We interact with an increasing amount of data but classical data structures and algorithms can't fit our requirements anymore. This talk is to present the probabilistic algorithms and data structures and describe the main areas of their applications.
Too Much Data? - Just Sample, Just Hash, ...Andrii Gakhov
Code & Supply | Pittsburgh Meetup | May 31, 2019
Probabilistic Data Structures and Algorithms (PDSA) is a common name of data structures based on different hashing techniques. They have been incorporated into Spark SQL. They are also used by Amazon Redshift and Google BigQuery, Redis and Elasticsearch, and many others. Consequently, PDSA is not just some interesting academic topic.
Book "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 978-3748190486 ) https://pdsa.gakhov.com
Probabilistic data structures. Part 3. FrequencyAndrii Gakhov
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com
In the presentation, I described popular and very simple data structures and algorithms to estimate the frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm, and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. On the other hand, when one is interested only in simple additive metrics like total page views or average price of conversion, it is obvious that raw data can be efficiently summarized, for example, on a daily basis or using simple in-stream counters. Computation of more advanced metrics like a number of unique visitor or most frequent items is more challenging and requires a lot of resources if implemented straightforwardly. In this article, I provide an overview of probabilistic data structures that allow one to estimate these and many other metrics and trade precision of the estimations for the memory consumption.
Probabilistic data structures. Part 2. CardinalityAndrii Gakhov
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com
In the presentation, I described common data structures and algorithms to estimate the number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog, and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.
Big Data Day LA 2015 - Large Scale Distinct Count -- The HyperLogLog algorith...Data Con LA
"At OpenX we not only use the tools in big data ecosystems to solve our business problems, but also explore the cutting edge algorithms for practical uses. HyperLogLog is one of the algorithm that we use intensively in our internal system. It has really low computation cost and can easily plug into map-reduce framework (hadoop or spark). Some of the applications that worth to highlight are:
* high cardinality test
* distinct count of unique users over time
* Visualize hyperloglog for fraud detection"
Too Much Data? - Just Sample, Just Hash, ...Andrii Gakhov
Code & Supply | Pittsburgh Meetup | May 31, 2019
Probabilistic Data Structures and Algorithms (PDSA) is a common name of data structures based on different hashing techniques. They have been incorporated into Spark SQL. They are also used by Amazon Redshift and Google BigQuery, Redis and Elasticsearch, and many others. Consequently, PDSA is not just some interesting academic topic.
Book "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 978-3748190486 ) https://pdsa.gakhov.com
Probabilistic data structures. Part 3. FrequencyAndrii Gakhov
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com
In the presentation, I described popular and very simple data structures and algorithms to estimate the frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm, and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. On the other hand, when one is interested only in simple additive metrics like total page views or average price of conversion, it is obvious that raw data can be efficiently summarized, for example, on a daily basis or using simple in-stream counters. Computation of more advanced metrics like a number of unique visitor or most frequent items is more challenging and requires a lot of resources if implemented straightforwardly. In this article, I provide an overview of probabilistic data structures that allow one to estimate these and many other metrics and trade precision of the estimations for the memory consumption.
Probabilistic data structures. Part 2. CardinalityAndrii Gakhov
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com
In the presentation, I described common data structures and algorithms to estimate the number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog, and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.
Big Data Day LA 2015 - Large Scale Distinct Count -- The HyperLogLog algorith...Data Con LA
"At OpenX we not only use the tools in big data ecosystems to solve our business problems, but also explore the cutting edge algorithms for practical uses. HyperLogLog is one of the algorithm that we use intensively in our internal system. It has really low computation cost and can easily plug into map-reduce framework (hadoop or spark). Some of the applications that worth to highlight are:
* high cardinality test
* distinct count of unique users over time
* Visualize hyperloglog for fraud detection"
An overview of streaming algorithms: what they are, what the general principles regarding them are, and how they fit into a big data architecture. Also four specific examples of streaming algorithms and use-cases.
STRIP: stream learning of influence probabilities.Albert Bifet
Influence-driven diffusion of information is a fundamental process in social networks. Learning the latent variables of such process, i.e., the influence strength along each link, is a central question towards understanding the structure and function of complex networks, modeling information cascades, and developing applications such as viral marketing.
Motivated by modern microblogging platforms, such as twitter, in this paper we study the problem of learning influence probabilities in a data-stream scenario, in which the network topology is relatively stable and the challenge of a learning algorithm is to keep up with a continuous stream of tweets using a small amount of time and memory. Our contribution is a number of randomized approximation algorithms, categorized according to the available space (superlinear, linear, and sublinear in the number of nodes n) and according to different models (landmark and sliding window). Among several results, we show that we can learn influence probabilities with one pass over the data, using O(nlog n) space, in both the landmark model and the sliding-window model, and we further show that our algorithm is within a logarithmic factor of optimal.
For truly large graphs, when one needs to operate with sublinear space, we show that we can still learn influence probabilities in one pass, assuming that we restrict our attention to the most active users.
Our thorough experimental evaluation on large social graph demonstrates that the empirical performance of our algorithms agrees with that predicted by the theory.
Graph Algorithms, Sparse Algebra, and the GraphBLAS with Janice McMahonChristopher Conlan
This talk will provide a very brief overview of graph algorithms and their expression using sparse linear algebra, followed by a high-level description of the GraphBLAS library and its usage.
Graphs are among the most important abstract data types in computer science, and the algorithms that operate on them are critical to modern life. Algorithms on graphs are applied in many ways in today's world—from Web rankings to metabolic networks, from finite element meshes to semantic graphs. Graphs have been shown to be powerful tools for modeling these complex problems because of their simplicity and generality. GraphBLAS is an API specification that defines standard building blocks for graph algorithms in the language of linear algebra. Graph algorithms have long taken advantage of the idea that a graph can be represented as a matrix, and graph operations can be performed as linear transformations and other linear algebraic operations on sparse matrices. For example, matrix-vector multiplication can be used to perform a step in a breadth-first search. The GraphBLAS specification (and the various libraries that implement it) provides data structures and functions to compute these linear algebraic operations. In particular, the GraphBLAS specifies sparse matrix objects which map well to graphs where vertices are likely connected to relatively few neighbors (i.e. the degree of a vertex is significantly smaller than the total number of vertices in the graph). The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance.
The difference between a good data scientist and a great data scientist is understanding what's happening "under the hood."
Computer engineer Janice McMahon and data scientist Chris Conlan will give a crash course in computational efficiency tailored for data scientists.
Big data serving: Processing and inference at scale in real timeItai Yaffe
Jon Bratseth (VP Architect) @ Verizon Media:
The big data world has mature technologies for offline analysis and learning from data, but have lacked options for making data-driven decisions in real time.
When it is sufficient to consider a single data point model servers such as TensorFlow serving can be used but in many cases you want to consider many data points to make decisions.
This is a difficult engineering problem combining state, distributed algorithms and low latency, but solving it often makes it possible to create far superior solutions when applying machine learning.
This talk will explain why this is a hard problem, show the advantages of solving it, and introduce the open source Vespa.ai platform which is used to implement such solutions in some of the largest scale problems in the world including the world's third largest ad serving system.
source{d} is building the open-source components to enable large-scale code analysis and machine learning on source code. Their powerful tools can ingest all of the world’s public git repositories turning code into ASTs ready for machine learning and other analyses, all exposed through a flexible and friendly API. Francesc will show you how to run machine learning on source code with a series of live demos.
An overview of streaming algorithms: what they are, what the general principles regarding them are, and how they fit into a big data architecture. Also four specific examples of streaming algorithms and use-cases.
STRIP: stream learning of influence probabilities.Albert Bifet
Influence-driven diffusion of information is a fundamental process in social networks. Learning the latent variables of such process, i.e., the influence strength along each link, is a central question towards understanding the structure and function of complex networks, modeling information cascades, and developing applications such as viral marketing.
Motivated by modern microblogging platforms, such as twitter, in this paper we study the problem of learning influence probabilities in a data-stream scenario, in which the network topology is relatively stable and the challenge of a learning algorithm is to keep up with a continuous stream of tweets using a small amount of time and memory. Our contribution is a number of randomized approximation algorithms, categorized according to the available space (superlinear, linear, and sublinear in the number of nodes n) and according to different models (landmark and sliding window). Among several results, we show that we can learn influence probabilities with one pass over the data, using O(nlog n) space, in both the landmark model and the sliding-window model, and we further show that our algorithm is within a logarithmic factor of optimal.
For truly large graphs, when one needs to operate with sublinear space, we show that we can still learn influence probabilities in one pass, assuming that we restrict our attention to the most active users.
Our thorough experimental evaluation on large social graph demonstrates that the empirical performance of our algorithms agrees with that predicted by the theory.
Graph Algorithms, Sparse Algebra, and the GraphBLAS with Janice McMahonChristopher Conlan
This talk will provide a very brief overview of graph algorithms and their expression using sparse linear algebra, followed by a high-level description of the GraphBLAS library and its usage.
Graphs are among the most important abstract data types in computer science, and the algorithms that operate on them are critical to modern life. Algorithms on graphs are applied in many ways in today's world—from Web rankings to metabolic networks, from finite element meshes to semantic graphs. Graphs have been shown to be powerful tools for modeling these complex problems because of their simplicity and generality. GraphBLAS is an API specification that defines standard building blocks for graph algorithms in the language of linear algebra. Graph algorithms have long taken advantage of the idea that a graph can be represented as a matrix, and graph operations can be performed as linear transformations and other linear algebraic operations on sparse matrices. For example, matrix-vector multiplication can be used to perform a step in a breadth-first search. The GraphBLAS specification (and the various libraries that implement it) provides data structures and functions to compute these linear algebraic operations. In particular, the GraphBLAS specifies sparse matrix objects which map well to graphs where vertices are likely connected to relatively few neighbors (i.e. the degree of a vertex is significantly smaller than the total number of vertices in the graph). The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance.
The difference between a good data scientist and a great data scientist is understanding what's happening "under the hood."
Computer engineer Janice McMahon and data scientist Chris Conlan will give a crash course in computational efficiency tailored for data scientists.
Big data serving: Processing and inference at scale in real timeItai Yaffe
Jon Bratseth (VP Architect) @ Verizon Media:
The big data world has mature technologies for offline analysis and learning from data, but have lacked options for making data-driven decisions in real time.
When it is sufficient to consider a single data point model servers such as TensorFlow serving can be used but in many cases you want to consider many data points to make decisions.
This is a difficult engineering problem combining state, distributed algorithms and low latency, but solving it often makes it possible to create far superior solutions when applying machine learning.
This talk will explain why this is a hard problem, show the advantages of solving it, and introduce the open source Vespa.ai platform which is used to implement such solutions in some of the largest scale problems in the world including the world's third largest ad serving system.
source{d} is building the open-source components to enable large-scale code analysis and machine learning on source code. Their powerful tools can ingest all of the world’s public git repositories turning code into ASTs ready for machine learning and other analyses, all exposed through a flexible and friendly API. Francesc will show you how to run machine learning on source code with a series of live demos.
Performance Analysis of Hashing Mathods on the Employment of App IJECEIAES
The administrative process carried out continuously produces large data. So the search process takes a long time. The search process by hashing methods can save time faster. Hashing is methods that directly access data in a table by making references to the key that hashing becomes the address in the table. The performance analysis of the hashing method is done by the number of 18 digit character values. The process of analysis is done on applications that have been implemented in the application. The algorithm of hashing method analyzed is progressive overflow (PO) and linear quotient (LQ). The main purpose of performance analysis of hashing method is to know how gig the performance of each method. The results analyzed showed the average value of collision with 15 keys in the analysis of 53.3% yield the same value, while 46.7% showed the linear quotient has better performance.
In this deck, Torsten Hoefler from ETH Zurich presents: Data-Centric Parallel Programming.
"The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating code definition from its optimization. We show how to tune several applications in this model and IR. Furthermore, we show a global, datacentric view of a state-of-the-art quantum transport simulator to optimize its execution on supercomputers. The approach yields coarse and fine-grained data-movement characteristics, which are used for performance and communication modeling, communication avoidance, and data-layout transformations. The transformations are tuned for the Piz Daint and Summit supercomputers, where each platform requires different caching and fusion strategies to perform optimally. We show that SDFGs deliver competitive performance, allowing domain scientists to develop applications naturally and port them to approach peak hardware performance without modifying the original scientific code."
Watch the video: https://wp.mep3RLHQ-kup
Learn more: http://htor.inf.ethz.ch
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This presentation describes a intelligent IT monitoring solution that uses Nagios as source of information, Esper as the CEP engine and a PCA algorithm.
ScaleGraph - A High-Performance Library for Billion-Scale Graph AnalyticsToyotaro Suzumura
Please cite the following paper:
Toyotaro Suzumura and Koji. Ueno, "ScaleGraph: A high-performance library for billion-scale graph analytics," 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015, pp. 76-84.
doi: 10.1109/BigData.2015.7363744
Recently, large-scale graph analytics has become a very popular topic owing to the emergence of gigantic graphs whose number of vertices and edges is in millions, billions or even trillions. Many graph analytics libraries and frameworks have been proposed with various computational models and programming languages to deal with such graphs. X10 programming language is a PGAS language that aims at both software performance and programmer's productivity. We introduce ScaleGraph library developed using X10 programming to illustrate the use of X10 for large-scale graph analytics. ScaleGraph library provides XPregel framework that is inspired by Google's Pregel computation model, serving as a building block for implementing graph kernels. We also optimized X10 runtime in some parts such as collective communication and memory management. We evaluated the performance and scalability of ScaleGraph libraries. The result shows that most graph kernels have good performance and scalability. ScaleGraph library is 9.4 times faster than Giraph in the experiment of PageRank with 16 machine nodes. To the best of our knowledge, ScaleGraph is the first X10-based library to address performance, scalability and productivity issues in dealing with large-scale graph analytics.
Talk given at Los Alamos National Labs in Fall 2015.
As research becomes more data-intensive and platforms become more heterogeneous, we need to shift focus from performance to productivity.
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018Codemotion
Monitoring an entire application is not a simple task, but with the right tools it is not a hard task either. However, events like Black Friday can push your application to the limit, and even cause crashes. As the system is stressed, it generates a lot more logs, which may crash the monitoring system as well. In this talk I will walk through the best practices when using the Elastic Stack to centralize and monitor your logs. I will also share some tricks to help you with the huge increase of traffic typical in Black Fridays.
Building graphs to discover information by David Martínez at Big Data Spain 2015Big Data Spain
The basic challenge of a data scientist is to unveil information from raw data. Traditional machine learning algorithms have treated “pure” data analytics situations that should comply with a set of restrictions, such as access to labels, a clear prediction objective… However, the reality in practice shows that, due to the wide spread of data science nowadays, the exception is the norm and it is usual to encounter situations that depend on gathering information from raw data which lacks any kind of structure, or objective that classic approaches assume. In these situations, building a graph that encodes the information we are trying to unveil is the most intuitive place to start or even the only one feasible when we lack any field knowledge or previously stated aim. Unfortunately, building a graph when the number of nodes is huge from scratch is a challenging task computationally, and requires some approximations to make it feasible. In this review, we will talk about the most standard way of building those graphs in practice, and how to exploit them to solve data science tasks.
Session presented at Big Data Spain 2015 Conference
15th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/thu/slot-11.html#spch11.2
Towards an Incremental Schema-level Index for Distributed Linked Open Data G...Till Blume
Semi-structured, schema-free data formats are used in many applications because their flexibility enables simple data exchange. Especially graph data formats like RDF have become well established in the Web of Data. For the Web of Data, it is known that data instances are not only added, changed, and removed regularly, but that their schemas are also subject to enormous changes over time. Unfortunately, the collection, indexing, and analysis of the evolution of data schemas on the web is still in its infancy. To enable a detailed analysis of the evolution of Linked Open Data, we lay the foundation for the implementation of incremental schema-level indices for the Web of Data. Unlike existing schema-level indices, incremental schema-level indices have an efficient update mechanism to avoid costly recomputations of the entire index. This enables us to monitor changes to data instances at schema-level, trace changes, and ultimately provide an always up-to-date schema-level index for the Web of Data. In this paper, we analyze in detail the challenges of updating arbitrary schema-level indices for the Web of Data. To this end, we extend our previously developed meta model FLuID. In addition, we outline an algorithm for performing the updates.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
Using Apache Pulsar to Provide Real-Time IoT Analytics on the EdgeDataWorks Summit
The business value of data decreases rapidly after it is created, particularly in use cases such as fraud prevention, cybersecurity, and real-time system monitoring. The high-volume, high-velocity datasets used to feed these use cases often contain valuable, but perishable, insights that must be acted upon immediately.
In order to maximize the value of their data enterprises must fundamentally change their approach to processing real-time data to focusing reducing their decision latency on the perishable insights that exist within their real-time data streams. Thereby enabling the organization to act upon them while the window of opportunity is open.
Generating timely insights in a high-volume, high-velocity data environment is challenging for a multitude of reasons. As the volume of data increases, so does the amount of time required to transmit it back to the datacenter and process it. Secondly, as the velocity of the data increases, the faster the data and the insights derived from it lose value.
In this talk, we will present a solution based on Apache Pulsar Functions that significantly reduces decision latency by using probabilistic algorithms to perform analytic calculations on the edge.
Let's start GraphQL: structure, behavior, and architectureAndrii Gakhov
In this talk, I describe the path to start with GraphQL in a company that has experience with Python stack and REST API. We go from the definition of GraphQL, via behavioral aspects and data management, to the most common architectural questions.
Implementing a Fileserver with Nginx and LuaAndrii Gakhov
Using the power of Nginx it is easy to implement quite complex logic of file upload with metadata and authorization support, and without need of any heavy application server. In this article you can find the basic implementation of such Fileserver using Nginx and Lua only.
Have you heard about Salad Olivje, Vereniki, Pirogi and Bliny, but you are unsure what it is all about? This easy Pecha Kucha presentation can help you to become an expert :)
Probabilistic data structures. Part 4. SimilarityAndrii Gakhov
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com
In this presentation, I described popular algorithms that employed Locality Sensitive Hashing (LSH) to solve similarity-related problems. I started with LSH in general and then switched to such algorithms as MinHash (LSH for Jaccard similarity) and SimHash (LSH for cosine similarity). Each approach came with some math that was behind it and simple examples to clarify the theory statements.
Some highlights and impression from the API Days Berlin + API Strat Europe // April 24-25, 2015
* microservices
* hypermedia API
* swagger
* 3scale API Gateway
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications
1. Andrii Gakhov, PhD
Exceeding Classical:
Probabilistic Data Structures
in Data-Intensive Applications
EuroSciPy 2019
Bilbao, Spain
2. Andrii Gakhov
Senior Software Engineer
at Ferret Go GmbH, Germany
Ph.D. in Mathematical Modelling,
M.Sc. in Applied Mathematics
Twitter: @gakhov | Website: gakhov.com
Probabilistic Data Structures
and Algorithms
for Big Data Applications
ISBN: 9783748190486
https://pdsa.gakhov.com
4. Bioinformatics: Counting k-mers in DNA
Counting substrings of length k in DNA sequence data (k-mers) is
essential in bioinformatics, for instance, for metagenomic sequencing.
A large fraction of the storage is spent on storing k-mers with sequencing
errors and which are observed only a single time in the data*.
Can we efficiently avoid to persist such invalid substrings?
Can we efficiently count valid substrings?
* Pritchard, J.K.: Efficient counting of k-mers in DNA sequences using a bloom filter. BMC Bioinformatics 12(1), 333, 2011
For example, the team that sequenced the giant panda genome needed to
count 8.62 billion 27-mers, where 68% were low-coverage k-mers.
5. 1. Data-Intensive Applications
in Big Data epoch
Exceeding Classical: Probabilistic Data Structures in Data-Intensive Applications
EuroSciPy 2019Andrii Gakhov @gakhov
6. What is Big Data?
Doug Laney in 2001 described Big Data datasets as such that
contain greater variety arriving in increasing volumes and with ever-
higher velocity. Today this is known as the famous 3V’s of Big Data.
Big
Data
Velocity Variety
Volume expresses the amount of data
describes the speed at which data is arriving refers to the number of types of data
7. What is Big Data?
Big Data is more than simply a
matter of size.
Big Data does not refer to data, it
refers to technology.
The datasets of Big Data are larger, more complex, and
generated more rapidly than our current resources can handle.
Image: https://www.freepngimg.com/electronics/technology
8. 2. Probabilistic Data Structures
and Algorithms
Exceeding Classical: Probabilistic Data Structures in Data-Intensive Applications
EuroSciPy 2019Andrii Gakhov @gakhov
9. Probabilistic Data Structures and Algorithms (PDSA)
A family of advanced approaches that are optimized to
use sublinear memory and constant execution time.
Cannot provide the exact answers and have some probability of error.
error
resources
The tradeoff between the error and the resources is another feature
that distinguish the algorithms and data structures of this family.
10. PDSA in Big Data Ecosystem
Count-Min Sketch
Count Sketch
Bloom Filter
Quotient Filter
Cuckoo Filter
Linear Counting
FM Sketch
LogLog
HyperLogLog
Random Sampling t-digestq-digestGreenwald-Khanna
MinHash
SimHash
LSH
Counting
find the number of unique elements
Membership
keep track of indexed elements
Rank approximate percentiles and quantiles
Frequency
estimate frequencies of elements
Similarity
find similar documents
Big
Data
Velocity Variety
Volume
11. PDSA in Apache Spark SQL (PySpark interface)
q-quantile estimation (Greenwald-Khanna)
# pyspark.sql.DataFrameStatFunctions(df).approxQuantile
df.approxQuantile("language", [0.5], 0.25)
Approximate number of distinct elements (HyperLogLog++)
#pyspark.sql.functions.approx_count_distinct
df.agg(approx_count_distinct(df.language).alias('lang')).collect()
Spark SQL is Apache Spark's module for working with structured data.
14. Frequency: Challenge
A hashtag is used to index a topic on Twitter and allows people to easily follow
items they are interested in. Hashtags are usually written with a # symbol in front.
Find the most trending hashtags on Twitter
every second about 6000 tweets are created on Twitter,
that is roughly 500 million items daily
most of tweets are linked with one or more hashtags
https://www.internetlivestats.com/twitter-statistics/
15. Frequency:Traditional Approach
Build a table that lists of all seen thus far
elements with corresponding counters
Increment counters when a new element
comes or add that element into the table and
initialize its counter
Return the value of the counter that
corresponds to the element as frequency
requires linear memory
requires O(n) time lookup (worst case)
huge overhead for heavy hitters search
1 1 1
1 1 2
16. Frequency: Challenges for Big Data data streams
Continuous data streams
potentially unbounded number of unique elements
➡ sublinear (polylogarithmic at most) space
not feasible to re-process data streams
➡ one-pass algorithms preferred
high frequency throughput
➡ fast updates
Image: https://www.pngfind.com
17. Count-Min Sketch
a simple space-efficient probabilistic data structure that is used to estimate
frequencies of elements in data streams and can address the Heavy hitters problem
presented by Graham Cormode and Shan Muthukrishnan in 2003
21. Counting: Invoking Count-Min Sketch from Python
import json
from pdsa.frequency.count_min_sketch import CountMinSketch
cms = CountMinSketch(5, 2000)
with open('tweets.txt') as f:
for line in f:
ip = json.loads(line)['hashtag']
cms.add(ip)
print('Frequency of #Python', cms.frequency("Python"))
size_in_bytes = cms.sizeof()
print('Size in bytes', size_in_bytes) # ~40Kb / 32-bit counters
22. 4. Counting
Exceeding Classical: Probabilistic Data Structures in Data-Intensive Applications
EuroSciPy 2019Andrii Gakhov @gakhov
23. Counting: Challenge
Count the number of unique visitors
Amazon and eBay had about 3.375 billion* visitors in June 2019
Assume 337 million of unique IP addresses (128 bit per IPv6 record)
5.4 GB of memory just to store them all
*SimilarWeb.Com Data for June, 2019
What if we can count them with 12 KB only?
Image: https://www.cleanpng.com
24. Counting:Traditional Approach
Build list of all unique elements
Sort / search
to avoid listing elements twice
Count elements in the list
requires linear memory
requires O(n·log n) time
26. HyperLogLog
a hash-based probabilistic algorithm for counting the number of distinct
values in the presence of duplicates
proposed by Philippe Flajolet, Éric Fusy, Olivier Gandouet, and Frédéric Meunier in 2007
29. Counting: HyperLogLog Algorithm
Based on a single 32-bit hash function
Simulates k hash functions using stochastic averaging
approach
p bits (32 - p) bits
addressing bits rank computation bits
hash(x) =
32-bit hash value
Stores only k = 2p
counters (registers), about 4 bytes each
The memory always fixed, regardless the number of unique elements
More counters provide less error (memory/accuracy trade-off)
30. Counting: Invoking HyperLogLog from Python
import json
from pdsa.cardinality.hyperloglog import HyperLogLog
hll = HyperLogLog(precision=10) # 2^{10} = 1024 counters
with open('visitors.txt') as f:
for line in f:
ip = json.loads(line)['ip']
hll.add(ip)
num_of_unique_visitors = hll.count()
print('Unique visitors', num_of_unique_visitors)
size_in_bytes = hll.sizeof()
print('Size in bytes', size_in_bytes) # ~ 4Kb
31. Counting: Distinct Count in Redis
Redis uses the HyperLogLog data structure to count unique elements in a set
requires a small constant amount of memory of 12KB for every data
structure
approximates the exact cardinality with a standard error of 0.81%.
redis> PFADD hll python java ruby
(integer) 1
redis> PFADD hll python python python
(integer) 0
redis> PFADD hll java ruby
(integer) 0
redis> PFCOUNT hll
(integer) 3
http://antirez.com/news/75
32. 5. Final Notes
Exceeding Classical: Probabilistic Data Structures in Data-Intensive Applications
EuroSciPy 2019Andrii Gakhov @gakhov
33. Final Notes
Think about Big Data as a technology
challenge
Instead of buying new servers, learn new
algorithms
Believe in hashing! Sample vs Hashing.
Probabilistic Data Structures and Algorithms
become useful when your problem fits
Image: https://longfordpc.com/
34. Read More
[book] Probabilistic Data Structures and Algorithms for Big Data Applications
https://pdsa.gakhov.com
[repo] Probabilistic Data Structures and Algorithms in Python
https://github.com/gakhov/pdsa
Sketch of the Day: HyperLogLog — Cornerstone of a Big Data Infrastructure
https://research.neustar.biz/2012/10/25/sketch-of-the-day-hyperloglog-cornerstone-of-a-big-data-infrastructure/
Redis new data structure: the HyperLogLog
http://antirez.com/news/75
Approximate Algorithms in Apache Spark: HyperLogLog and Quantiles
https://databricks.com/blog/2016/05/19/approximate-algorithms-in-apache-spark-hyperloglog-and-quantiles.html
Big Data with Sketchy Structures
https://towardsdatascience.com/b73fb3a33e2a
Count-Min Sketch
http://dimacs.rutgers.edu/~graham/pubs/papers/cmencyc.pdf
35. Exceeding Classical: Probabilistic Data Structures in Data-Intensive Applications
EuroSciPy 2019Andrii Gakhov @gakhov
Website: www.gakhov.com
Twitter: @gakhov
Probabilistic Data Structures and
Algorithms for Big Data Applications
pdsa.gakhov.com
Eskerrik asko!
36. 6.Additional Slides
Exceeding Classical: Probabilistic Data Structures in Data-Intensive Applications
EuroSciPy 2019Andrii Gakhov, @gakhov
(for that person who wants more)
38. Counting:Accuracy vs MemoryTradeoff in HyperLogLog
!38
More counters require more memory (4 bytes per counter)
More counters need more bits for addressing them (m = 2p
)
39. Counting: HyperLogLog++Algorithm
HyperLogLog++
64-bit hash function, so allows to count more values
better bias correction using pre-trained data
proposed a sparse representation of the counters
(registers) to reduce memory requirements
HyperLogLog++ is an improved version of HyperLogLog
developed in Google and proposed in 2013