This document discusses big data in agriculture. It defines big data as large volumes of data that require automation to process rather than individual humans. It notes that data comes from people through surveys and sensors, as well as systems like communication networks. While some technologies aim to marginally increase yields, most big data solutions will need to generate revenue by serving the agricultural value chain through traders, processors, and other stakeholders rather than smallholder farmers directly. Success requires understanding both the technology costs and dimensions as well as the agricultural revenue targets and dimensions.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
This document provides an overview of big data and how to manage large amounts of data. It defines big data, discusses the characteristics of big data including volume, variety and velocity. It describes who generates big data and technologies that can be used to analyze big data like Hadoop, data warehousing and stream computing. The challenges of handling big data are also mentioned.
On Big Data Analytics - opportunities and challengesPetteri Alahuhta
This document discusses big data analytics and its opportunities and challenges. It defines big data and explains the increasing number of "V's" that characterize big data, such as volume, velocity, variety, and veracity. It also outlines some common uses of big data analytics including customer insights, security and risk analysis, and resource optimization. Additionally, it discusses challenges of big data adoption like skills shortages and infrastructure limitations, as well as trends in big data and areas of expertise related to big data that VTT focuses on.
The talk will cover in broad strokes the building blocks, facilitators and challenges for big data based decision making.
Using examples from two projects from very dissimilar domains (High tech manufacturing and Public Health) Dr. Vinze will present possibilities for Data Science for both practitioners and academic researchers.
Data Mining and Big Data Challenges and Research OpportunitiesKathirvel Ayyaswamy
The document discusses 10 challenging problems in data mining research. It summarizes each problem with 1-2 paragraphs explaining the challenges. Some of the key problems discussed include developing a unifying theory of data mining, scaling up for high dimensional and streaming data, mining complex relationships from interconnected data, ensuring privacy and security of data, and dealing with non-static and unbalanced data. The document advocates that more research is needed to address these issues and better integrate data mining with database systems and domain knowledge.
Keynote talk by David Dietrich, EMC Education Services at ICCBDA 2013 : International Conference on Cloud and Big Data Analytics
http://twitter.com/imdaviddietrich
http://infocus.emc.com/author/david_dietrich/
The document introduces big data by classifying data as structured, semi-structured, or unstructured and noting that most data is unstructured. It describes big data as large volumes of both structured and unstructured data that are too complex to process with traditional methods. Finally, it outlines the four V's of big data - volume, velocity, variety, and veracity - describing how big data is characterized by massive amounts of data that comes from many different sources and flows in at a high speed, creating challenges for storage, analysis, and ensuring meaningfulness.
This document discusses big data in agriculture. It defines big data as large volumes of data that require automation to process rather than individual humans. It notes that data comes from people through surveys and sensors, as well as systems like communication networks. While some technologies aim to marginally increase yields, most big data solutions will need to generate revenue by serving the agricultural value chain through traders, processors, and other stakeholders rather than smallholder farmers directly. Success requires understanding both the technology costs and dimensions as well as the agricultural revenue targets and dimensions.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
This document provides an overview of big data and how to manage large amounts of data. It defines big data, discusses the characteristics of big data including volume, variety and velocity. It describes who generates big data and technologies that can be used to analyze big data like Hadoop, data warehousing and stream computing. The challenges of handling big data are also mentioned.
On Big Data Analytics - opportunities and challengesPetteri Alahuhta
This document discusses big data analytics and its opportunities and challenges. It defines big data and explains the increasing number of "V's" that characterize big data, such as volume, velocity, variety, and veracity. It also outlines some common uses of big data analytics including customer insights, security and risk analysis, and resource optimization. Additionally, it discusses challenges of big data adoption like skills shortages and infrastructure limitations, as well as trends in big data and areas of expertise related to big data that VTT focuses on.
The talk will cover in broad strokes the building blocks, facilitators and challenges for big data based decision making.
Using examples from two projects from very dissimilar domains (High tech manufacturing and Public Health) Dr. Vinze will present possibilities for Data Science for both practitioners and academic researchers.
Data Mining and Big Data Challenges and Research OpportunitiesKathirvel Ayyaswamy
The document discusses 10 challenging problems in data mining research. It summarizes each problem with 1-2 paragraphs explaining the challenges. Some of the key problems discussed include developing a unifying theory of data mining, scaling up for high dimensional and streaming data, mining complex relationships from interconnected data, ensuring privacy and security of data, and dealing with non-static and unbalanced data. The document advocates that more research is needed to address these issues and better integrate data mining with database systems and domain knowledge.
Keynote talk by David Dietrich, EMC Education Services at ICCBDA 2013 : International Conference on Cloud and Big Data Analytics
http://twitter.com/imdaviddietrich
http://infocus.emc.com/author/david_dietrich/
The document introduces big data by classifying data as structured, semi-structured, or unstructured and noting that most data is unstructured. It describes big data as large volumes of both structured and unstructured data that are too complex to process with traditional methods. Finally, it outlines the four V's of big data - volume, velocity, variety, and veracity - describing how big data is characterized by massive amounts of data that comes from many different sources and flows in at a high speed, creating challenges for storage, analysis, and ensuring meaningfulness.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Big data emerged in the early 2000s and was first adopted by online companies like Google, eBay, and Facebook. It refers to data that exceeds the processing capacity of traditional databases due to its large size, speed of creation, and unstructured nature. The key attributes of big data are volume, variety, velocity and complexity. It comes from a variety of sources like sensors, social media, web logs, and photos. Analyzing big data can provide competitive advantages through insights from hidden patterns. While big data offers opportunities, organizations must ensure they have the right skills, manage costs, and address privacy issues.
The document discusses challenges and opportunities related to big data and high performance computing. It notes that computational power is increasing exponentially according to Moore's Law, but clock speeds have plateaued forcing a shift to multi-core processors. This is driving the need for parallel programming and new software approaches. Big data is also growing dramatically from various sources, such as sensors and social media. Analyzing this large, heterogeneous data requires new techniques in data mining, machine learning, and visualization. High performance computing and citizen science initiatives can help extract insights from big data to address important problems in health, environment, and other domains.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Banji Adenusi - big data prezzie - InfoSciBanji Adenusi
The document provides an overview of legal and technical aspects of big data. It defines big data as high-volume, high-velocity, and high-variety information that requires new processing methods. The document discusses key characteristics of big data including volume, velocity, variety, and veracity. It also summarizes infographics about the evolution of big data and provides an overview of technical challenges like data heterogeneity and privacy. On the legal side, it discusses issues around data ownership, intellectual property rights, data protection, and competition regulation in the use of big data.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Personalized News and Video Recomendation System at LinkSureLeanne Hwee
In recent years, the Internet industry has shifted more and more towards digital content distribution through online services. This presentation provides an overview of the overall system design and architecture of LinkSure News and Video Recommendations, the challenges encountered in practice, and the lessons learned from the production deployment of these systems at LinkSure. Specifically, we will highlight how news selection and personalisation of recommendations are formulated and addressed at LinkSure. By presenting our experiences in applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modelling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations.
Hawaii International Conference on Systems Sciences 2017. There are many opportunities for academics to submit papers for presentation at this very important conference which has sessions on Cognitive, Analytics, Big Data and much more. Haluk Demirkan, U Washington and Sergey Belov, IBM University Relations CEEMA made this presentation at Cognitive Systems Institute Speaker Series call on March 10, 2016.
Many companies face the challenge of building up a data science team from scratch and it can be hard to figure out how to start. In 2016, I was the first hire of a new data science team, with little infrastructure or strategy in place. Over the years, there were many different challenges for us to solve and mistakes to learn from as the team got more and more mature. This talk is about what I learned about the process of building up a data science team, from both my own experience in the past years and conversations with other data scientists in a similar situation.
The document outlines an agenda for a presentation on big data analytics, data science, and fast data. The agenda includes introductions to these topics as well as use cases. It discusses key characteristics of big data such as volume, complexity, and diverse data structures. Examples are provided of big data use cases in industries like healthcare, public services, and life sciences. The presentation aims to convey how these new data sources and analytical techniques can provide new insights.
This document discusses big data, including opportunities and risks. It covers big data technologies, the big data market, opportunities and risks related to capital trends, and issues around algorithmic accountability and privacy. The document contains several sections that describe topics like the Internet of Things, Hadoop, analytics approaches for static versus streaming data, big data challenges, and deep learning. It also includes examples of big data use cases and discusses hype cycles, adoption curves, and strategies for big data adoption.
The document discusses the Vienna Data Science Group (VDSG), a nonprofit organization that aims to promote data science. It has diverse members from various academic and professional fields. VDSG brings data science to life through talks, conferences, workshops, and networking events. It also discusses the impact of data science on society through applications like autonomous vehicles, smart home devices, and predictive analytics. Data science is changing areas like mobility, sports, finance, and advertising. Emerging technologies like the Internet of Things and predictive modeling raise important questions for society regarding privacy, ethics, and the limits of data-driven decisions.
NOVA Data Science Meetup 1/19/2017 - Presentation 1NOVA DATASCIENCE
The document discusses cognitive computing and its applications. It begins with an agenda that includes an overview of cognitive computing and examples of its use. It then discusses IBM Research's work leading to the development of Watson. Key points made include that most data is now unstructured, cognitive systems can reason, learn and understand like humans, and examples of cognitive computing applications in various domains.
Keynote: Graphs in Government_Lance Walter, CMONeo4j
This document contains an agenda and presentation slides for a Neo4j Graphs in Government event. The presentation introduces graph databases and Neo4j, discusses how graphs can help solve network-oriented problems, provides examples of graph use cases in various industries, and highlights new features in Neo4j 4.0 like easy management, unlimited scaling, and granular security. Case studies demonstrate how Neo4j has helped organizations like the US Army, MITRE, Adobe, and the German Center for Diabetes Research tackle complex data challenges.
Neo4j GraphTour New York_ State of the State_Amit Chaudhry Neo4jNeo4j
The document outlines an agenda for the Neo4j Graph Tour in New York that included discussions on graph databases, data management trends, case studies, and the future of graphs. It also provided examples of how various organizations like Caterpillar, Comcast, and the German Center for Diabetes Research are using Neo4j graph databases for applications like equipment maintenance, smart home services, and medical genomic research.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Big data emerged in the early 2000s and was first adopted by online companies like Google, eBay, and Facebook. It refers to data that exceeds the processing capacity of traditional databases due to its large size, speed of creation, and unstructured nature. The key attributes of big data are volume, variety, velocity and complexity. It comes from a variety of sources like sensors, social media, web logs, and photos. Analyzing big data can provide competitive advantages through insights from hidden patterns. While big data offers opportunities, organizations must ensure they have the right skills, manage costs, and address privacy issues.
The document discusses challenges and opportunities related to big data and high performance computing. It notes that computational power is increasing exponentially according to Moore's Law, but clock speeds have plateaued forcing a shift to multi-core processors. This is driving the need for parallel programming and new software approaches. Big data is also growing dramatically from various sources, such as sensors and social media. Analyzing this large, heterogeneous data requires new techniques in data mining, machine learning, and visualization. High performance computing and citizen science initiatives can help extract insights from big data to address important problems in health, environment, and other domains.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Banji Adenusi - big data prezzie - InfoSciBanji Adenusi
The document provides an overview of legal and technical aspects of big data. It defines big data as high-volume, high-velocity, and high-variety information that requires new processing methods. The document discusses key characteristics of big data including volume, velocity, variety, and veracity. It also summarizes infographics about the evolution of big data and provides an overview of technical challenges like data heterogeneity and privacy. On the legal side, it discusses issues around data ownership, intellectual property rights, data protection, and competition regulation in the use of big data.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Personalized News and Video Recomendation System at LinkSureLeanne Hwee
In recent years, the Internet industry has shifted more and more towards digital content distribution through online services. This presentation provides an overview of the overall system design and architecture of LinkSure News and Video Recommendations, the challenges encountered in practice, and the lessons learned from the production deployment of these systems at LinkSure. Specifically, we will highlight how news selection and personalisation of recommendations are formulated and addressed at LinkSure. By presenting our experiences in applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modelling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations.
Hawaii International Conference on Systems Sciences 2017. There are many opportunities for academics to submit papers for presentation at this very important conference which has sessions on Cognitive, Analytics, Big Data and much more. Haluk Demirkan, U Washington and Sergey Belov, IBM University Relations CEEMA made this presentation at Cognitive Systems Institute Speaker Series call on March 10, 2016.
Many companies face the challenge of building up a data science team from scratch and it can be hard to figure out how to start. In 2016, I was the first hire of a new data science team, with little infrastructure or strategy in place. Over the years, there were many different challenges for us to solve and mistakes to learn from as the team got more and more mature. This talk is about what I learned about the process of building up a data science team, from both my own experience in the past years and conversations with other data scientists in a similar situation.
The document outlines an agenda for a presentation on big data analytics, data science, and fast data. The agenda includes introductions to these topics as well as use cases. It discusses key characteristics of big data such as volume, complexity, and diverse data structures. Examples are provided of big data use cases in industries like healthcare, public services, and life sciences. The presentation aims to convey how these new data sources and analytical techniques can provide new insights.
This document discusses big data, including opportunities and risks. It covers big data technologies, the big data market, opportunities and risks related to capital trends, and issues around algorithmic accountability and privacy. The document contains several sections that describe topics like the Internet of Things, Hadoop, analytics approaches for static versus streaming data, big data challenges, and deep learning. It also includes examples of big data use cases and discusses hype cycles, adoption curves, and strategies for big data adoption.
The document discusses the Vienna Data Science Group (VDSG), a nonprofit organization that aims to promote data science. It has diverse members from various academic and professional fields. VDSG brings data science to life through talks, conferences, workshops, and networking events. It also discusses the impact of data science on society through applications like autonomous vehicles, smart home devices, and predictive analytics. Data science is changing areas like mobility, sports, finance, and advertising. Emerging technologies like the Internet of Things and predictive modeling raise important questions for society regarding privacy, ethics, and the limits of data-driven decisions.
NOVA Data Science Meetup 1/19/2017 - Presentation 1NOVA DATASCIENCE
The document discusses cognitive computing and its applications. It begins with an agenda that includes an overview of cognitive computing and examples of its use. It then discusses IBM Research's work leading to the development of Watson. Key points made include that most data is now unstructured, cognitive systems can reason, learn and understand like humans, and examples of cognitive computing applications in various domains.
Keynote: Graphs in Government_Lance Walter, CMONeo4j
This document contains an agenda and presentation slides for a Neo4j Graphs in Government event. The presentation introduces graph databases and Neo4j, discusses how graphs can help solve network-oriented problems, provides examples of graph use cases in various industries, and highlights new features in Neo4j 4.0 like easy management, unlimited scaling, and granular security. Case studies demonstrate how Neo4j has helped organizations like the US Army, MITRE, Adobe, and the German Center for Diabetes Research tackle complex data challenges.
Neo4j GraphTour New York_ State of the State_Amit Chaudhry Neo4jNeo4j
The document outlines an agenda for the Neo4j Graph Tour in New York that included discussions on graph databases, data management trends, case studies, and the future of graphs. It also provided examples of how various organizations like Caterpillar, Comcast, and the German Center for Diabetes Research are using Neo4j graph databases for applications like equipment maintenance, smart home services, and medical genomic research.
A Successful Data Strategy for Insurers in Volatile Times (EMEA)Denodo
This webinar discusses how data virtualization can help insurance companies address challenges from trends like digital disruption and mergers and acquisitions. It provides examples of how Denodo has helped customers like AXA XL and Prudential Financial improve data access, consistency, and governance through a data fabric architecture built on data virtualization. Key benefits highlighted include reduced data replication, increased data accuracy and reliability, role-based access controls, and more agile use of data to support initiatives like analytics and regulatory reporting.
A Successful Data Strategy for Insurers in Volatile Times (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3rpr4La
Data is an insurer’s most valuable asset. Capitalizing on all of that stored and incoming data to draw valuable insights for business decisions is what ultimately makes a competitive difference.
But, insurers face challenges when it comes to modernizing and digitizing their data architectures. Most organizations rely on traditional systems and data integration processes that are time consuming and slow. In addition, as many adopt cloud strategies, these traditional approaches fill the cloud modernization process with downtime and end user frustration.
This is why insurers need a flexible and easily adaptable data integration technology that allows them to keep up with the ever-changing and growing data environment.
Data virtualization is that modern data integration technology. It can support insurers not only on their journey to digitization, but also on their future infrastructure changes and innovations, adding agility, flexibility and efficiency to data architectures. Data virtualization can help insurance companies create 360° views of deals and claims processes as well as gather quick social media or sensor data for on-the-go risk profiling.
Join this on-demand webinar to:
- Find out why data virtualization should be a part of your enterprise data strategy
- See how this technology can help you capitalize on your data
- Hear how many of your peers are already leveraging the Denodo Platform for Data Virtualization and the benefits they’re observing
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
Oracle is a leading technology company focused on database software and cloud computing. It generates revenue from software licenses and cloud services. While Oracle faces competition from other large tech companies, its strengths include consulting services, global sales channels, and expertise in data storage and applications. The rise of big data presents both opportunities and challenges for Oracle to leverage new types and volumes of customer information through its products.
Big data analytics (BDA) provides capabilities for revealing additional value from big data. It examines large amounts of data from various sources to deliver insights that enable real-time decisions. BDA is different from data warehousing and business intelligence systems. The complexity of big data systems required developing specialized architectures like Hadoop, which processes large amounts of data in a timely and low cost manner. Big data challenges include capturing, storing, analyzing, sharing, transferring, visualizing, querying, updating, and ensuring privacy of large and diverse datasets.
The document discusses big data issues and challenges. It defines big data as large volumes of structured and unstructured data that is growing exponentially due to increased data generation. Some key challenges discussed include storage and processing limitations of exabytes of data, privacy and security risks, and the need for new skills and training to manage and analyze big data. Examples are given of large data projects in various domains like science, healthcare, and commerce that are driving big data growth.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
How to add security in dataops and devopsUlf Mattsson
The emerging DataOps is not Just DevOps for Data. According to Gartner, DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization.
The goal of DataOps is to create predictable delivery and change management of data, data models and related artifacts. DataOps uses technology to automate data delivery with the appropriate levels of security, quality and metadata to improve the use and value of data in a dynamic environment.
This session will discuss how to add Security in DataOps and DevOps.
Geospatial Intelligence Middle East 2013_Big Data_Steven RamageSteven Ramage
Some initial considerations and discussion points around geospatial big data. Location adds context and relevance. Need to consider a number of V factors including Value.
CTO Perspectives: What's Next for Data Management and Healthcare?Health Catalyst
Health Catalyst's Chief Technology Officer, Bryan Hinton, shares his perspective, thoughts, and insights on new and emerging trends for data management in healthcare. Bryan offers a brief presentation on what hospitals and healthcare systems can expect, followed by an extended Q&A.
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j
The document describes an event for GraphTalks Zürich in July 2017. It includes an agenda with presentations on graph databases and Neo4j, visualizing big data sets in the pharmaceutical industry using graph databases, and an open networking session.
The document discusses information systems and digital transformation. It covers topics like structured vs unstructured data, how businesses can leverage data and technology, and challenges around measuring success and the impact of digital initiatives. Examples are provided of how technology is changing work and forcing businesses to adapt. The document also outlines potential deliverables for a course, including a curation page, case study, and video presentation.
My testimony to NSTAC (http://www.dhs.gov/national-security-telecommunications-advisory-committee) on the need for more research data in big data networking analysis, better taxonomies/ontologies, and the need for more accessible tools, given December 8, 2015. A very insightful, thoughtful group of people. The administration really got it right with this one.
The document announces a Neo4j GraphTalks event in February 2016 focusing on semantic networks. The agenda includes an introduction to graph databases and Neo4j, a presentation on semantic product data management at Schleich, and a talk on building semantic networks quickly with Structr and Neo4j. An open discussion period will follow with additional speakers.
This document provides an agenda for a presentation on data analytics. It includes an introduction to data analytics concepts and examples of applications in various industries. A demo of Instant Insights, a cloud-based analytics tool, is presented. The document emphasizes that building analytics solutions through testing and iteration is better than just discussing ideas. Users should test solutions in the real world and measure user behavior to make data-driven decisions.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
6. Networks of People Business Processes Knowledge Networks
E.g., Risk management, Citizen
Service, Payments
E.g., Employees, Citizens,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
Data connections are increasing as rapidly as data volumes
The Rise of Connections in Data
7. Graphs have been universally recognized as a great solution
for specific types of problems
- Graph Problems -
and
recognition is GROWING!
8. Look at this data…
Element Depends On
A B
A C
A D
C H
D J
E F
E G
F J
G L
H I
J N
J M
L M
9. Element Depends On
A B
A C
A D
C H
D J
E F
E G
F J
G L
H I
J N
J M
L M
Time challenge #1: What if J fails?
?
11. If your business problem has a lot of dependencies - which in IT /
database terms are represented by JOINs between different entities -
and if solving for these dependencies in near real time is important
to you, then your problem is probably easiest solved with graph
technology - and we can safely call it a
GRAPH PROBLEM.
Identifying Graph Problems
12. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…
The Graph Problem at Scale: Panama
Papers
13.
14.
15. DB-engines Ranking of Database Categories
Graph DB
2013 2014 2015 2016 2017 2018 2019
• Graph DBMS
• Key-value stores
• Document stores
• Wide column store
• RDF stores
• Time stores
• Native XML DBMS
• Object oriented DBMS
• Multivalue DBMS
• Relational DBMS
Popularity of Graphs
17. “Choice (from 3:00 to 6:00), during
which the DBMS technology asset
class typically moves from
adolescent status into the early
mainstream. This is the phase of
highest growth in demand (market
penetration potentially reaches
50%), during which supply options
should grow…”
17
Graphs in the Early Mainstream
23. 76%FORTUNE
100
have adopted
or are piloting
Neo4jFinance
20 of top 25
7 of top 10
Software
Retail
7 of top 10
Airline
s
3 of top 5
Logistic
s
3 of top 5
Telco
4 of top 5
Hospitalit
y
3 of top 5
Growing
Adoption
in the
Enterprise
25. Background
• US IT consulting firm helped US Army streamline
equipment deployments and maintenance spending
• Saving lives by improving the operational readiness
of Army equipment like tanks, radios, transports,
aircraft, weaponry, etc.
Business Problem
• Needed to modernize procurement, budget and
logistics processes for equipment & spare parts
• Millions of connections among a tank’s bill-of-
materials, for example
• Improve “what if” cost calculations when planning
missions and troop deployments
• Mainframe systems required over 60 man-hrs to
calculate changes… planning took too long.
Solution and Benefits
• 118M nodes & 185M relationships
• Shed cost estimation times by 88%
• Improved parts delivery timing and accuracy
• DBA labor required dropped by 77%
• Equipment TCO more predictable
• Safer soldiers
US Army / Calibre Systems Equipment Logistics
Parts Assembly & Equipment Maintenance25
26. Background
• The MITRE Corporation is a federally-funded, not-
for-profit company that manages cybersecurity for
seven national research and development
laboratories around the United States including the
Center for National Security
• Founded in 1958, engaged in numerous public-
private partnerships as well as independent
research
Problem
• Constantly-evolving networks – devices,
configurations
• Huge volumes of “noise” from virus warnings to
failed logins
• Isolated datapoints with no context to separate the
most serious threats from the benign
• Existing database could not provide the context or
performance to manage a real-time environment
Solution and Benefits
• CyGraph - Agencies now have scalable,
comprehensive analytic and visualization capabilities
• Allowed agencies to capture a picture of their
cybersecurity environment that connects previously
isolated data points
Mitre Cybersecurity for Federal Agencies
26
“CyGraph’s comprehensive knowledge base tells
a much more complete story than that of basic
attack graphs or mission dependency models. [It]
includes potential attack-pattern relationships that
fill in gaps between known vulnerabilities and
threat indicators.”
- Steven Noel, Principal Cybersecurity
Engineer
27. Background
• Social network of 10M graphic artists
• Peer-to-peer evaluation of art and works-in-progress
• Job sourcing site for creatives
• Massive, millions of updates (reads & writes) to Activity
Feed
• 150 Mongos to 48 Cassandras to 3 Neo4j’s!
Business Problem
• Artists subscribe, appreciate and curate “galleries” of
works of their own and from other artists
• Activities Feed is how everyone receives updates
• 1st implementation was 150 MongoDB instances
• 2nd implementation shrunk to 48 Cassandras, but it
was still too slow and required heavy IT overhead
Solution and Benefits
• 3rd implementation shrunk to 3 Neo4j instances
• Saved over $500k in annual AWS fees
• Reduced data footprint from 50TB to 40GB
• Significantly easier to introduce new features like,
“New projects in you Network”
Adobe Behance Social Network of 10M Graphic Artists
Social Network27
EE Customer since 2016 Q
28. Background
• Over 7M citizens suffer from Diabetes
• Connecting over 400 researchers
• Incorporates over 50 databases, 100k’s of Excel
workbooks, 30 database of biological samples
• Sought to examine disease from as many angles as
possible.
Business Problem
• Genes are connected by proteins or to metabolites,
and patients are connected with their diets, etc…
• Needed to improve the utilization of immensely
technical data
• Needed to cater to doctors and researchers with
simple navigation, communication and connections
of the graph.
Solution and Benefits
• Dr. Alexander Jarasch, Head of Bioinformatics and
Data Management
• Scientists can conduct parallel research without asking
the same questions or repeating tests
• Built views like a liver sample knowledge graph
DZD - German Center for Diabetes Research
Medical Genomic Research28
EE Customer since 2016 Q