Architecture of Search Systems and Measuring the Search EffectivenessFindwise
Lecture made at the 19th of April 2012, at the Warsaw University of Technology. This is the 9th lecture in the regular course at master grade studies "Introduction to text mining".
Presenterad på Sitevisiondagarna 2012 i Örebro av Anders Häggdahl.
Presentationen bygger på vår långa erfarenhet av att ta fram och implementera sök och Findability-lösningar.
Vi visar exempel på hur några av de mer än 100 organisationer som vi arbetar med, tänker runt Findability och berättar om Findwise metod för att skapa maximal utväxling på våra kunders investeringar. Vidare går vi igenom vilka fördelar vårt samarbete med Sitevision ger användarna när man ska förbättra sin sökfunktionalitet.
Best Practices for Enterprise Search - What Leading Practitioners DoFindwise
Best Practices for Enterprise Search, from the perspective of practitioners. More focused on tasks and processes than technology. Based on data from the Enterprise Search and Findability Survey, other research, empirical evidence and the experience gained by Findwise consultants.
Architecture of Search Systems and Measuring the Search EffectivenessFindwise
Lecture made at the 19th of April 2012, at the Warsaw University of Technology. This is the 9th lecture in the regular course at master grade studies "Introduction to text mining".
Presenterad på Sitevisiondagarna 2012 i Örebro av Anders Häggdahl.
Presentationen bygger på vår långa erfarenhet av att ta fram och implementera sök och Findability-lösningar.
Vi visar exempel på hur några av de mer än 100 organisationer som vi arbetar med, tänker runt Findability och berättar om Findwise metod för att skapa maximal utväxling på våra kunders investeringar. Vidare går vi igenom vilka fördelar vårt samarbete med Sitevision ger användarna när man ska förbättra sin sökfunktionalitet.
Best Practices for Enterprise Search - What Leading Practitioners DoFindwise
Best Practices for Enterprise Search, from the perspective of practitioners. More focused on tasks and processes than technology. Based on data from the Enterprise Search and Findability Survey, other research, empirical evidence and the experience gained by Findwise consultants.
Findability Day 2016 - Enterprise Search and Findability Survey 2016Findwise
In April 2016, Findwise opened the fifth annual Enterprise Search and Findability Survey to investigate how search is managed and used globally. Mattias Ellison presents some of the most interesting founds at Findability Day 2016 in Stockholm.
Findability Day 2016 - Enterprise Search and Findability Survey 2016Findwise
In April 2016, Findwise opened the fifth annual Enterprise Search and Findability Survey to investigate how search is managed and used globally. Mattias Ellison presents some of the most interesting founds at Findability Day 2016 in Stockholm.
Elastic presents how The Guardian uses log analysis when creating articles and content on their website.
Findwise talks about Big Data and log analysis and the possibilities it gives.
Findability Day 2016 - Enterprise Search and Findability Survey 2016Findwise
In April 2016, Findwise opened the fifth annual Enterprise Search and Findability Survey to investigate how search is managed and used globally. Mattias Ellison presents some of the most interesting founds at Findability Day 2016 in Stockholm.
Findability Day 2016 - Enterprise Search and Findability Survey 2016Findwise
In April 2016, Findwise opened the fifth annual Enterprise Search and Findability Survey to investigate how search is managed and used globally. Mattias Ellison presents some of the most interesting founds at Findability Day 2016 in Stockholm.
Elastic presents how The Guardian uses log analysis when creating articles and content on their website.
Findwise talks about Big Data and log analysis and the possibilities it gives.
Opendatabay - Open Data Marketplace.pptxOpendatabay
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First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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).
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/
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
4. Content Inventory
What is where?
Phase 1: Scoping
Phase 2: Discovery
Phase 3: Analysis
Patterns and sources.
Explore.
Compliance?
5. Phase 1: Scoping
Where to look and what to look for?
Identify source and
content owners.
Identify and
prioritize sources.
1 2 3
Identify patterns
to look for.
6. Phase 1: Scoping
Where to look and what to look for?
Social security number
aaa-gg-ssss
Name
Aaaaa Aaaaaaa
Phone numbers
0xx - xxxx xx xx, 0xxx xx xx xx
IP address
aaa-gg-ssss
Date of birth
YYMMDD
E-mail address
aaaaa@aaaaa.aaaaa
9. Phase 3: Analysis
o Source by source
o Processes and routines
o Legal advice
Risk and compliance
Editor's Notes
Transparenta ringar.
Discover, classify and manage information.
Datainspektionen rekommenderar är att identifiera och klassificera persondata inom organisationen:
http://www.datainspektionen.se/Documents/vagledning-forberedelser-pua.pdf
Såväl strukturerad som ostrukturerad data.
Steg 1 som
Talk to source and content owners.
Personuppgiftsansvarig.
DPO – Data Protection Officer.
Personal data patterns may vary:
(entitty extraction, text analysis, pattern recognition)
Name
Named entity recognition
Name lists (ex. for Swedish names, name lists from Statistiska Centralbyrån)
Exception lists, manually or from HR system
Gender
Fixed values. PoS tagging. (eg. for Sweden: man, Plocka ut man som har rätt form).
Phone numbers
Pattern recognition, start with country code or riktnummer
Exception lists, manually or from HR system
E-mail addresses
Pattern recognition
Exception lists, manually or from HR system
IP address
Pattern recognition
Social security number
Pattern recognition
Date of birth
Named entity recognition
Pattern recognition combined with list of words that has to be close
Ethnicity
List of values combined with name or gender in the same sentence
Credit card numbers
Pattern recognition
Notes and comments about a person
Source specific
Log posts e.g. chat conversations, purchase history, support errands, transactions
Source specific
Find what you didn’t know was there.
Find duplicates
Entity extraction, text analys, pattern recognition.
Standard search gui or customized dashboard.
Gränsnittsbild. Mer riktig.