This document summarizes information about CID GmbH and its subsidiaries Pattern Science AG and CID Consulting GmbH. CID GmbH is a software development company based in Germany that has been developing digital business process solutions using Microsoft .NET since 1997. It has 160 employees. Pattern Science AG focuses on text mining, semantics, and machine learning research. CID Consulting GmbH provides technology consulting services and implements knowledge management and competitive intelligence processes.
Gain Competitive Advantage by Increasing Knowledge ProductivityCID GmbH
Gain Competitive Advantage by Increasing Knowledge Productivity: Link Insights from Big Data directly to Business Processes
• real-time monitoring of Big and Smart Data
• consolidated analysis of external (Web, Social Media, Deep Web, …) and internal (SharePoint, File Shares, Data Warehouses, …) data
• provision of direct business process support through dashboards and alerts
This presentation was held at the 2014 International Competitive Intelligence Conference in Bad Nauheim, Germany.
Competitive Intelligence & “Big Data“ – Information Monitoring, Analysis & Trend Detection in Real-Time
> how competitive intelligence can profit from knowledge Management & big data
> how to tackle information overload and analyze most different kinds of data from financial and market figures,
competitor and product information, news, scientific publications to social Media etc.
> possibilities and methods not only to manage knowledge but to gain insights and support decision making
> ways to prove the benefit of an investment in a ci tool for business
This presentation was held during the 2013 CiMi.CON Evolution Conference in Berlin, Germany
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
Gain Competitive Advantage by Increasing Knowledge ProductivityCID GmbH
Gain Competitive Advantage by Increasing Knowledge Productivity: Link Insights from Big Data directly to Business Processes
• real-time monitoring of Big and Smart Data
• consolidated analysis of external (Web, Social Media, Deep Web, …) and internal (SharePoint, File Shares, Data Warehouses, …) data
• provision of direct business process support through dashboards and alerts
This presentation was held at the 2014 International Competitive Intelligence Conference in Bad Nauheim, Germany.
Competitive Intelligence & “Big Data“ – Information Monitoring, Analysis & Trend Detection in Real-Time
> how competitive intelligence can profit from knowledge Management & big data
> how to tackle information overload and analyze most different kinds of data from financial and market figures,
competitor and product information, news, scientific publications to social Media etc.
> possibilities and methods not only to manage knowledge but to gain insights and support decision making
> ways to prove the benefit of an investment in a ci tool for business
This presentation was held during the 2013 CiMi.CON Evolution Conference in Berlin, Germany
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
Data-Ed Webinar: Monetizing Data Management - Show Me the MoneyDATAVERSITY
Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
In this webinar—designed to appeal to both business and IT attendees—your presenter will:
Describe multiple types of value produced through data-centric development and management practices
Expand on and beyond metrics meant for increasing revenues or decreasing costs—i.e. investments that directly impact an organization’s financial position
Detail how alternative statistics and valuations can be used to justify data management and quality initiatives
Data Analytics: An On-Ramp to a Better Understanding of Your BusinessSkoda Minotti
Data analytics is a hot topic in business today. But is it right for your business? What does it do for you, and most importantly, how do you get started? This executive overview explores the business implications of data analytics, while leaving the technicalities to the side.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Advanced Analytics Governance - Effective Model Management and StewardshipDATAVERSITY
There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics.
Join this webinar to learn best practices and vetted approaches for how to:
Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship
Adapt governance best practices for different analytics use cases
Confirm alignment of your analytics and BI strategy with critical business objectives
Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
Analysts in the line of business deal with a myriad of time-consuming data preparation and analytic challenges that often require IT or DBA intervention to deliver a requested dataset. Others have taught themselves “enough SQL to be dangerous”, learning the necessary code to extract the data needed to answer their business question. Self-service data analytics empowers these business analysts to take control of the entire analytics process, delivering the necessary results for better business decisions.
Join us to learn how self-service data analytics allows analysts to:
- Utilize a drag-and-drop workflow for data and analytic processes without writing code
- Minimize data movement and ensure data integrity through in-database capabilities
- Easily work across relational and non-relational databases to deliver faster business results
Self-service data analytics delivers a repeatable process that is transparent to not only business analysts, but also SQL coders and decision makers across the organization.
How Can You Calculate the Cost of Your Data?DATAVERSITY
Today, self-service, Cloud and big data technologies make new data preparation capabilities necessary…and possible. But, we've all been through the hype cycle and know the trough of disillusionment can come on hard and fast.
Organizations have been trying to solve the data quality problem and democratize insights for years spending millions of dollars and dedicating an increasing amount of resources to manage and govern the data. The result? Everyone is still looking to solve the problem.
Data preparation offers a new paradigm, but how can you avoid another round of minimal business impact? We’ll review a true data ROI model that helps organizations understand the value of existing versus modern data management architectures.
Implementing Big Data, NoSQL, & Hadoop - Bigger Is (Usually) BetterDATAVERSITY
From its widespread formal business practice to the scope of casual popular awareness, “Big Data” has a tendency to live up to its name. Featured in countless headlines, journal articles, and industry reviews, Big Data metrics and methods such as NoSQL and Hadoop have taken up plenty of the spotlight as of late. However, most of what has been written about these topics is focused on the hardware, services, and scale-out involved with them, a misguided focus that ignores the critical questions driving any shift in corporate strategy: what can Big Data do for you? Which approach to it best fits your organization? And perhaps most importantly, what is required on your end in order to spur a successful implementation process?
In the interest of answering these and other questions, this webinar will:
Provide guidance on how to think about and establish realistic Big Data management plans and expectations for generating business value, as well as on the means by which big data can complement existing data management practices
Introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL
Elaborate upon the prototyping nature of practicing Big Data techniques
Show how to demonstrate a sample use ca
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
Self-Service data analysis holds the promise of more rapid time-to-value for both business and IT users as advanced tooling & visualization helps make sense of raw and source data sets. Does this mean that the paradigm of ‘design-then-build’ that’s typical of data modeling is no longer relevant? Or is it more relevant than ever, as more eyes on the data means more questions about core business definitions.
Join Donna Burbank for this webinar to discuss the realities of where data modeling fits in this new paradigm.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
Scan any number of financial industry news publications, and stories regarding Wall Street’s hunger for new data sets to improve alpha abound. While this might seem like a new trend, monetizing data - or the ability to turn corporate data into revenue streams - has existed for decades. But both the supply side and the demand side have changed. On the supply side, the extreme variety of data that now exists (location-based, geospatial, socio-demographic, online search trends, pricing, etc.) combines with high computing power and new digital requirements to create a fertile data market environment. On the demand side, to remain competitive, companies in a wide variety of industries, not just the financial sector, are leveraging data in all forms to maintain an edge or be disruptive.
During this session we’ll explore what data monetization is and the forms it can take; characteristics of data that could make it more valuable to external parties; and key considerations in making data products available to external parties. Intellectual property, data privacy, and contractual issues will also be explored.
What You Don’t Know May Hurt You – Achieving Insight and Knowledge DiscoveryConcept Searching, Inc
Think you are too busy and solutions too expensive? Take another look at how text analytics and mining can boost your bottom line, through insight and knowledge discovery. The technique is simple and the results will probably surprise you.
Stay one step ahead of the competition and find out what’s really in your content. Guest speaker Russ Stalters, information management strategist and former BP executive, explores real-life knowledge discovery scenarios, and discusses the significant return on investment achieved.
This session provides an overview of text analytics and mining, and how the appropriate solution can be used to extract and refine the dataset, by business professionals with no expertise in programming languages or databases.
The ease of use makes this concept-based searching solution ideal for organizations with analysts and knowledge workers, who need to capture live information to address issues and develop opportunities, and are not technically oriented.
Speakers:
Russ Stalters – Information Management Strategist at Clear Path Solutions
Carla Mulley – Vice President of Marketing at Concept Searching
About
Evolution of Data, Data Science , Business Analytics, Applications, AI, ML, DL, Data science – Relationship, Tools for Data Science, Life cycle of data science with case study,
Algorithms for Data Science, Data Science Research Areas,
Future of Data Science.
Data-Ed Webinar: Monetizing Data Management - Show Me the MoneyDATAVERSITY
Practicality and profitability may share a page in the dictionary, but incorporating both into a data management plan can prove challenging. Many data professionals struggle to demonstrate tangible returns on data management investments, especially in industries such as healthcare where financial results aren’t necessarily an organization’s primary concern. The key to “monetizing” data management, therefore, is thinking about data in a different way: as an information solution rather than simply an IT one, using data to drive decision-making towards increased profits and potentially alternative returns on investment or value outcomes as well. Taking a broader view of data assets facilitates easier sharing of information across organizational silos, and allows for a wider understanding of the investment’s requirements and benefits.
In this webinar—designed to appeal to both business and IT attendees—your presenter will:
Describe multiple types of value produced through data-centric development and management practices
Expand on and beyond metrics meant for increasing revenues or decreasing costs—i.e. investments that directly impact an organization’s financial position
Detail how alternative statistics and valuations can be used to justify data management and quality initiatives
Data Analytics: An On-Ramp to a Better Understanding of Your BusinessSkoda Minotti
Data analytics is a hot topic in business today. But is it right for your business? What does it do for you, and most importantly, how do you get started? This executive overview explores the business implications of data analytics, while leaving the technicalities to the side.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Advanced Analytics Governance - Effective Model Management and StewardshipDATAVERSITY
There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics.
Join this webinar to learn best practices and vetted approaches for how to:
Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship
Adapt governance best practices for different analytics use cases
Confirm alignment of your analytics and BI strategy with critical business objectives
Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
Analysts in the line of business deal with a myriad of time-consuming data preparation and analytic challenges that often require IT or DBA intervention to deliver a requested dataset. Others have taught themselves “enough SQL to be dangerous”, learning the necessary code to extract the data needed to answer their business question. Self-service data analytics empowers these business analysts to take control of the entire analytics process, delivering the necessary results for better business decisions.
Join us to learn how self-service data analytics allows analysts to:
- Utilize a drag-and-drop workflow for data and analytic processes without writing code
- Minimize data movement and ensure data integrity through in-database capabilities
- Easily work across relational and non-relational databases to deliver faster business results
Self-service data analytics delivers a repeatable process that is transparent to not only business analysts, but also SQL coders and decision makers across the organization.
How Can You Calculate the Cost of Your Data?DATAVERSITY
Today, self-service, Cloud and big data technologies make new data preparation capabilities necessary…and possible. But, we've all been through the hype cycle and know the trough of disillusionment can come on hard and fast.
Organizations have been trying to solve the data quality problem and democratize insights for years spending millions of dollars and dedicating an increasing amount of resources to manage and govern the data. The result? Everyone is still looking to solve the problem.
Data preparation offers a new paradigm, but how can you avoid another round of minimal business impact? We’ll review a true data ROI model that helps organizations understand the value of existing versus modern data management architectures.
Implementing Big Data, NoSQL, & Hadoop - Bigger Is (Usually) BetterDATAVERSITY
From its widespread formal business practice to the scope of casual popular awareness, “Big Data” has a tendency to live up to its name. Featured in countless headlines, journal articles, and industry reviews, Big Data metrics and methods such as NoSQL and Hadoop have taken up plenty of the spotlight as of late. However, most of what has been written about these topics is focused on the hardware, services, and scale-out involved with them, a misguided focus that ignores the critical questions driving any shift in corporate strategy: what can Big Data do for you? Which approach to it best fits your organization? And perhaps most importantly, what is required on your end in order to spur a successful implementation process?
In the interest of answering these and other questions, this webinar will:
Provide guidance on how to think about and establish realistic Big Data management plans and expectations for generating business value, as well as on the means by which big data can complement existing data management practices
Introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL
Elaborate upon the prototyping nature of practicing Big Data techniques
Show how to demonstrate a sample use ca
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
Self-Service data analysis holds the promise of more rapid time-to-value for both business and IT users as advanced tooling & visualization helps make sense of raw and source data sets. Does this mean that the paradigm of ‘design-then-build’ that’s typical of data modeling is no longer relevant? Or is it more relevant than ever, as more eyes on the data means more questions about core business definitions.
Join Donna Burbank for this webinar to discuss the realities of where data modeling fits in this new paradigm.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
Scan any number of financial industry news publications, and stories regarding Wall Street’s hunger for new data sets to improve alpha abound. While this might seem like a new trend, monetizing data - or the ability to turn corporate data into revenue streams - has existed for decades. But both the supply side and the demand side have changed. On the supply side, the extreme variety of data that now exists (location-based, geospatial, socio-demographic, online search trends, pricing, etc.) combines with high computing power and new digital requirements to create a fertile data market environment. On the demand side, to remain competitive, companies in a wide variety of industries, not just the financial sector, are leveraging data in all forms to maintain an edge or be disruptive.
During this session we’ll explore what data monetization is and the forms it can take; characteristics of data that could make it more valuable to external parties; and key considerations in making data products available to external parties. Intellectual property, data privacy, and contractual issues will also be explored.
What You Don’t Know May Hurt You – Achieving Insight and Knowledge DiscoveryConcept Searching, Inc
Think you are too busy and solutions too expensive? Take another look at how text analytics and mining can boost your bottom line, through insight and knowledge discovery. The technique is simple and the results will probably surprise you.
Stay one step ahead of the competition and find out what’s really in your content. Guest speaker Russ Stalters, information management strategist and former BP executive, explores real-life knowledge discovery scenarios, and discusses the significant return on investment achieved.
This session provides an overview of text analytics and mining, and how the appropriate solution can be used to extract and refine the dataset, by business professionals with no expertise in programming languages or databases.
The ease of use makes this concept-based searching solution ideal for organizations with analysts and knowledge workers, who need to capture live information to address issues and develop opportunities, and are not technically oriented.
Speakers:
Russ Stalters – Information Management Strategist at Clear Path Solutions
Carla Mulley – Vice President of Marketing at Concept Searching
About
Evolution of Data, Data Science , Business Analytics, Applications, AI, ML, DL, Data science – Relationship, Tools for Data Science, Life cycle of data science with case study,
Algorithms for Data Science, Data Science Research Areas,
Future of Data Science.
Presentation about BigData from a German Webcast: http://business-services.heise.de/it-management/big-data/beitrag/big-data-technologie-einsatzgebiete-datenschutz-160.html?source=IBM_12_2013_IT_Conn
Complementing Political / Social Research with AI for a Deeper Understanding ...Inspirient
Presented at the Bitkom Big-Data.AI Summit on 14 September 2022 together with Kantar Public. We discuss how AI technology was used to automate survey analytics at Kantar Public Germany, and detail key business benefits such as improved efficiency, quick deliverables to the client, and advanced data validation and analysis.
This work is part of Kantar Public's Public Data Innovation Hub (https://www.kantarpublic.com/de/Unsere-Expertise/daten-und-fakten/public-data-innovation-hub). Supporting write-ups are available at https://www.inspirient.com/case-studies/survey_analysis_automation.php and https://www.inspirient.com/case-studies/survey_quality_assurance.php
Full abstract at https://big-data.ai/programme#Complementing-PoliticalSocial-Research-with-AI-for-a-Deeper-Understanding-of-Society
This talk was presented by Mrs. Gordana Djankovic, Lead Data Scientist at TeleSign, during Data Science Conference 4.0, as a part of ML & AI track.
You can find this talk presentation on the following link:
https://www.slideshare.net/Insitute_of_Contemporary_Sciences/deep-attention-model-for-triage-of-emergency-department-patients-djordje-gligorijevic
More info about Data Science Conference:
Website: http://datasciconference.com
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Facebook: https://www.facebook.com/DataSciConference/
Twitter: https://twitter.com/datasciconf
Flickr: https://www.flickr.com/photos/data-science-conference
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Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
IMS on Mainframe host many Enterprise Critical assets, transactional and batch applications as well as data. Analytics solutions apply to both!
Contact me for more details
Big Data kennen sehr viele IT-Experten, wenigstens haben Sie eine Vorstellung davon. In der Praxis arbeiten damit in Deutschland derzeit nur wenige. Dabei bringt Big Data ein ganz neues Momentum in moderne Softwarelösungen und ist im Kontext der Mobil-, Cloud- und Social-Veränderungen nicht wegzudenken. Big Data macht Software intelligent und damit auf eine ganz neue Art für die Benutzer erlebbar. Mit Big Data entstehen neue Softwarearchitekturen, weil Informationen völlig anders verarbeitet werden - nämlich schneller, differenzierter und oft mit dem Ziel, Schlüsse zu ziehen und Vorhersagen zu treffen.
In diesem Vortrag wird erläutert, wie moderne Softwarearchitekturen gestaltet werden, sodass Sie Big Data Paradigmen erfolgreich umsetzen und welche Vorteile sich für die zunehmend mobilen Softwarelösungen ergeben. Wir werfen zudem einen Blick auf die Potentiale und Optionen in Branchen wie Banken, Versicherung oder Handel.
Experimental transformation of ABS data into Data Cube Vocabulary (DCV) form...Alistair Hamilton
Presentation by Al Hamilton and Cody Johnson to Canberra Semantic Web Meetup Group on why producers of official statistics are interested in semantic web community (including Linked Open Data) and outlining experimental work by Cody Johnson on transforming selected Population Census data released by the ABS in SDMX-ML to RDF Data Cube Vocabulary format.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
CID and Predictive Policing at the 2015 European Police Congress in Berlin
1.
2. CID GmbH
• Digitization of Business Processes
since 1997
• Software Development based on
Microsoft .net
Pattern Science AG
• Research & Development on
Text Mining, Semantics &
Machine Learning since 2007
• Scientific Cooperations
CID Consulting GmbH
• Technology-focused Consulting
since 2008
• Implementation of
Knowledge Management and
Competitive Intelligence Processes
Hasselroth, Germany
• 160 Employees
• Founded 1997
Enterprise-ready
Software Solutions for
Efficient Processes
combined with
Innovative Research
and Development
Delivering Smart Answers
3. Mission at Police Services
Situation Overview
Analysis of Confiscated Data
Analysis of Paper-based Files
Analysis of Investigation Records, Documents, etc.
8. Solution
Data Collection
• CORPUS® crawls web data, imports files, integrates with
data bases and other systems (e.g. CRM, ERP,...)
• Focusing on text, CORPUS® additionally provides
processing and analysis of structured data (e.g. financial
figures).
9. Solution
Data Collection Processing
• Automated Processing of imported data:
• Automated Natural Language Processing and Semantic
Analysis for the exact indexing of keywords, names
and other concepts
• Special, customer-related Taxonomies
10. Solution
Data Collection Processing
• Real-time Search and Analysis for Interactive Exploration
• Semantic Faceted Search for precise and thorough
results
• Topic Search to answer vaguely described questions
• Automated summary of result sets for a quick overview
and additional impulses
Search
11. Search
Solution
Data Collection Processing
Visualize &
Share
• Automated Text Analysis for a first overview:
• Significant keywords and topics representing an
aggregated overview of large data sets
• Content-based and statistical relationships between
documents, actors, ...
12. Solution
Processing
Visualize &
Share
• Automated processing incorporating Digital Image
Analysis, Text Mining and Semantics
• Quick Overview of content and correlations using
Significance and Topic Analysis
• Interactive exploration based on Semantic Search and
Topic Search with real-time content analysis
SearchData Collection