In this session from Neo4j Government Graphday, Philip Rathle discusses how federal agencies and contractors can utilize graphs to power their applications.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
Andrea Bielli, IT Architect Global Digital Solution, Enel
Davide Gimondo, Software Engineer, Enel
Enel mostra come neo4j aiuta nella gestione delle reti elettriche in 8 paesi nel mondo.
Con l’obiettivo di ottimizzare gli algoritmi di percorrenza della rete elettrica, in modo da rendere le reti sempre più efficienti e resilienti.
L’obiettivo di Enel è una gestione ottimale della topologia della rete per garantire gli obiettivi del gruppo: la transizione energetica e l’elettrificazione dei paesi in cui opera, verso l’obiettivo Net Zero, relativo alla riduzione delle emissioni nella produzione e distribuzione dell’energia elettrica.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
Andrea Bielli, IT Architect Global Digital Solution, Enel
Davide Gimondo, Software Engineer, Enel
Enel mostra come neo4j aiuta nella gestione delle reti elettriche in 8 paesi nel mondo.
Con l’obiettivo di ottimizzare gli algoritmi di percorrenza della rete elettrica, in modo da rendere le reti sempre più efficienti e resilienti.
L’obiettivo di Enel è una gestione ottimale della topologia della rete per garantire gli obiettivi del gruppo: la transizione energetica e l’elettrificazione dei paesi in cui opera, verso l’obiettivo Net Zero, relativo alla riduzione delle emissioni nella produzione e distribuzione dell’energia elettrica.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Straight Talk to Demystify Data LineageDATAVERSITY
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Data modelling is considered a staple in the world of data management. The skill of the data modeler and their knowledge of the business plays a large role in successful Enterprise Information Management across many organizations. Data modeling requires formal accountability, attention to metadata and getting the business heavily involved in data requirement development. These are all traits of solid Data Governance programs.
Join Bob Seiner and a special guest modeler extraordinaire in this month’s installment of Real-World Data Governance to discuss data modeling as a form of data governance. Learn how to use the skillfulness of the data modeler to advance data-as-an-asset and governance agendas while conveying the importance and value of both disciplines.
In this webinar Bob and a special guest will talk about:
•Data Modeling as Art or Science
•Role of Data Modeler in a Governance Program
•Data Modeler Skills as Governance Skills
•Modeling and Governance Best Practices
•Leveraging the Model as a Governance Artifact
BigID, OneTrust, IAPP Webinar: Bridging the Privacy Office with ITBigID Inc
Dimitri Sirota, CEO, BigID and Blake Bannon, VP of Product, OneTrust, present will detail best practices for synchronizing a privacy office enterprise privacy management platform with a tool for finding, classifying and correlating PI or PII across the data center and cloud.
Access the webinar presentation to learn:
-What the market landscape for privacy-centric products looks like
-Key considerations for evaluating privacy office software
-Key considerations to consider for privacy-oriented data discovery software
-How to ensure your privacy policy is aligned with operational reality
-Integration scenarios and use cases that connect the privacy office with IT
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
Slides from tutorial at EDW 2017 in Atlanta, GA on Implementing Agile Data Governance. Discusses how to write and add governance stories into existing Agile projects.
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
JSON Data Modeling in Document DatabaseDATAVERSITY
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool?
Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
Data Governance is becoming a more mature and better understood practice that reduces risk and creates value across all industries.
This presentation covers:
-Typical obstacles to sustainable Data Governance
- Re-energizing your program after a key player (or two) leave and other personnel challenges
- Staying relevant to the company as the business evolves over time
- Understanding the role of metrics and why they are critical
- Leveraging Communication and Stakeholder Management practices to maintain commitment
- Embedding Data Governance into the operations of the company
Graph Database Management Systems provide an effective
and efficient solution to data storage in current scenarios
where data are more and more connected, graph models are
widely used, and systems need to scale to large data sets.
In this framework, the conversion of the persistent layer of
an application from a relational to a graph data store can
be convenient but it is usually an hard task for database
administrators. In this paper we propose a methodology
to convert a relational to a graph database by exploiting
the schema and the constraints of the source. The approach
supports the translation of conjunctive SQL queries over the
source into graph traversal operations over the target. We
provide experimental results that show the feasibility of our
solution and the efficiency of query answering over the target
database.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Straight Talk to Demystify Data LineageDATAVERSITY
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Data modelling is considered a staple in the world of data management. The skill of the data modeler and their knowledge of the business plays a large role in successful Enterprise Information Management across many organizations. Data modeling requires formal accountability, attention to metadata and getting the business heavily involved in data requirement development. These are all traits of solid Data Governance programs.
Join Bob Seiner and a special guest modeler extraordinaire in this month’s installment of Real-World Data Governance to discuss data modeling as a form of data governance. Learn how to use the skillfulness of the data modeler to advance data-as-an-asset and governance agendas while conveying the importance and value of both disciplines.
In this webinar Bob and a special guest will talk about:
•Data Modeling as Art or Science
•Role of Data Modeler in a Governance Program
•Data Modeler Skills as Governance Skills
•Modeling and Governance Best Practices
•Leveraging the Model as a Governance Artifact
BigID, OneTrust, IAPP Webinar: Bridging the Privacy Office with ITBigID Inc
Dimitri Sirota, CEO, BigID and Blake Bannon, VP of Product, OneTrust, present will detail best practices for synchronizing a privacy office enterprise privacy management platform with a tool for finding, classifying and correlating PI or PII across the data center and cloud.
Access the webinar presentation to learn:
-What the market landscape for privacy-centric products looks like
-Key considerations for evaluating privacy office software
-Key considerations to consider for privacy-oriented data discovery software
-How to ensure your privacy policy is aligned with operational reality
-Integration scenarios and use cases that connect the privacy office with IT
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptxNeo4j
Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.
Slides from tutorial at EDW 2017 in Atlanta, GA on Implementing Agile Data Governance. Discusses how to write and add governance stories into existing Agile projects.
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
JSON Data Modeling in Document DatabaseDATAVERSITY
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool?
Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
Data Governance is becoming a more mature and better understood practice that reduces risk and creates value across all industries.
This presentation covers:
-Typical obstacles to sustainable Data Governance
- Re-energizing your program after a key player (or two) leave and other personnel challenges
- Staying relevant to the company as the business evolves over time
- Understanding the role of metrics and why they are critical
- Leveraging Communication and Stakeholder Management practices to maintain commitment
- Embedding Data Governance into the operations of the company
Graph Database Management Systems provide an effective
and efficient solution to data storage in current scenarios
where data are more and more connected, graph models are
widely used, and systems need to scale to large data sets.
In this framework, the conversion of the persistent layer of
an application from a relational to a graph data store can
be convenient but it is usually an hard task for database
administrators. In this paper we propose a methodology
to convert a relational to a graph database by exploiting
the schema and the constraints of the source. The approach
supports the translation of conjunctive SQL queries over the
source into graph traversal operations over the target. We
provide experimental results that show the feasibility of our
solution and the efficiency of query answering over the target
database.
How to Design Retail Recommendation Engines with Neo4jNeo4j
Recommendations are at the core of digital transformation in retail today. Whether you’re building features such as product recommendations, promotion recommendations, personalized customer experience, or re-imagining your supply chain to meet customer demands for same day delivery — you’re facing challenges that require the ability to leverage connections from many different data sources, in real-time. There’s no better technology to meet these challenges than a native graphDB technology such as Neo4j.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
In this webinar we'll explore a data set using Neo4j and Cypher and compare the approach we might take with a relational database and SQL. We'll cover the following topics: Modeling the data set Importing the data Querying the data Evolving the model and queries as the data changes.
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...Alexander Pozdneev
The slides presented by Alexander Pozdneev at GraphHPC-2017 conference (http://www.dislab.org/GraphHPC-2017/en/agenda.php).
Graph databases are increasingly popular in managing the information where the relationships between the data entities are of highest priority. However, a technical task of deploying, managing, and maintaining a graph database on-a-premise is decoupled from the process of solving an applied problem. IBM Graph is a graph database-as-a-service available on the IBM Bluemix cloud platform-as-a-service. IBM Graph is built upon open-source components while featuring high-availability and scalability on-demand. In this talk, we will introduce the main concepts behind IBM Graph and show how to leverage its API and the Bluemix console GUI.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
SWOT of Bigdata Security Using Machine Learning Techniquesijistjournal
This paper gives complete guidelines on BigData, Different Views of BigData, etc.How the BigData is useful to us and what are the factors affecting BigData all the things are covered under this paper. The paper also contains the BigData Machine learning techniques and how the Hadoop comes into the picture. It also contains the what is importance of BigData security. The paper mostly covers all the main point that affect Big Data and Machine Learning.
The Art of Storytelling Using Data ScienceGramener
Gramener's VP - Sales, APAC Region, Vijayam Sirikonda interacted with the students of IIM Raipur and talked about the importance of data storytelling for business users.
"Big Data" is term heard more and more in industry – but what does it really mean? There is a vagueness to the term reminiscent of that experienced in the early days of cloud computing. This has led to a number of implications for various industries and enterprises. These range from identifying the actual skills needed to recruit talent to articulating the requirements of a "big data" project. Secondary implications include difficulties in finding solutions that are appropriate to the problems at hand – versus solutions looking for problems. This presentation will take a look at Big Data and offer the audience with some considerations they may use immediately to assess the use of analytics in solving their problems.
The talk begins with an idea of how big "Big Data" can be. This leads to an appreciation of how important "Management Questions" are to assessing analytic needs. The fields of data and analysis have become extremely important and impact nearly all facets of life and business. During the talk we will look at the two pillars of Big Data – Data Warehousing and Predictive Analytics. Then we will explore the open source tools and datasets available to NATO action officers to work in this domain. Use cases relevant to NATO will be explored with the purpose of show where analytics lies hidden within many of the day-to-day problems of enterprises. The presentation will close with a look at the future. Advances in the area of semantic technologies continue. The much acclaimed consultants at Gartner listed Big Data and Semantic Technologies as the first- and third-ranked top technology trends to modernize information management in the coming decade. They note there is an incredible value "locked inside all this ungoverned and underused information." HQ SACT can leverage this powerful analytic approach to capture requirement trends when establishing acquisition strategies, monitor Priority Shortfall Areas, prepare solicitations, and retrieve meaningful data from archives.
In this talk, I reflect on the tasks commonly involved in crafting visualizations and show examples of different applications of information/data visualization. Along this ride I will share my workflow, point out the common pitfalls and provide recommendations.
These slides were from my guest lecture in InfoVis class at UC Berkeley iSchool on Apr 11, 2016. Thank you Prof. Marti Hearst for inviting.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
How to Feed a Data Hungry Organization – by Traveloka Data TeamTraveloka
In Traveloka's Inaugural Data Meetup held in April 2017, Ainun Najib (Head of Data), Dr. Philip Thomas (Lead Data Scientist), and Rendy B. Junior (Lead Data Engineer) shared about the journey that Traveloka's Data Team have taken so far so that the audience can learn from the struggles and triumphs in managing Traveloka's burgeoning data.
You will learn more about:
1) Data culture in Traveloka
2) Data engineering in Traveloka
3) Data science in Traveloka
To follow our LinkedIn page, visit bit.ly/TravelokaLinkedInPage
Safe Harbor Statement
Our discussion may include predictions, estimates or other information that might be considered conclusive. While these conclusive statements represent our current judgment on the best practices, they are subject to risks and uncertainties that could cause actual results to differ materially. You are cautioned not to place undue reliance on our statements, which reflect our opinions only as of the date of this presentation. Please keep in mind that we are not obligating ourselves to revise or publicly release the results of any revision to these presentation materials in light of new information or future events.
Graph Database is the new paradigm of Big Data.
New insights are discovered in the connected data.
Fabricating Big Data into connected data is the cutting edge technology.
Graph database is the driver for sustainable growth in the Era of Big Data.
Graph Data is already prevailing among the global leading companies.
Graph Database will pass the dawn of standards.
The most widely adopted method will be the Hybrid Database.
Each company needs to prepare for the wave of change.
AgenGraph will support your business with superior capabilities.
For more information, please visit www.bitnine.net
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Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
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Links:
Autonomous Action
http://Avtonom.org
Anarchist Black Cross Moscow
http://Avtonom.org/abc
Solidarity Zone
https://t.me/solidarity_zone
Memorial
https://memopzk.org/, https://t.me/pzk_memorial
OVD-Info
https://en.ovdinfo.org/antiwar-ovd-info-guide
RosUznik
https://rosuznik.org/
Uznik Online
http://uznikonline.tilda.ws/
Russian Reader
https://therussianreader.com/
ABC Irkutsk
https://abc38.noblogs.org/
Send mail to prisoners from abroad:
http://Prisonmail.online
YouTube: https://youtu.be/c5nSOdU48O8
Spotify: https://podcasters.spotify.com/pod/show/libertarianlifecoach/episodes/Russian-anarchist-and-anti-war-movement-in-the-third-year-of-full-scale-war-e2k8ai4
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Donate Us
https://serudsindia.org/how-individuals-can-support-street-children-in-india/
#donatefororphan, #donateforhomelesschildren, #childeducation, #ngochildeducation, #donateforeducation, #donationforchildeducation, #sponsorforpoorchild, #sponsororphanage #sponsororphanchild, #donation, #education, #charity, #educationforchild, #seruds, #kurnool, #joyhome
3. Fred Kagan David Mesa
Chief Knowledge
Architect for
NASA
Kimberly Kagan
Director
Critical Threats Project
American Enterprise Institute
President
Institute for the
Study of War
Today’s Guest Speakers
4. “Life can only be understood backwards;
but it must be lived forwards.”
-Søren Kierkegaard
5.
6. Yellowstone National Park Ecosystem
Known Influences Entered One-at-a-Time
(Willow)-[:HABITAT_FOR]->(Lincoln’s Sparrow)
(Aspen)-[:FOOD_FOR]->(Beaver)
(Beaver Ponds)-[:HABITAT_FOR]->(Beaver)
(Deer)-[:BROWSE_ON]->(Cottonwood)
(Berry Shrubs)-[:FOOD_FOR]->(Bears)
…
8. MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"})
WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS number
RETURN factor AS How_Wolves_Affect_RiverStability
ORDER BY number
LIMIT 5
Yellowstone National Park Ecosystem
Query for Trophic Cascades
Conclusion:
9. 1. Where do graph databases fit into the overall data landscape?
2. What is a graph database & when is it useful?
3. Be inspired to find your next graph in government
Takeaways from this Session:
11. Discrete Data
Minimally
connected data
All You Really Need to Know
(at least for today)
Other NoSQL Relational DBMS Neo4j Graph DB
Connected Data
Focused on
Data Relationships
12. e of Graphs has created some of the most successful companies in the wo
13. “Graph analysis is possibly the single most effective competitive
differentiator for organizations pursuing data-driven operations
and decisions after the design of data capture.”
By the end of 2018, 70% of leading organizations will have one or
more pilot or proof-of-concept efforts underway utilizing graph
databases.
Analyst Perspective
“Forrester estimates that over 25% of enterprises will be using
graph databases by 2017”
IT Market Clock for Database Management Systems, 2014
https://www.gartner.com/doc/2852717/it-market-clock-database-management
TechRadar™: Enterprise DBMS, Q1 2014
http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801
Making Big Data Normal with Graph Analysis for the Masses, 2015
http://www.gartner.com/document/3100219
22. Relational
Database
Good for:
• Well-understood data structures
that don’t change too frequently
A way of representing data
• Known problems involving
discrete parts of the data, or
minimal connectivity
DATA
23. Graph
Database
Relational
Database
A way of representing data
Good for:
• Dynamic systems: where the data
topology is difficult to predict
• Dynamic requirements:
the evolve with the business
• Problems where the relationships
in data contribute meaning & value
Good for:
• Well-understood data structures
that don’t change too frequently
• Known problems involving
discrete parts of the data, or
minimal connectivity
24. 27
A unified view for
ultimate agility
• Easily understood
• Easily evolved
• Easy collaboration
between business
and IT
#1 Benefit: Project Agility
The Whiteboard Model Is the Physical Model
25. Connectedness and Size of Data Set
ResponseTime
Relational and
Other NoSQL
Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Neo4j
“Minutes to
milliseconds”
#2 Benefit:
“Minutes to Milliseconds” Real-Time Query Performance
26. “We found Neo4j to be literally thousands of times faster
than our prior MySQL solution, with queries that require
10-100 times less code. Today, Neo4j provides eBay with
functionality that was previously impossible.”
- Volker Pacher, Senior Developer
“Minutes to milliseconds” performance
Queries up to 1000x faster than RDBMS or other NoSQL
#3 Benefit:
“Minutes to Milliseconds” Real-Time Query Performance
28. At Write Time:
data is connected
as it is stored
At Read Time:
Lightning-fast retrieval of data and
relationships via pointer chasing
Index free adjacency
Magic Ingredient #1 of 3:
Graph Optimized Memory & Storage
29. MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
Magic Ingredient #2 of 3:
A Productive and Powerful Graph Query Language
30. 3
3
Example HR Query in SQL The Same Query using Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting
Magic Ingredient #2 of 3:
A Productive and Powerful Graph Query Language
31. Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
34
Maintains Integrity Over Time Becomes Corrupt Over Time
Magic Ingredient #3 of 3:
ACID Graph Writes
36. Law
Enforcement
Use Case:
Information and Data
Synchronization in
Law Enforcement
Law Enforcement Agencies use
Neo4j to model the information
into graphs to improve efficiency
and make direct and implicit
patterns readily apparent in real
time.
A suspect often appears in several
different databases
Financial recordsConvictions
Adresses
Vehicles
Traffic cameras
Arrests
Police Reports
Agency Records Public Records Traffic Records
SUSPECT
The Graphs In Government
37. The Graphs In Government 01
Bystander investigated
due to deep connection found
Use Case:
Modeling Graphs
in Investigations
Neo4j is used by LE to track all
parts of criminal investigations,
including witnesses, suspects,
forensic evidence, and
locations. All related directly and
indirectly.
Law
Enforcement
39. Revolving Debt
Number of Accounts
Normal behavior
Fraudulent pattern
Fraud Detection With Connected Analysis
40. The Graphs In Government 01
ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
ADDRESS
PHONE
NUMBER
PHONE
NUMBER
SSN 2
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
Law
Enforcement
Use Case:
Modeling Fraud
Rings as Graphs
Organizing a fraud ring in the real
world is relatively simple. A group of
people share their personal
information to create synthetic
identities. For example with just 2
individuals sharing names and
social security numbers can create
4 different identities. This can be
discovered with connected analysis.
48. The Graphs In Government 01
Withdraw
Use Case:
Combating Money
Laundering With
Graphs
Neo4j is used to combat
advanced money laundering
schemes. Money laundering is all
about how funds travel across a
network of parties. Without graph
analysis capabilities, some of
these patterns can be impossible
to detect.
Washed in complex series of transfers
Anti-Money
Laundering
Deposit
49. The Graphs In Government 01
The Cali
Cartel Money
Laundering
Scheme
Money
Laundering
50. Source: http://neo4j.com/blog/analyzing-panama-papers-neo4j/
Case Study:
“The Panama
Papers”
• The International Consortium of
Investigative Journalists (ICIJ) exposed
highly connected networks of offshore tax
structures used by the world’s richest elites.
• With 11,5 million documents, it’s the largest
financial leak of all times.
• The unfolded connections in “The Panama
Papers” was a major news story 2016.
The Graphs In Government 01
Money
Laundering
If there’s one thing to remember for today, it’s this journey
Frederick Kagan, Director, Critical Threats Project at American Enterprise Institute, and Kimberly Kagan, President at Institute for the Study of War
I’d like to start the day with a koan…
What Kierkegaard is talking about is Causality. Oftentimes we don’t understand what the effects will be because they’re so complex. --- Let me show you one of my favorite examples of complex causality in nature.
Here is what one gets if one does precisely that. Finding the paper, reading it, and expressing the links as a graph, was given to one of our summer interns, who was able to build this graph in an afternoon.
Source: http://gist.neo4j.org/?0ac320c799ce55089377
Here is what one gets if one does precisely that. Finding the paper, reading it, and expressing the links as a graph, was given to one of our summer interns, who was able to build this graph in an afternoon.
Source: http://gist.neo4j.org/?0ac320c799ce55089377
The beauty of the graph however is in the questions it enables us to answer. For example, here we ask: what are all of the paths between “Wolves” and “Rivers”. It turns out there are quite a few, but that all four of the paths leading immediately into “Rivers” have the effect of promoting the rivers. We can easily conclude therefore that wolves can be expected to have an overall salutary effect upon the rivers… which was, after 15 years of experimental science, found to be true.
And deriving value from data-relationships is exactly what some of the most successful companies in the world have done.
Google created perhaps the most valuable advertising system of all time on top of their search-enginge, which is based on relationships between webpages.
Linkedin created perhaps the most valuable HR-tool ever based on relationships amongst professional
And this is also what pay-pal did, creating a peer-to-peer transaction service, based on relationships.
How many of you feel you have a handle on the Big Data Landscape?
So apparently according to this article in Dataconomy, it’s simply a matter of memorizing this.
The “fruit salad” slide earlier outlined technologies that were mostly focused on dealing with data in discrete chunks.
What’s interesting about the right side is that some of the largest & most successful tech companies in the last decade were possible thanks to their use of graphs.
And deriving value from data-relationships is exactly what some of the most successful companies in the world did.
Google created perhaps the most valuable advertising system of all time on top of their search-enginge, which is based on relationships between webpages.
Linkedin created perhaps the most valuable HR-tool ever based on relationships amongst professional
And this is also what pay-pal did, creating a peer-to-peer transaction service, based on relationships.
Continuing on – We have receive very solid validation from these industry watchers that the market we are pursuing represents a huge opportunity and being anointed as the leader in this market that is likely to grow at this rate is very exciting.
“Neo4j is the most popular graph database on the planet, so we have a privileged view on the massive adoption of graph databases. As an example, Neo4j is today used in verticals as diverse as <click> Software, <click>, Financial Services, <click> Retail, etc… by some of the biggest companies on the planet, across a wide range of use cases.”
First, not everyone in the room would know what a graph is.
What this means for your data structure
Kick off with discussing major trends happening in enterprises.
The query asks: “Find all direct reports and how many people they manage, up to three levels down”
Keeping Your Graph Intact is Essential for Graph Operations
This is great… Now let’s talk about reads. Some applications are ok with somewhat stale data. Some are not. Causal consistency gives you the choice.
Titan example:
Ghost Vertices: If a vertex gets deleted while it is concurrently being modified, the vertex might re-appear as a ghost.
Stale Index entries: Index entries might point to nonexistent vertices in case of partial mutation persistence.
Half-Edges: Only one direction of an edge gets persisted or deleted which might lead to the edge not being or incorrectly being retrieved.
Uni-directed Ghost Edges: A uni-directed edge points to a deleted vertex.
First, not everyone in the room would know what a graph is.
Its obvious that traditional technologies which were aimed at individuals and their behavior are inadequate to detect and prevent sophisticated fraud rings. So why is that?
So let’s take a look on how Data Synchronization in Law Enforcement could work modeled in a graph.
For example: We have a suspect that might have prior convictions, arrests, and figures in police reports, and this could be stored in agency records..
A suspect might appear in many different databases. However these systems are not designed to relate to each other and here Neo4j and a graph database approach would be a very effective tool to augment existing systems.
Having graph search capabilities across this data opens up for both targeted searches and advanced connected analysis.
Neo4j is used by LE to track all parts of criminal investigations, including witnesses, suspects, forensic evidence, and locations.
All of this is related directly and indirectly. Therefor connected analysis can give Law Enforcement agents an important insight of who and what to investigate… even implicit connections could unravel patterns that weren’t available before.
[In this simple fraud detection approach to detect credit card fraud, it is relatively easy to spot outliers. But what if the fraudster commits fraud while still exhibiting normal behavior. Well - this is exactly how fraud rings operate]
[A fraud ring rarely strays outside the normal behavior band. Instead they operate within normal limits and commit widespread fraud. This is very hard to detect by systems that are looking for outliers or activities outside the normal band.]
Another important area for Law Enforcement is Fraud.
Organizing a fraud ring in the real world is relatively simple. A group of people share their personal information to create synthetic identities. For example with just 2 individuals sharing names and social security numbers can create 4 different identities. This is something that can be discovered with connected analysis and the use of graphs.
Today, agencies need to augment their discrete analysis capability with connected analysis. Whether you’re dealing with a fraud ring or stolen and synthetic identities, it’s extremely powerful to use a graph database
Normally, your operational data is loaded into your fraud detection application which then conducts a wide range of discrete analysis to help your internal team to detect fraud.
Neo4j helps extend this capability with connected analysis. You can load some of the same information inside Neo4j. Neo4j’s native graph model stores both the data and its relationships which can help your team detect known fraud patterns as well as discover new ones.
Top Queries:
1. Trace dependencies up from servers all the way to applications and users
2. Trace dependencies across virtual and physical layers of infrastructure
3. Identify routes & alternate paths between various points in the network
4. Find the best, shortest, or least busy path, the best location in the network to introduce a new service
Because money laundering is all about how funds travel across a network of parties. Without graph analysis capabilities, some of these patterns can be impossible to detect.
What we see here a simple sketch of how this could be modelled. It’s deposits from different people and money that gets washed in a complex series of transfers.
This is a real example of such complexities, done by the Department of Treasury’s Office of Foreign Assets Control… unfolding the Money Laundering Scheme of the Cali Cartel.
And what we see here is a highly connected network of accounts, assets and people, which is perfect for graph analysis.
One case study that we are very proud of is enabling The International Consortium of Investigative Journalists (ICIJ) to expose highly connected networks of offshore tax structures, through what they call the Panama Papers.
Mars Curiosity Rover
In the retail-example… this would probably look something like this. You will have systems in place to perform different functions, all of them probably crucial and doing the job btw, The problem is that these systems are designed to perform a very specific task.
So in order to put the data from all these systems to good use effectively, in the recommendations-example we just showed you, in real-time you need to add the graph database-layer.
Because building a system based on a foundation that doesn't handle connections naturally, is extremely difficult, and it would require so much time and money, that it is virtually impossible to justify.
That WWI can be predicted without domain knowledge by iterating a graph and applying local structural constraints is nothing short of astonishing to me.
Note how the network slides into a balanced labeling — and into World War I.
You can pull in other dimensions easily here: geopolitics, weapons technology, genealogy (because the royals were not exactly blameless in this),
Graphs rock. Sometimes humans not so much.