cloud, and 360 Digitech have all conducted benchmarking test of Nebula Graph against other competitors such as Neo4j, Dgraph, and JanusGraph. You can see the results in the deck.
7. Adopters. Currently Nebula Graph has been deployed by internet giants like Tencent, Meituan, and Xiaohongshu in their production environments for various use cases: real-time recommendation, risk control, knowledge graph, etc.
8. Roadmap of Nebula Graph
Nebula Graph nMeetup in Shanghai - Meet with Graph Technology EnthusiastsNebula Graph
This is a speech given by Nebula Graph during the offline meetup with a bunch of graph technology enthusiasts. The slides mainly include the following info:
1. A brief introduction to the graph theory and graph database category
2. The Nebula Graph team's thoughts on the graph technology and the development of the graph database industry in recent years, including advantages and challenges
3. The architecture of Nebula Graph based on the thoughts
4. Q&A
Dataviz presentation at ThingsKamp2015 IstanbulCédric Lombion
Dataviz presentation at ThingsKamp2015 Istanbul. Intended for newcomers to information visualisation, with the test of the first prototype of a dataviz card game.
Restructuring and transforming attributes is often an important part of data preparation. Here are tips for managing and validating attributes, plus examples of new functionality that makes it easier than ever to work with date and time.
MuseoTorino, first italian project using a GraphDB, RDFa, Linked Open Data21Style
MuseoTorino, is the first italian project using Web 3.0 tecnologies. NOSQL-GraphDB (Neo4J), RDFa, Linked Open Data.
MuseoTorino is a 21style (www.21-style.com) project for the municipality of Torino, Italy.
These slides come from CodeMotion, the best Italian conference for developers and IT entusiast !
Karnataka Geospatial Experience FME World Tour 2017 IndiaRaghavendran S
FME World Tour 2017, India
Presentation from FME magicians. Inspiring stories of FME wizardry and tips for brewing powerful data workflows.
Presentation by Mr H Hemanth KumarPrincipal Scientific OfficerPrincipal Investigator – NRDMS, KSSDI & VIS on Geospatial Technologies for informed decision making – Karnataka Experience
Finding Insights In Connected Data: Using Graph Databases In JournalismWilliam Lyon
When dealing with datasets, journalists have many options to choose from when moving beyond Excel. Usually the first step is using a relational (or SQL) database. While a relational database can be a good choice for some datasets, data analysts today turn to new tools to gain deeper insight. This talk will show how we can use a graph database to analyze highly connected data using examples from U.S. Congressional data and political email archives. Using the U.S. Congress data, we’ll show you how to explore the dataset using Cypher, the Neo4j query language, to discover legislator activity including bill sponsorship and voting activity. Building up our knowledge of Cypher as we progress, we’ll show how you can use principles from social network analysis to find influential legislators and discover what topics legislators have influence over. Finally, we will examine how to draw insights from the Hillary Clinton email dataset, released as part of a FOIA request earlier this year. We will explore this dataset as a graph of interactions among users, answering questions like: Who is communicating with Hillary the most? What are the topics of these emails? You’ll learn how to visualize these using the Neo4j browser to quickly make sense of the data as we are exploring.
The goal of this talk is to provide a demonstration of database tools that any journalist can use to explore datasets and draw insights from connected datasets.
Transforming AI with Graphs: Real World Examples using Spark and Neo4jFred Madrid
Graphs – or information about the relationships, connection, and topology of data points – are transforming machine learning. We’ll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI. This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We’ll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery. This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.
Reversim Summit 2014: re:dash a new way to query, visualize and collaborate o...Arik Fraimovich
re:dash is EverythingMe's take on freeing the data within our company in a way that will better fit our culture and usage patterns.
Prior to re:dash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.
re:dash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL).
More information about re:dash and background: http://geeks.everything.me/2013/12/05/introducing_redash/
GitHub: https://github.com/everythingme/redash
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, uses real-world flight data to discuss RDF and its newer property-graph-functionality iteration, RDF*, wrapping up with a pair of real-world demonstrations via Zeppelin notebooks.
With information available in more systems than ever, how do we make sense of it all? Here are a few examples of how people have blended large amounts of data across the web and enterprise, and turned it into something useful and visually pleasing.
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
An introduction to Microsoft R Services,
Microsoft R Open and Microsoft R Server.
This presentation will briefly cover the following:
-Why consider MRO and R Server
-R Server
-MRO
-Microsoft R Services/R Server Platform
-DistributedR
-RevoScaleR/ScaleR
-ConnectR
-DevelopR
-DeployR
-Resources
-References
OrientDB, the fastest document-based graph database @ Confoo 2014 in Montreal...Alessandro Nadalin
OrientDB is a NoSQL graph database which also includes a document layer (like MongoDB): it gained a lot of attention, enough to push big companies like Sky and UltraDNS to use it in production: it's written in Java and it's amazingly fast, since it can store up to 150,000 records per second on common hardware; moreover, thanks to being a graphdb, it can manage relationship so fast that, compared to traditional DBs, can be 1000% faster than them.
Dataviz presentation at ThingsKamp2015 IstanbulCédric Lombion
Dataviz presentation at ThingsKamp2015 Istanbul. Intended for newcomers to information visualisation, with the test of the first prototype of a dataviz card game.
Restructuring and transforming attributes is often an important part of data preparation. Here are tips for managing and validating attributes, plus examples of new functionality that makes it easier than ever to work with date and time.
MuseoTorino, first italian project using a GraphDB, RDFa, Linked Open Data21Style
MuseoTorino, is the first italian project using Web 3.0 tecnologies. NOSQL-GraphDB (Neo4J), RDFa, Linked Open Data.
MuseoTorino is a 21style (www.21-style.com) project for the municipality of Torino, Italy.
These slides come from CodeMotion, the best Italian conference for developers and IT entusiast !
Karnataka Geospatial Experience FME World Tour 2017 IndiaRaghavendran S
FME World Tour 2017, India
Presentation from FME magicians. Inspiring stories of FME wizardry and tips for brewing powerful data workflows.
Presentation by Mr H Hemanth KumarPrincipal Scientific OfficerPrincipal Investigator – NRDMS, KSSDI & VIS on Geospatial Technologies for informed decision making – Karnataka Experience
Finding Insights In Connected Data: Using Graph Databases In JournalismWilliam Lyon
When dealing with datasets, journalists have many options to choose from when moving beyond Excel. Usually the first step is using a relational (or SQL) database. While a relational database can be a good choice for some datasets, data analysts today turn to new tools to gain deeper insight. This talk will show how we can use a graph database to analyze highly connected data using examples from U.S. Congressional data and political email archives. Using the U.S. Congress data, we’ll show you how to explore the dataset using Cypher, the Neo4j query language, to discover legislator activity including bill sponsorship and voting activity. Building up our knowledge of Cypher as we progress, we’ll show how you can use principles from social network analysis to find influential legislators and discover what topics legislators have influence over. Finally, we will examine how to draw insights from the Hillary Clinton email dataset, released as part of a FOIA request earlier this year. We will explore this dataset as a graph of interactions among users, answering questions like: Who is communicating with Hillary the most? What are the topics of these emails? You’ll learn how to visualize these using the Neo4j browser to quickly make sense of the data as we are exploring.
The goal of this talk is to provide a demonstration of database tools that any journalist can use to explore datasets and draw insights from connected datasets.
Transforming AI with Graphs: Real World Examples using Spark and Neo4jFred Madrid
Graphs – or information about the relationships, connection, and topology of data points – are transforming machine learning. We’ll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI. This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We’ll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery. This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.
Reversim Summit 2014: re:dash a new way to query, visualize and collaborate o...Arik Fraimovich
re:dash is EverythingMe's take on freeing the data within our company in a way that will better fit our culture and usage patterns.
Prior to re:dash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.
re:dash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL).
More information about re:dash and background: http://geeks.everything.me/2013/12/05/introducing_redash/
GitHub: https://github.com/everythingme/redash
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, uses real-world flight data to discuss RDF and its newer property-graph-functionality iteration, RDF*, wrapping up with a pair of real-world demonstrations via Zeppelin notebooks.
With information available in more systems than ever, how do we make sense of it all? Here are a few examples of how people have blended large amounts of data across the web and enterprise, and turned it into something useful and visually pleasing.
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
An introduction to Microsoft R Services,
Microsoft R Open and Microsoft R Server.
This presentation will briefly cover the following:
-Why consider MRO and R Server
-R Server
-MRO
-Microsoft R Services/R Server Platform
-DistributedR
-RevoScaleR/ScaleR
-ConnectR
-DevelopR
-DeployR
-Resources
-References
OrientDB, the fastest document-based graph database @ Confoo 2014 in Montreal...Alessandro Nadalin
OrientDB is a NoSQL graph database which also includes a document layer (like MongoDB): it gained a lot of attention, enough to push big companies like Sky and UltraDNS to use it in production: it's written in Java and it's amazingly fast, since it can store up to 150,000 records per second on common hardware; moreover, thanks to being a graphdb, it can manage relationship so fast that, compared to traditional DBs, can be 1000% faster than them.
Applying large scale text analytics with graph databasesData Ninja API
Data Ninja Services collaborated with Oracle to reach a major milestone in the integration of text analytics with Oracle Spatial and Graph. The Data Ninja Services client in Java can be used to analyze free texts, extract entities, generate RDF semantic graphs, and choose from a number of graph analytics to infer entity relationships. We demonstrated two case studies involving mining health news and detecting anomalies in product reviews.
Graphs in the Database: Rdbms In The Social Networks AgeLorenzo Alberton
Despite the NoSQL movement trying to flag traditional databases as a dying breed, the RDBMS keeps evolving and adding new powerful weapons to its arsenal. In this talk we'll explore Common Table Expressions (SQL-99) and how SQL handles recursion, breaking the bi-dimensional barriers and paving the way to more complex data structures like trees and graphs, and how we can replicate features from social networks and recommendation systems. We'll also have a look at window functions (SQL:2003) and the advanced reporting features they make finally possible.
Trees In The Database - Advanced data structuresLorenzo Alberton
Storing tree structures in a bi-dimensional table has always been problematic. The simplest tree models are usually quite inefficient, while more complex ones aren't necessarily better. In this talk I briefly go through the most used models (adjacency list, materialized path, nested sets) and introduce some more advanced ones belonging to the nested intervals family (Farey algorithm, Continued Fractions, and other encodings). I describe the advantages and pitfalls of each model, some proprietary solutions (e.g. Oracle's CONNECT BY) and one of the SQL Standard's upcoming features, Common Table Expressions.
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf
Abstract: How graphs became just another big data primitive
Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.
While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models.
RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different.
Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin).
Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web.
How To Model and Construct Graphs with Oracle Database (AskTOM Office Hours p...Jean Ihm
2nd in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
With property graphs in Oracle Database, you can perform powerful analysis on big data such as social networks, financial transactions, sensor networks, and more.
To use property graphs, first, you’ll need a graph model. For a new user, modeling and generating a suitable graph for an application domain can be a challenge. This month, we’ll describe key steps required to construct a meaningful graph, and offer a few tips on validating the generated graph.
Albert Godfrind (EMEA Solutions Architect), Zhe Wu (Architect), and Jean Ihm (Product Manager) walk you through, and take your questions.
AI, Knowledge Representation and Graph Databases - Key Trends in Data ScienceOptum
Knowledge Representation is a key focus for most modern AI texts. Many AI experts feel that over half of their work is understanding how to find the right knowledge structures to build intelligent agents that can continuously learn and respond to changing events in their world. In 2012, a paper published by Google started a consolidation of the many diverse forms of knowledge representation into a single general-purpose structure called a labeled property graph.
This talk will describe the key events behind this movement and show how a new generation of data scientist will be needed to build and maintain corporate knowledge graphs that contain a uniform, normalized and highly connected data sets for used by researchers and intelligent agents. We will also discuss the challenges of transferring siloed project-knowledge to reusable structures.
Graph Databases - Where Do We Do the Modeling Part?DATAVERSITY
Graph processing and graph databases have been with us for a while. However, since their physical implementations are the same for every database in production (Node connected to node, or triplets), there's a perception that data modeling (and data modelers) have no role on projects where graph databases are used.
This month we'll talk about where graph databases are a best fit in a modern data architecture and where data models add value.
Graph Database Defined. A graph database is defined as a specialized, single-purpose platform for creating and manipulating graphs. Graphs contain nodes, edges, and properties, all of which are used to represent and store data in a way that relational databases are not equipped to do.
Morpheus SQL and Cypher® in Apache® Spark - Big Data Meetup MunichMartin Junghanns
Extending Apache Spark Graph for the Enterprise with Morpheus and Neo4j
The talk covers:
* Neo4j, Property Graph Model and Cypher
* Cypher query exectution in Apache Spark
* Neo4j graph algorithms
* Example Code
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
1st in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Xavier Lopez (PM Senior Director) and Zhe Wu (Graph Architect) will share a brief intro to what property graphs can do for you, and take your questions - on property graphs or any other aspect of Oracle Database Spatial and Graph features. With property graphs, you can analyze relationships in Big Data like social networks, financial transactions, or IoT sensor networks; identify influencers; discover patterns of fraudulent behavior; recommend products, and much more -- right inside Oracle Database.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
3. Who am I
Ph.D Kisung Kim - Chief Technology Officer of Bitnine Global Inc.
Researched query optimization for graph-structured data during doctorate degree
Developed a distributed relational database engine in TmaxSoft
Lead the development of a new graph database, Agens Graph in Bitnine Global
4. Graph data model
Modeling data as entities and their relationships
Relational data model
Handle data as tables
What is Graph Database?
Real-world
Phenomena
Relational
Data Model
Graph
Data Model
Entity-Relation
Modeling
Database
Table schema
Normalization/Denormalization
Referential constraints
Join keys
Graphs
5. Property Graph Model
Terminology:
Entity - Node - Vertex
Relationships - Edge
Property - Attribute
person company
works_for
Name: Kisung Kim
Email: kskim@bitnine.net
Name: Bitnine Global
Homepage: http://bitnine.net
title: CTO
Team: agens graph
Property
Node
Relationship
Very intuitive and easy
to model E-R diagram to property graphs
9. Concise Querying: Cypher Example
From Zhu, Y., Yan, E., & Song, I.-Y. (2016). The use of a graph-based system to improve bibliographic information retrieval: System design, implementation,
and evaluation. Journal of the Association for Information Science & Technology
Affiliation
Author
Paper
Paper
Term:
‘Database’
cite
write
work for
topic
Query: Which institute does cite papers about ‘Database’?
10. Brief History of Graph Database
1970s: Network data model before relational model
1980: Big bang
The birth of the relational model and the declarative query language SQL
1990s: XML, Semantic Web standard (RDF, SPARQL) using graph model
1998~: NoSQL boom including Graph Database
2000s: Neo4j started and Cypher was borned
Cypher borrows some concepts(i.e, graph pattern matching) from SPARQL
11. Cypher
Most famous graph database, Cypher
O(1) access using fixed-size array
Gremlin Distributed graph system based on Cassandra
AQL Multi-model database (Document + Graph)
OQL Multi-model database (Document + Graph)
Graph Databases
DSE Graph
There are many other graph systems;
RDF stores (Allegrograph, Oracle, Virtuoso, … )
Graph analytics (Giraph, GraphX, PowerGraph, PGX, ThingSpan(InfiniteGraph), … )
14. NoSQL Databases
Document store, Key/value store, Column-family store
Ignores relationships of data
(Does not handle them in database engine)
Focus on maximization of scalability and availability
Sacrifice declarative querying and transactional consistency, …
Graph store
Different motivation: graph data model
But NoSQL databases are evolving; e.g. Couchbase’s N1QL and Cassandra’s CQL
16. Summary
Graph database motivation
Simple and intuitive data modeling for complex relationship data
Graph database strengths
Enhanced productivity from concise queries
Fast traversal performance for complex graphs
Graph visualization and graph analytics