The slides will walk thru the requirements and will enable to pick appropriate No SQL database as a best match for the requirements i.e. fitness for the purpose
My presentation on RDFauthor at EKAW2010, Lisbon. For more information on RDFauthor visit http://aksw.org/Projects/RDFauthor; for the code visit http://code.google.com/p/rdfauthor/.
The slides will walk thru the requirements and will enable to pick appropriate No SQL database as a best match for the requirements i.e. fitness for the purpose
My presentation on RDFauthor at EKAW2010, Lisbon. For more information on RDFauthor visit http://aksw.org/Projects/RDFauthor; for the code visit http://code.google.com/p/rdfauthor/.
Ever used a graph database to store your data?
Ever wondered if it is possible to administrate the data without the need to write update queries, but to have a nice visual interface that renders your graph and offers you interaction?
In this talk i present a graph viewer interface built on top of ArangoDB and the challenges i had to solve during its creation.
Seminar presentation for which the entire work was conducted at Technical University Kaiserslautern. The seminar work involved understanding the Semantic Web technology along with RDF and querying mechanism. It also involved looking at technologies that are used for data storage, data management and data querying.
There is a lot of confusion out there about the various kinds of NoSQL, and NewSQL, technologies. Document stores, graph databases, columnar databases, graph databases, and the list goes on. This confusion has lead to a good deal of less than optimal deployments, pain, and, ultimately, antipathy.
In this talk, Dan will walk us through a high-level explanation of the various NoSQL technologies available to us, how they work, and provide some dos and don'ts for their implementation.
Basic performance application optimization techniques that can be applied to any application, from web to desktop or mobile, but with focus on php/mysql stack. How to identify bottlenecks and resolve them and what strategies to choose to avoid them upfront.
Live presentation:
https://www.youtube.com/watch?v=aas8oM7CLjk
We will take a deep dive into ArangoDB (https://www.arangodb.com/) together with Max (https://www.linkedin.com/in/maxneunhoeffer) one of the core developers of the product.
ArangoDB is a multi-model database, which means that it is a document store, a key/value store and a graph database, all in one engine and with a query language that supports all three data models, as well as joins and transactions. Queries can use a single data model or can even mix them.
ArangoDB scales out horizontally with convenient cluster deployment using Apache Mesos. Furthermore, the HTTP API can easily be extended by server-side JavaScript code using high performance access to the C++ database core.
During the talk I will show all these features using several different cloud deployments, since in most projects one will not deploy a ArangoDB monolith, but rather multiple instances, each either a possibly replicated single server, or a cluster. This demonstrates that all these properties together make ArangoDB a very useful and valuable tool in modern microservice oriented architectures.
Supercharge your RDBMS with ElasticsearchArthur Gimpel
Leverage modern data architecture in the big data era. Lecture by Arthur Gimpel @ Elasticsearch{Zone} as part of Big Data Month 2016 by DataZone and Elastic
Ever used a graph database to store your data?
Ever wondered if it is possible to administrate the data without the need to write update queries, but to have a nice visual interface that renders your graph and offers you interaction?
In this talk i present a graph viewer interface built on top of ArangoDB and the challenges i had to solve during its creation.
Seminar presentation for which the entire work was conducted at Technical University Kaiserslautern. The seminar work involved understanding the Semantic Web technology along with RDF and querying mechanism. It also involved looking at technologies that are used for data storage, data management and data querying.
There is a lot of confusion out there about the various kinds of NoSQL, and NewSQL, technologies. Document stores, graph databases, columnar databases, graph databases, and the list goes on. This confusion has lead to a good deal of less than optimal deployments, pain, and, ultimately, antipathy.
In this talk, Dan will walk us through a high-level explanation of the various NoSQL technologies available to us, how they work, and provide some dos and don'ts for their implementation.
Basic performance application optimization techniques that can be applied to any application, from web to desktop or mobile, but with focus on php/mysql stack. How to identify bottlenecks and resolve them and what strategies to choose to avoid them upfront.
Live presentation:
https://www.youtube.com/watch?v=aas8oM7CLjk
We will take a deep dive into ArangoDB (https://www.arangodb.com/) together with Max (https://www.linkedin.com/in/maxneunhoeffer) one of the core developers of the product.
ArangoDB is a multi-model database, which means that it is a document store, a key/value store and a graph database, all in one engine and with a query language that supports all three data models, as well as joins and transactions. Queries can use a single data model or can even mix them.
ArangoDB scales out horizontally with convenient cluster deployment using Apache Mesos. Furthermore, the HTTP API can easily be extended by server-side JavaScript code using high performance access to the C++ database core.
During the talk I will show all these features using several different cloud deployments, since in most projects one will not deploy a ArangoDB monolith, but rather multiple instances, each either a possibly replicated single server, or a cluster. This demonstrates that all these properties together make ArangoDB a very useful and valuable tool in modern microservice oriented architectures.
Supercharge your RDBMS with ElasticsearchArthur Gimpel
Leverage modern data architecture in the big data era. Lecture by Arthur Gimpel @ Elasticsearch{Zone} as part of Big Data Month 2016 by DataZone and Elastic
Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database?
Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model.
In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.
Joseph Bradley, Software Engineer, Databricks Inc. at MLconf SEA - 5/01/15MLconf
Spark DataFrames and ML Pipelines: In this talk, we will discuss two recent efforts in Spark to scale up data science: distributed DataFrames and Machine Learning Pipelines. These components allow users to manipulate distributed datasets and handle complex ML workflows, using intuitive APIs in Python, Java, and Scala (and R in development).
Data frames in R and Python have become standards for data science, yet they do not work well with Big Data. Inspired by R and Pandas, Spark DataFrames provide concise, powerful interfaces for structured data manipulation. DataFrames support rich data types, a variety of data sources and storage systems, and state-of-the-art optimization via the Spark SQL Catalyst optimizer.
On top of DataFrames, we have built a new ML Pipeline API. ML workflows often involve a complex sequence of processing and learning stages, including data cleaning, feature extraction and transformation, training, and hyperparameter tuning. With most current tools for ML, it is difficult to set up practical pipelines. Inspired by scikit-learn, we built simple APIs to help users quickly assemble and tune practical ML pipelines.
Building Data Pipelines with Spark and StreamSetsPat Patterson
Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Metadata in upstream sources can ‘drift’ due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an Apache 2.0 licensed open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we’ll look at how SDC’s “intent-driven” approach keeps the data flowing, with a particular focus on clustered deployment with Spark and other exciting Spark integrations in the works.
How to Migrate from Cassandra to Amazon DynamoDB - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to migrate from Cassandra to DynamoDB
- Learn about the considerations and pre-requisites for migrating to DynamoDB
- Learn the benefits of a fully managed nosql database - DynamoDB
Using SparkML to Power a DSaaS (Data Science as a Service) with Kiran Muglurm...Databricks
Almost all organizations now have a need for data science and, as such, the main challenge after determining the algorithm is to scale it up and make it operational. Comcast uses several tools and technologies such as Python, R, SaS, H2O and so on. In this session, they’ll show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees, Clustering, NLP, etc.
Apache Spark has several machine learning algorithms built in and has excellent scalability. Hence, at Comcast, they built a platform to provide DSaaS on top of Spark with REST API as a means of controlling and submitting jobs, so as to abstract most users from the rigor of writing (repeating) code, instead focusing on the actual requirements.
Learn how they solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. They’ll also showcase their use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500-node Spark clusters.
Spark auf Hadoop ist hochskalierbar. Cloud Computing ist hochskalierbar. R, die erweiterbare Open Source Data Science Software, eher nicht. Aber was passiert, wenn wir Spark auf Hadoop, Cloud Computing und den Microsoft R Server zu einer skalierbaren Data Science-Plattform zusammenfügen? Stellen Sie sich vor wie es sein könnte, wenn Sie das Erkunden, Transformieren und Modellieren von Daten in jeder beliebigen Größe aus Ihrer Lieblings-R-Umgebung durchführen könnten. Stellen Sie sich nun vor, wie man anschließend die erzeugten Modelle - mit wenigen Klicks - als skalierbare, cloud basierte Web-Services-API bereitstellt. In dieser Session zeigt Sascha Dittmann, wie Sie Ihren R-Code, tausende von Open-Source-R-Pakete sowie die verteilte Implementierungen der beliebtesten Maschine-Learning-Algorithmen nutzen können, um genau dies umzusetzen. Dabei zeigt er wie man ein HDInsight Spark-Cluster inkl. eines Microsoft R Server-Clusters erstellt, sowie das daraus entstandene Model im SQL Server oder als swagger-based API für Anwendungsentwickler bereitstellt.
NoSQL is not a buzzword anymore. The array of non- relational technologies have found wide-scale adoption even in non-Internet scale focus areas. With the advent of the Cloud...the churn has increased even more yet there is no crystal clear guidance on adoption techniques and architectural choices surrounding the plethora of options available. This session initiates you into the whys & wherefores, architectural patterns, caveats and techniques that will augment your decision making process & boost your perception of architecting scalable, fault-tolerant & distributed solutions.
Operationalizing security data science for the cloud: Challenges, solutions, ...Ram Shankar Siva Kumar
In most security data science talks that describe a specific algorithm used to solve a security problem, the audience is left wondering: how did they perform system testing when there is no labeled attack data; what metrics do they monitor; and what do these systems actually look like in production? Academia and industry both focus largely on security detection, but the emphasis is almost always on the algorithmic machinery powering the systems. Prior art productizing solutions is sparse: it has been studied from a machine-learning angle or from a security angle but has not been jointly explored. But the intersection of operationalizing security and machine-learning solutions is important not only because security data science solutions inherit complexities from both fields but also because each has unique challenges—for instance, compliance restrictions that dictate data cannot be exported from specific geographic locations (a security constraint) have a downstream effect on model design, deployment, evaluation, and management strategies (a data science constraint). This talk explores this intersection!
Building a SIMD Supported Vectorized Native Engine for Spark SQLDatabricks
Spark SQL works very well with structured row-based data. Vectorized reader and writer for parquet/orc can make I/O much faster. It also used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions under complicated queries. Apache Arrow provides columnar in-memory layout and SIMD optimized kernels as well as a LLVM based SQL engine Gandiva. These native based libraries can accelerate Spark SQL by reduce the CPU usage for both I/O and execution.
Migration from Oracle to PostgreSQL: NEED vs REALITYAshnikbiz
Some of the largest organization in the world today are going cost-efficient by innovating their database layer. Migrating workloads from legacy systems to an enterprise open source database technology like Postgres is a preferred choice for many.
FireEye & Scylla: Intel Threat Analysis Using a Graph DatabaseScyllaDB
FireEye believes in intelligence driven cyber security. Their legacy system used PostgreSQL with a custom graph database system to store and facilitate analysis of threat intelligence data. As their user base increased they ran into scaling issues requiring a system redesign with a new platform.
This presentation will focus on the bac kend systems and migration path to a new technology stack using JanusGraph running on top of Scylla plus Elasticsearch.
Using Scylladb turned out to be a game-changer in terms of performance and the types of analysis our application is able to do effortlessly.
Similar to Text classification With Rapid Miner (20)
Web scraping is the process of extracting data from websites. It is typically done using a computer program that automates the process of visiting websites, parsing the HTML code, and extracting the desired data. Web scraping can be used for a variety of purposes, such as collecting product prices, tracking competitor activity, or creating market research reports.
There are a variety of tools and techniques that can be used for web scraping. Some popular tools include Python libraries such as BeautifulSoup and Scrapy, as well as online services such as ScraperAPI and Octoparse. The specific tool or technique that is best for a particular task will depend on the complexity of the data that needs to be extracted and the frequency with which it needs to be updated.
It is important to use web scraping responsibly and ethically. This means following the terms of service of the websites that are being scraped, not overloading the websites with requests, and giving credit to the original source of the data. Web scraping can be a powerful tool, but it is important to use it in a way that does not harm the websites that are being scraped.
ChatGPT is a powerful language model developed by OpenAI. It is designed to generate human-like text based on given prompts. As a prompt engineer, you can utilize ChatGPT to create engaging conversations, provide information, answer questions, and assist users. It's a versatile tool for natural language processing tasks, enabling more interactive and intelligent interactions.
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
Overall AI/ML Solution and technique at Softnix Technology that include about Anomaly Detection on time series we use dbscan , LSTM technique and we use autoencoder to detect log may have risk to anomaly and another optimize solutions
Part I Machine learning technique
Introduction to Machine Learning
Genetic Algorithm
Monte Calo
Reinforcement Learning
Generative Adversarial Networks
Part II Anomaly Detection technique
Type of Anomaly
RNN
Historical
DB-SCAN
Time Shift Detection
Text Pattern Anomaly Detection
Part 1
- Introduction
- Application for Anomaly Detection
- AIOps
- GraphDB
Part 2
- Type Of Anomaly Detection
- How to Identify Outliers in your Data
Part 3
- Anomaly Detection for Timeseries Technique
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.