Applications and "organizations" are merging into a new
type of "organization" - Fast Innovation Amsterdam (Fixxx): started using design thinking + agile development for digital transformation in the City of Amsterdam in 2015.
The document discusses e-business and business processes on the internet. It provides definitions of e-business from Margaret Rouse as the conduct of business processes on the internet, including buying/selling products and services, customer service, payments, production, and collaborating with partners. It also discusses definitions of business processes from Hammer & Champy, Rummler & Brache, and Davenport as a series of steps to produce a product/service, or a set of activities to create value for customers.
Using graph technology for multi-INT investigationsLinkurious
Linkurious is a graph analysis software that helps organizations identify insights hidden in complex data by providing a unified view of information from different sources and enabling new analytical capabilities. It breaks down data silos and reduces complexity for multi-INT (intelligence integration) investigations. The presentation discusses why the graph approach is useful for multi-INT analysis, demonstrates Linkurious Enterprise with examples of tax evasion and corruption, and shows how the intelligence analysis team at AEI uses it to gain insights from disparate data.
For decades, the intelligence community has been collecting and analyzing information to produce timely and actionable insights for intelligence consumers. But as the amount of information collected increases, analysts are facing new challenges in terms of data processing and analysis. In this presentation, we explore the possibilities that graph technology is offering for intelligence analysis.
Using Linkurious in your Enterprise Architecture projectsLinkurious
1) Linkurious is a graph visualization and analysis startup that helps customers understand complex connected data through its graph database and visualization tools. 2) Enterprise architects use Linkurious to model enterprise architecture data as a graph with various entity types and relationships in order to better understand interdependencies and perform impact analysis across business, data, applications and technology. 3) Linkurious allows architects to intuitively investigate architecture overviews, generate custom reports, and collaborate more effectively on enterprise architecture projects.
Big data business analytics | Introduction to Business AnalyticsShilpaKrishna6
This document provides an introduction to business analytics, including the types (descriptive, predictive, and prescriptive), tools (data visualization, business intelligence reporting software, self-service analytics platforms, statistical analysis, and big data platforms), roles (business user, project sponsor, project manager, business intelligence analyst, database administrator, data engineer, and data scientist), and lifecycle (problem definition, research, resource assessment, data acquisition, data storage, exploratory analysis, modeling, implementation, and developing deliverables).
Graph technology and data-journalism: the case of the Paradise PapersLinkurious
Discover how graph analysis and visualization technologies allowed the ICIJ journalists to highlight the suspicious relations between political figures and offshore companies in the Paradise Papers investigations.
Congratulations, your data is up and running in a graph database! This is the first step of many to unlocking the potential in your data. It’s easy to get mired in the complexities of graph technology and forget that real users, mere mortals, will need to use this information to inform mission critical tasks. To get the value out of your graph investment, you’ll need to provide an experience that enables users to explore and visualize your graph data in meaningful ways.
In this talk, we’ll take a hands on approach to applying user-centered strategies and leveraging the latest UI tools to rapidly create great experiences with graph data. Topics will include network analysis queries with Cypher and APOC, tailoring experiences to the intended audience and data, determining the the right visualization for the job and cutting through the clutter on choosing the right visualization tools.
Graph technologies have the potential to help businesses understand complex connected data. From financial crime to cyber-security to IT management, specific business requires custom applications. This is why we created the Linkurious SDK , a toolkit that enables you to quickly build secure and flexible applications to leverage the connections within your data or unveil hidden relationships.
Discover in this presentation the challenges of integrating graph technologies into enterprise applications; and how to use the Linkurious SDK to build a robust, secure and interactive graph application.
The document discusses e-business and business processes on the internet. It provides definitions of e-business from Margaret Rouse as the conduct of business processes on the internet, including buying/selling products and services, customer service, payments, production, and collaborating with partners. It also discusses definitions of business processes from Hammer & Champy, Rummler & Brache, and Davenport as a series of steps to produce a product/service, or a set of activities to create value for customers.
Using graph technology for multi-INT investigationsLinkurious
Linkurious is a graph analysis software that helps organizations identify insights hidden in complex data by providing a unified view of information from different sources and enabling new analytical capabilities. It breaks down data silos and reduces complexity for multi-INT (intelligence integration) investigations. The presentation discusses why the graph approach is useful for multi-INT analysis, demonstrates Linkurious Enterprise with examples of tax evasion and corruption, and shows how the intelligence analysis team at AEI uses it to gain insights from disparate data.
For decades, the intelligence community has been collecting and analyzing information to produce timely and actionable insights for intelligence consumers. But as the amount of information collected increases, analysts are facing new challenges in terms of data processing and analysis. In this presentation, we explore the possibilities that graph technology is offering for intelligence analysis.
Using Linkurious in your Enterprise Architecture projectsLinkurious
1) Linkurious is a graph visualization and analysis startup that helps customers understand complex connected data through its graph database and visualization tools. 2) Enterprise architects use Linkurious to model enterprise architecture data as a graph with various entity types and relationships in order to better understand interdependencies and perform impact analysis across business, data, applications and technology. 3) Linkurious allows architects to intuitively investigate architecture overviews, generate custom reports, and collaborate more effectively on enterprise architecture projects.
Big data business analytics | Introduction to Business AnalyticsShilpaKrishna6
This document provides an introduction to business analytics, including the types (descriptive, predictive, and prescriptive), tools (data visualization, business intelligence reporting software, self-service analytics platforms, statistical analysis, and big data platforms), roles (business user, project sponsor, project manager, business intelligence analyst, database administrator, data engineer, and data scientist), and lifecycle (problem definition, research, resource assessment, data acquisition, data storage, exploratory analysis, modeling, implementation, and developing deliverables).
Graph technology and data-journalism: the case of the Paradise PapersLinkurious
Discover how graph analysis and visualization technologies allowed the ICIJ journalists to highlight the suspicious relations between political figures and offshore companies in the Paradise Papers investigations.
Congratulations, your data is up and running in a graph database! This is the first step of many to unlocking the potential in your data. It’s easy to get mired in the complexities of graph technology and forget that real users, mere mortals, will need to use this information to inform mission critical tasks. To get the value out of your graph investment, you’ll need to provide an experience that enables users to explore and visualize your graph data in meaningful ways.
In this talk, we’ll take a hands on approach to applying user-centered strategies and leveraging the latest UI tools to rapidly create great experiences with graph data. Topics will include network analysis queries with Cypher and APOC, tailoring experiences to the intended audience and data, determining the the right visualization for the job and cutting through the clutter on choosing the right visualization tools.
Graph technologies have the potential to help businesses understand complex connected data. From financial crime to cyber-security to IT management, specific business requires custom applications. This is why we created the Linkurious SDK , a toolkit that enables you to quickly build secure and flexible applications to leverage the connections within your data or unveil hidden relationships.
Discover in this presentation the challenges of integrating graph technologies into enterprise applications; and how to use the Linkurious SDK to build a robust, secure and interactive graph application.
Geschäftliches Potential für System-Integratoren und Berater - Graphdatenban...Neo4j
This document provides an agenda for a Neo4j partner day event. The agenda includes sessions on the business potential of Neo4j for system integrators and consultants, the Neo4j partner program, and a case study on using Neo4j to analyze data from the Panama Papers leak. There are also sessions on networking breaks and lunch.
GraphTour - How to Build Next-Generation Solutions using Graph DatabasesNeo4j
The document provides an agenda and overview of a presentation about graph-based solutions using Neo4j for telecom services. The presentation covers topics like service assurance in telecoms using Neo4j to model dependencies, performing impact and root cause analysis, and using graphs for governance, metadata management and GDPR compliance through concepts like data modeling, transformation, and entitlement management. The conclusions emphasize that graphs are well-suited for these domains and that the combination of domain expertise with Neo4j's graph capabilities provides powerful solutions.
Reactive Reatime Big Data with Open Source Lambda Architecture - TechCampVN 2014Trieu Nguyen
This document discusses using a reactive lambda architecture with open source tools to solve real-time big data problems. It begins by defining big data and explaining that simply having data is not enough - you need to solve the right problems with the right team and tools. It then presents three example problems that could benefit from real-time big data solutions: disaster prediction and response, understanding customers through social media data, and optimizing marketing campaigns in real-time. The document proposes using a reactive lambda architecture along with open source frameworks like Hadoop, Spark, Storm and databases like Redis, HDFS and HBase to build streaming data pipelines and query data in real-time. It demonstrates this through a social media user tracking and personalized recommendations use
This document provides an agenda and information for a Neo4j GraphTalks event on DSGVO & Compliance. The agenda includes three presentations: an introduction to graph databases and Neo4j, a talk on using graphs for GDPR metadata management and data governance, and a session on successful Neo4j project implementation. The document also includes background information on Neo4j and graph databases.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch here: https://bit.ly/3719Bi7
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
-How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
-About the success McCormick has had as a result of seasoning the Machine Learning and Blockchain Landscape with data virtualization
New Ways to Deliver Business Outcomes with INtelligent Workstream CollaborationLetsConnect
The document summarizes a presentation given by Jim Puckett on intelligent workstream collaboration using IBM products. It discusses:
1) Using IBM Connections and Watson Workspace to enable intelligent team collaboration for targeted solutions.
2) Powering the ecosystem with Watson Work to integrate applications, discussions, content and tasks within teams.
3) Building on IBM Connections and IBM Docs capabilities with additional workflow features to orchestrate actions while AI assists to filter important information.
4) A demo use case showing how these products could improve sales productivity through intelligent collaboration.
Intelligent Collaboration driving Digital TransformationLetsConnect
The document discusses Eurapco's approach to simplicity in order to address challenges with their social connections platform. Some of the main challenges mentioned include a lack of exclusivity compared to competitors and a lack of control since only a small portion of users are employed by Eurapco. Feedback from users identified pain points such as the platform not being user friendly, too complex, and requiring too much time. To address these issues, Eurapco focuses on an approach to simplicity through customization options to make the platform easier to use and find relevant information.
Fighting financial crime with graph analysis at BIWA Summit 2017Linkurious
Additional details on our blog: https://linkurio.us/visualize-oracle-graph-data-ogma-library/
Discover how to use graph analysis to identify suspicious connections and unmask criminals. In this session, Jean will share his experience working on the Panama Papers or with banks and insurance companies (first-party fraud, anti-money laundering, insurance fraud). He will explain how to combine the kind of graph analytics enabled by Oracle Spatial and Graph with powerful graph visualization to help analysts detect, investigate and stop financial crime.
This document discusses lessons learned from 5 years of open data initiatives in Ghent, Belgium. It addresses developing a common vocabulary around data standards and metadata. It emphasizes that an organization's first priority with open data should be internal reuse to maximize data quality and link data internally. The document also stresses analyzing and tracking open data to personalize communication, and that future open data efforts should focus on internal reuse, involve data owners more, visualize the data value chain, and link open data to organizational processes.
Big data is the process of examining large volumes of diverse data types at high speeds to discover useful patterns and information. It involves acquiring data from traditional and unstructured sources, analyzing it using advanced statistical tools, and organizing the information to make real-time or quick decisions to positively impact the business. There are different types of analytics like descriptive, predictive, and prescriptive analytics and different types of big data like structured, semi-structured, and unstructured data.
The document summarizes a presentation about machine learning fundamentals and applications for maximizing online sales. It discusses different machine learning approaches like supervised vs. unsupervised learning, and examples like housing price prediction, customer segmentation, and fraud detection. It then presents a scenario of using machine learning to optimize suggestions for online shopping customers based on their behaviors and attributes. The presentation discusses building customer segments, improving suggestions over time, and the architecture and components needed for a reinforcement learning solution. It also covers limitations and improvement opportunities of machine learning adoption.
This is a brief survey of data journalism, including the kinds of issues data journalists tackle, key challenges involved, and some examples of notable work.
Data journalism covers a broad range of activities. Some journalists construct databases from scratch. Others make detailed visualizations that illuminate hidden patterns. Using data, journalists can uncover new areas for potential stories, discover systemic patterns, verify claims, and address issues with greater transparency and detail.
Transforming your application with ElasticsearchBrian Ritchie
Brian Ritchie will give a presentation on transforming applications with Elastic Search. Elastic Search is an open source, distributed search and analytics engine that can be used to add powerful search capabilities to applications. It allows storing and searching large volumes of data quickly and flexibly scales. The presentation will cover introducing Elastic Search, bringing application data into it, security considerations, and an example of putting it all together to build a searchable application using Elastic Search.
This document summarizes RecSys 2015, a conference on recommender systems. It discusses trends in academia versus industry, including that academia focuses on complex models and small datasets while industry uses simpler models like SVD on large "Big Data" with metrics like click-through rate and retention. It also summarizes several papers presented on topics like using social networks, cross-domain recommendations, and addressing the cold start problem. Industry sessions were held by companies like LinkedIn, Netflix, and Amazon discussing their recommendation architectures and focus on user experience over complex models.
Opportunities and methodological challenges of Big Data for official statist...Piet J.H. Daas
1) The document discusses opportunities and challenges of using Big Data for official statistics. It describes Big Data as data that is difficult to collect, store, or process using conventional statistical systems due to issues of volume, velocity, structure, or variety.
2) The author outlines their experiences at Statistics Netherlands using various Big Data sources like traffic sensor data, mobile phone data, and social media data. They discuss methodological challenges in accessing and analyzing large volumes of data, dealing with noisy and unstructured data, and addressing issues of selectivity.
3) The document emphasizes the need for new skills like data science, high performance computing, and people with open and pragmatic mindsets to work with Big Data. It also addresses privacy
INSEAD Sharing on Lazada Data Science and my JourneyEugene Yan Ziyou
Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP
This document provides information about P. Vishak, a first year MBA student studying Business Analytics at Bharathiyar University. It discusses the trends of SMAC - social media, mobility, analytics, and cloud computing. It explains how each component of SMAC impacts business operations and customer interactions. Specifically, it describes how social media and mobile technologies change communication and commerce, while analytics and cloud computing allow businesses to access and analyze large amounts of customer data in real-time to quickly adapt to changing markets. The document indicates analytics has the greatest impact and will continue growing as more objects generate petabytes of digital data daily.
The document provides an overview of IBM Mashup Center and discusses how it allows users to quickly create applications by combining information from multiple sources. Some key points:
- Mashups can be created in days rather than months and help access internal and external data through an easy-to-use visual interface without extensive coding.
- IBM Mashup Center provides pre-built connectors to commonly used systems and allows custom connectors to be created when needed.
- It includes features for discovering, re-using and sharing assets like widgets, feeds and applications via a central catalog to encourage collaboration.
- Examples are given of real-world mashups created for industries like aviation, manufacturing and government to provide benefits like time
Geschäftliches Potential für System-Integratoren und Berater - Graphdatenban...Neo4j
This document provides an agenda for a Neo4j partner day event. The agenda includes sessions on the business potential of Neo4j for system integrators and consultants, the Neo4j partner program, and a case study on using Neo4j to analyze data from the Panama Papers leak. There are also sessions on networking breaks and lunch.
GraphTour - How to Build Next-Generation Solutions using Graph DatabasesNeo4j
The document provides an agenda and overview of a presentation about graph-based solutions using Neo4j for telecom services. The presentation covers topics like service assurance in telecoms using Neo4j to model dependencies, performing impact and root cause analysis, and using graphs for governance, metadata management and GDPR compliance through concepts like data modeling, transformation, and entitlement management. The conclusions emphasize that graphs are well-suited for these domains and that the combination of domain expertise with Neo4j's graph capabilities provides powerful solutions.
Reactive Reatime Big Data with Open Source Lambda Architecture - TechCampVN 2014Trieu Nguyen
This document discusses using a reactive lambda architecture with open source tools to solve real-time big data problems. It begins by defining big data and explaining that simply having data is not enough - you need to solve the right problems with the right team and tools. It then presents three example problems that could benefit from real-time big data solutions: disaster prediction and response, understanding customers through social media data, and optimizing marketing campaigns in real-time. The document proposes using a reactive lambda architecture along with open source frameworks like Hadoop, Spark, Storm and databases like Redis, HDFS and HBase to build streaming data pipelines and query data in real-time. It demonstrates this through a social media user tracking and personalized recommendations use
This document provides an agenda and information for a Neo4j GraphTalks event on DSGVO & Compliance. The agenda includes three presentations: an introduction to graph databases and Neo4j, a talk on using graphs for GDPR metadata management and data governance, and a session on successful Neo4j project implementation. The document also includes background information on Neo4j and graph databases.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch here: https://bit.ly/3719Bi7
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
-How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
-About the success McCormick has had as a result of seasoning the Machine Learning and Blockchain Landscape with data virtualization
New Ways to Deliver Business Outcomes with INtelligent Workstream CollaborationLetsConnect
The document summarizes a presentation given by Jim Puckett on intelligent workstream collaboration using IBM products. It discusses:
1) Using IBM Connections and Watson Workspace to enable intelligent team collaboration for targeted solutions.
2) Powering the ecosystem with Watson Work to integrate applications, discussions, content and tasks within teams.
3) Building on IBM Connections and IBM Docs capabilities with additional workflow features to orchestrate actions while AI assists to filter important information.
4) A demo use case showing how these products could improve sales productivity through intelligent collaboration.
Intelligent Collaboration driving Digital TransformationLetsConnect
The document discusses Eurapco's approach to simplicity in order to address challenges with their social connections platform. Some of the main challenges mentioned include a lack of exclusivity compared to competitors and a lack of control since only a small portion of users are employed by Eurapco. Feedback from users identified pain points such as the platform not being user friendly, too complex, and requiring too much time. To address these issues, Eurapco focuses on an approach to simplicity through customization options to make the platform easier to use and find relevant information.
Fighting financial crime with graph analysis at BIWA Summit 2017Linkurious
Additional details on our blog: https://linkurio.us/visualize-oracle-graph-data-ogma-library/
Discover how to use graph analysis to identify suspicious connections and unmask criminals. In this session, Jean will share his experience working on the Panama Papers or with banks and insurance companies (first-party fraud, anti-money laundering, insurance fraud). He will explain how to combine the kind of graph analytics enabled by Oracle Spatial and Graph with powerful graph visualization to help analysts detect, investigate and stop financial crime.
This document discusses lessons learned from 5 years of open data initiatives in Ghent, Belgium. It addresses developing a common vocabulary around data standards and metadata. It emphasizes that an organization's first priority with open data should be internal reuse to maximize data quality and link data internally. The document also stresses analyzing and tracking open data to personalize communication, and that future open data efforts should focus on internal reuse, involve data owners more, visualize the data value chain, and link open data to organizational processes.
Big data is the process of examining large volumes of diverse data types at high speeds to discover useful patterns and information. It involves acquiring data from traditional and unstructured sources, analyzing it using advanced statistical tools, and organizing the information to make real-time or quick decisions to positively impact the business. There are different types of analytics like descriptive, predictive, and prescriptive analytics and different types of big data like structured, semi-structured, and unstructured data.
The document summarizes a presentation about machine learning fundamentals and applications for maximizing online sales. It discusses different machine learning approaches like supervised vs. unsupervised learning, and examples like housing price prediction, customer segmentation, and fraud detection. It then presents a scenario of using machine learning to optimize suggestions for online shopping customers based on their behaviors and attributes. The presentation discusses building customer segments, improving suggestions over time, and the architecture and components needed for a reinforcement learning solution. It also covers limitations and improvement opportunities of machine learning adoption.
This is a brief survey of data journalism, including the kinds of issues data journalists tackle, key challenges involved, and some examples of notable work.
Data journalism covers a broad range of activities. Some journalists construct databases from scratch. Others make detailed visualizations that illuminate hidden patterns. Using data, journalists can uncover new areas for potential stories, discover systemic patterns, verify claims, and address issues with greater transparency and detail.
Transforming your application with ElasticsearchBrian Ritchie
Brian Ritchie will give a presentation on transforming applications with Elastic Search. Elastic Search is an open source, distributed search and analytics engine that can be used to add powerful search capabilities to applications. It allows storing and searching large volumes of data quickly and flexibly scales. The presentation will cover introducing Elastic Search, bringing application data into it, security considerations, and an example of putting it all together to build a searchable application using Elastic Search.
This document summarizes RecSys 2015, a conference on recommender systems. It discusses trends in academia versus industry, including that academia focuses on complex models and small datasets while industry uses simpler models like SVD on large "Big Data" with metrics like click-through rate and retention. It also summarizes several papers presented on topics like using social networks, cross-domain recommendations, and addressing the cold start problem. Industry sessions were held by companies like LinkedIn, Netflix, and Amazon discussing their recommendation architectures and focus on user experience over complex models.
Opportunities and methodological challenges of Big Data for official statist...Piet J.H. Daas
1) The document discusses opportunities and challenges of using Big Data for official statistics. It describes Big Data as data that is difficult to collect, store, or process using conventional statistical systems due to issues of volume, velocity, structure, or variety.
2) The author outlines their experiences at Statistics Netherlands using various Big Data sources like traffic sensor data, mobile phone data, and social media data. They discuss methodological challenges in accessing and analyzing large volumes of data, dealing with noisy and unstructured data, and addressing issues of selectivity.
3) The document emphasizes the need for new skills like data science, high performance computing, and people with open and pragmatic mindsets to work with Big Data. It also addresses privacy
INSEAD Sharing on Lazada Data Science and my JourneyEugene Yan Ziyou
Sharing about how Lazada applies data science to improve customer and seller experience, and my personal journey to my current role in Lazada as Data Science Lead, VP
This document provides information about P. Vishak, a first year MBA student studying Business Analytics at Bharathiyar University. It discusses the trends of SMAC - social media, mobility, analytics, and cloud computing. It explains how each component of SMAC impacts business operations and customer interactions. Specifically, it describes how social media and mobile technologies change communication and commerce, while analytics and cloud computing allow businesses to access and analyze large amounts of customer data in real-time to quickly adapt to changing markets. The document indicates analytics has the greatest impact and will continue growing as more objects generate petabytes of digital data daily.
The document provides an overview of IBM Mashup Center and discusses how it allows users to quickly create applications by combining information from multiple sources. Some key points:
- Mashups can be created in days rather than months and help access internal and external data through an easy-to-use visual interface without extensive coding.
- IBM Mashup Center provides pre-built connectors to commonly used systems and allows custom connectors to be created when needed.
- It includes features for discovering, re-using and sharing assets like widgets, feeds and applications via a central catalog to encourage collaboration.
- Examples are given of real-world mashups created for industries like aviation, manufacturing and government to provide benefits like time
IBM Mashup Center allows users to quickly create new applications by combining existing data and services. It provides tools for developing widgets and mashups that unlock information from various sources, such as enterprise systems, the web, personal data, and departmental information. This enables faster application development and business insights through information remixing.
Innovation med big data – chr. hansens erfaringerMicrosoft
Mange steder er Big Data stadig det nye og ukendte, der ikke har topprioritet hos IT, da ”vi ikke har store datamængder”. Men Big Data er meget mere end store datamængder. I Chr. Hansen A/S har Forskning og Udvikling (Innovation) afdelingen arbejdet med værdien af data og som resultat etableret et tværfagligt BioInformatik-program på Big Data teknologier fra Microsoft.
Business intelligence and analytics systems use data from various sources to provide useful information and insights. These systems include tools for online analytical processing, data mining, visualization, and decision support. They help organizations make better decisions by analyzing large amounts of structured and unstructured data. Netflix is an example of a company that has gained a competitive advantage through business intelligence by using customer viewing data to provide personalized recommendations.
1) SMAC (social media, mobility, analytics, and cloud computing) allows businesses to improve operations and customer interactions with minimal resources and maximum reach.
2) Experts predict SMAC will be a major business technology driver in the next decade by making organizations more connected, collaborative, real-time, and productive.
3) Social media, mobile technologies, analytics, and cloud computing each provide new ways for businesses to engage customers and access technology and data to quickly adapt to changing markets.
18Mar14 Find the Hidden Signal in Market Data Noise Webinar Revolution Analytics
This document summarizes a webinar presented by Revolution Analytics on using R for financial applications.
The webinar covered how R was created in 1993 and is now used by over 2.5 million users. It discussed Revolution Analytics' products that enhance R for enterprise use, including capabilities for big data, distributed execution, and integration with various data sources. The webinar also provided an overview of how R and various packages can be used for tasks in empirical finance and highlighted OneTick, a market data management platform, and how it integrates with R for options analytics.
A Semantic Search Approach to Task-Completion EnginesDarío Garigliotti
Date: February 27, 2018
Venue: Stavanger, Norway. UiS TN910 - Innovation and Project Awareness
Please cite, link to or credit this presentation when using it or part of it in your work.
Looking beyond plain text for document representation in the enterpriseArjen de Vries
In many real life scenarios, searching for information is not the user's end goal. In this presentation I look into the specific example of corporate strategy and business development in a university setting.
In today's academic institutions, strategic questions are those that relate to dependency on funding instruments, the public private partnerships that exist (and those that should be extended!), and the match between topic areas addressed by the research staff and those claimed important by policy makers. The professional search tasks encountered to answer questions in this domain are usually addressed by business intelligence (BI) tools, and not by search engines. However, professionals are known to be busy people inspired by their own research interests, and not particularly fond of keeping the
customer relationship management (CRM) or knowledge management systems up to date for the organisation's strategic interest. This then results in incomplete and inaccurate data.
Instead of requiring research staff (or their administrative support) to provide this management information, I will illustrate by example how the desired information usually exists already in the documents inherent to the academic work process. Information retrieval could thus play an important role in the computer systems that support the business analytics involved, and could significantly improve the coverage of entities of interest - i.e., to reduce the effort involved in achieving good recall in business analytics. The ranking functionality over the enterprise's (textual) content should however not be an isolated component. Our example setting integrates the information derived from research proposals, research publications and the financial systems, providing an excellent motivation for a more unified approach to structured and unstructured data.
Big Data and official statistics with examples of their usePiet J.H. Daas
The document provides an overview of the work done by the Center for Big Data Statistics (CBDS) at Statistics Netherlands. It discusses several examples of using big data sources to produce official statistics:
1) Road sensor data was used to produce the first official big data-based statistics on traffic intensity and its correlation with GDP.
2) Mobile phone data was analyzed to produce statistics on mobility patterns, daytime population, and tourism.
3) AIS ship tracking data was analyzed to study ship movements and transhipment locations.
4) Web scraping and text analysis of company websites was used to identify innovative companies, including small companies not covered by traditional surveys.
5) Sentiment analysis and
Analyzing Social Media with Digital Methods. Possibilities, Requirements, and...Bernhard Rieder
Digital methods allow for the computational analysis of social media data through three main steps: data extraction via platform APIs, data processing and aggregation through extraction software, and data analysis and visualization using analysis software. While promising access to behavioral data at scale, social media analysis requires an understanding of each platform's data formalizations and technical limitations. Different analytical gestures can be applied through statistics, graph theory, and other methods to investigate patterns in content, users, and their relations.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
Overview and slides demonstrating our work helping small charities make choices and build Customer Relationship Management (CRM) systems
On 27 November 2023 Datawise London launched the Small Charity Data Journeys research report, holding a series of workshops to delve deeper into findings and explore ways of working.
This document discusses challenges that charities face in managing customer relationship management (CRM) systems and data. It notes that charities often have data stored in many different places like spreadsheets and various digital tools. This can make processes time-consuming and lack integrity. The document considers whether charities should stick with their current systems or switch to new ones. It then outlines several initiatives to help charities, such as mentoring programs, user groups, and consolidating resources. A workshop brainstormed further ideas like creating a questionnaire for CRM providers and demonstration workshops of different systems. The overall goal is to help charities navigate options and have the right data systems to better serve people and demonstrate their impact.
This document provides an overview of Amundsen, an open source data discovery and metadata platform developed by Lyft. It begins with an introduction to the challenges of data discovery and outlines Amundsen's architecture, which uses a graph database and search engine to provide metadata about data resources. The document discusses how Amundsen impacts users at Lyft by reducing time spent searching for data and discusses the project's community and future roadmap.
Splunk is a tool that allows users to search through log files and machine data from servers, databases, applications and other systems to troubleshoot issues and gain insights. The document provides examples of how Splunk was used to resolve a website outage by searching logs, track increased online traffic due to a celebrity tweet, and improve an online shopping experience. It also discusses how Splunk works, the types of machine data that can be analyzed, and how operational intelligence benefits organizations.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
1. If it ain't broke,
don't Fixxx it
On DataPunt, DataLab Amsterdam and Fixxx
@AmsterdamNL
By Johan Groenen (@JPAGroenen),
December 16, 2015
2. About me
- Computer Science @UniLeiden
- Web Application Development
- Start-ups (Product Owner, Lead Architect, Data Services
Architect)
- DataLab Fixxx
3. Current Developments
- Online and offline are merging
- mobile broadband
- new types of interfaces
- internet of things
- virtual/augmented reality
- ubiquitous computing
- Applications and "organizations" are merging into a new
type of "organization"
4. City Developments
- Smart City:
- Sensors
- Open Data & Open Government
- Communication and coordination
- Sharing economy and crowd intelligence
- "Permanent Beta" mentality (hacking)
- City as a Platform:
- Service Oriented Organization
- 2-way API's
- Decentralization
5. DataPunt
- Rebuilding Atlas (data visualization tool for Amsterdam
data) https://atlas.amsterdam.nl
- Combining many data sources (Open Data, Closed Data)
- Service Oriented Architecture
- RESTful APIs
- Need for API management platform
- New view on Data and the City (infrastructure)
- Open Data
- API's
- 2-way data services
6. DataLab Amsterdam
- Civil servants need new skillset: data science, application
hacking, agile and open
- Central "workplace" for DataPunt services, knowledge
center, development partner within city government
- Where to start?
7. Fixxx
- Scrum teams: UX, app dev, devOps, scrum master
- Tackle real, tangible problems with data driven solutions
- In the process create new valuable data sets
8. Fixxx: Why
- Need to try/prove start-up best practices in/to
government organization
- Need to fail fast: motivation momentum
9. Fixxx: How
- Show, don't tell
- Maximize chance of success using motivations as intake
criteria
- Limit resources: 3 to 4 person scrum team
- Limit timeframe: 3 months
- Limit scope: minimum viable product (keep it simple)
- Clear expectations: communicate, involve
- Maximize results: transfer process and skills
- Focus. Balance. Keep improving.
10. Fixxx: What
- Experience problem domain
- Rephrase the problem statement
- Find the hook
- Define minimum viable solution
- User centered development
- Continuous testing
- Educate problem owner
12. Explore problem domain
- Be there, experience the problem
- Listen to people
- Look around: processes, software, relations,
stakeholders, attitudes
- Test all assumptions
- Document (but keep it simple)