Stratio platform overview v4.1

•

1 like•1,459 views

Stratio is a Big Data platform based on Spark. It is 100% open source and enterprise - ready. In Stratio we are Pure Spark, since it is the only technology in the market able to combine stored data analyses with real time streaming data, all in the same query. We are unique in integrating Spark processing with the main NoSql databases: Cassandra, MongoDB, ElasticSearch, ...

Engineering

SELECT* FROMtweets WHERElucene=
'{
filter :
{
type : "range",
field : "time",
lower : "2014/04/25",
upper : "2014/04/1"
},
query :
{
type : "phrase",
field : "body",
values : ["big", "data"]
},
sort :
{
fields: [ {field:"retweets”, reverse:true} ]
}
}';

CASSANDRA
Kafka
STRATIO DEEP
STRATIO DEEP
•
•
•
•
•
•
•

readClob
readCSV
readLine
readMultiLine
readAvro
readJson
addCurrentTime
addLocalHost
geoIP
findReplace
Split
generateUUID
decompress
If
extractJsonPaths
detectMimeType
xquery
extractURIComponents
xslt
Grok(regular expressions)
exec
spooling
SNMP

Lucene Revolution 2016, Boston: Talk by Johannes Weigend (@JohannesWeigend, CTO at QAware). Abstract: Solr is a distributed NoSQL database with impressive search capabilities. Spark is the new megastar in the distributed computing universe. In this code-intense session we show you how to combine both to solve real-time search and processing problems. We will show you how to set up a Solr/Spark combination from scratch and develop first jobs with runs distributed on shared Solr data. We will also show you how to use this combination for your next-generation BI platform.

Building a Scalable Distributed Stats Infrastructure with Storm and KairosDB

Cody Ray

Building a Scalable Distributed Stats Infrastructure with Storm and KairosDB Many startups collect and display stats and other time-series data for their users. A supposedly-simple NoSQL option such as MongoDB is often chosen to get started... which soon becomes 50 distributed replica sets as volume increases. This talk describes how we designed a scalable distributed stats infrastructure from the ground up. KairosDB, a rewrite of OpenTSDB built on top of Cassandra, provides a solid foundation for storing time-series data. Unfortunately, though, it has some limitations: millisecond time granularity and lack of atomic upsert operations which make counting (critical to any stats infrastructure) a challenge. Additionally, running KairosDB atop Cassandra inside AWS brings its own set of challenges, such as managing Cassandra seeds and AWS security groups as you grow or shrink your Cassandra ring. In this deep-dive talk, we explore how we've used a mix of open-source and in-house tools to tackle these challenges and build a robust, scalable, distributed stats infrastructure.

Buzzwords 2014 / Overview / part1

Andrii Gakhov

Redis: REmote DIctionary ServerEzra Zygmuntowicz

Norikra: Stream Processing with SQL

SATOSHI TAGOMORI

Intro to py spark (and cassandra)

Jon Haddad

From the original abstract: If you're already using Cassandra you're already aware of it’s strengths of high availability and linear scalability. The downside to this power is less query flexibility. For an OLTP system with an SLA this is an acceptable tradeoff, but for a data scientist it’s extremely limiting. Enter Apache Spark. Apache spark complements an existing Cassandra cluster by providing a means of executing arbitrary queries, filters, sorting and aggregation. It’s possible to use functional constructs like map, filter, and reduce, as well as SQL and DataFrames. In this presentation I’ll show you how to process Cassandra data in bulk or through a Kafka stream using Python. Then we’ll visualize our data using iPython notebooks, leveraging Pandas and matplotlib. This is an advanced talk. We will assume existing knowledge of Cassandra and CQL.

RESTful API – How to Consume, Extract, Store and Visualize Data with InfluxDB...

InfluxData

Nowadays, every single modern application, system or solution does expose a RESTful API. On one hand, this is absolutely great and it has led to where we are today, having hundreds of other solutions or applications that can leverage these APIs, extend them, or even build on top of them. On the other hand, we have difficulty monitoring these new and modern systems, applications or solutions. In this session, we will learn how to query the data first using Swagger, when available, extract and parse the data that’s useful for us, store it in InfluxDB, and finally how to create beautiful and meaningful dashboards to have everything on a single pane of glass.

Stratio’s Cassandra Lucene Index, derived from Stratio Cassandra, is an open sourced plugin for Apache Cassandra that extends its index functionality to provide near real time search such as ElasticSearch or Solr, including full text search capabilities and free multivariable, geospatial and bitemporal search. It is achieved through an Apache Lucene based implementation of Cassandra secondary indexes, where each node of the cluster indexes its own data. Stratio’s Cassandra indexes are one of the core modules on which Stratio’s BigData platform is based. Andres de la Peña discusses the recently added geospatial search features in Stratio's Cassandra Lucene index using some Nephila Capital use cases. These new features include indexing complex polygons, nearest neighbour search, and the application of chained geometrical transformations such as bounding box, convex hull, centroid, union, intersection, exclusion and distance buffer.

Spark Structured Streaming

Revin Chalil

Spark Streaming with Cassandra

Jacek Lewandowski

Introduction to Apache Drill - interactive query and analysis at scaleMapR Technologies

Stream Processing made simple with Kafka

DataWorks Summit/Hadoop Summit

Real-time Meetup Stream Analysis

Deep Mehta

Realtime Statistics based on Apache Storm and RocketMQ

Xin Wang

MongoSF - mongodb @ foursquare

jorgeortiz85

Spark Streaming, Machine Learning and meetup.com streaming API.

Sergey Zelvenskiy

Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...

Alexey Kharlamov

At Integral, we process heavy volumes of click-stream traffic. 50K QPS of ad impressions at peak and close to 200K QPS of all browser calls. We build analytics on this streams of data. There are two applications which require quite significant computational effort: 'sessionization' and fraud detection. Sessionization implies linking a series of requests from same browser into single record. There can be 5 or more total requests spread over 15-30 minutes which we need to link to each other. Fraud detection is a process looking at various signals in browser requests and at substantial historical evidence data classifying ad impression either as legitimate or as fraudulent. We've been doing both (as well as all other analytics) in batch mode once an hour at best. Both processes, and, in particular, fraud detection, are time sensitive and much more meaningful if done in near-real-time. This talk would be about our experience migrating a once-per-day offline batch processing of impression data using hadoop to in-memory stream processing using Kafka, Storm and Cassandra. We will touch upon our choices and our reasoning for selecting the products used for this solution. Hadoop is no longer the only or always preferred option in Big Data space. In-memory stream processing may be more effective for time series data preparation and aggregation. Ability to scale at a significantly lower cost means more customers, better accuracy and better business practices: since only in-stream processing allows for low-latency data and insight delivery it opens entirely new opportunities. However, transitioning of non-trivial data pipelines raises a number of questions hidden previously within the offline nature of batch processing. How will you join several data feeds? How will you implement failure recovery? In addition to handling terabytes of data per day our streaming system has to be guided by the following considerations: • Recovery time • Time relativity and continuity • Geographical distribution of data sources • Limit on data loss • Maintainability The system produces complex cross-correlational analysis of several data feeds and aggregation for client analytics with input feed frequency of up to 100K msg/sec. This presentation will benefit anyone interested in learning an alternate approach for big data analytics, especially the process of joining multiple streams in memory using Cassandra. Presentation will also highlight certain optimization patterns used those can be useful in similar situations.

Hadoop Integration in Cassandra

Jairam Chandar

Making your elastic cluster perform - Jettro Coenradie - Codemotion Amsterdam...

Codemotion

In the past few years I have helped a lot of customers optimising their elastic cluster. With each version elasticsearch has more options to track performance of your nodes and recently profiling your queries was added. In this talk I am going to discuss the steps you have to take when starting with elasticsearch. The choices you have to make for the size of your cluster, the amount of indexes, amount of shards, choosing the right mappings, and creating better queries. After the setup I'll continue showing how to monitor your cluster and profile your queries.

Closing the Loop in Extended Reality with Kafka Streams and Machine Learning ...

confluent

We’ve built a real-time streaming platform that enables prediction based on user behavior, with events occurring in virtual and augmented reality environments. The solution enables organizations to train people in an extended reality environment, where real-life training may be costly and dangerous. Kafka Streams enables analyzing spatial and event data to detect gestural feature and analyze user behavior in real-time to be able to predict any future mistake the user might make. Kafka is the backbone of our real-time analytics and extended reality communication platform with our cluster and applications being deployed on Kubernetes. In this talk, we will mainly focus on the following: 1. Why Extended Reality with Kafka is a step in the right direction. 2. Architecture & Power of Schema Registry in building a generic platform for pluggable XR apps and analytics models 3. How KSQL and Kafka Streams fits in Kafka Ecosystem to help analyze human motion data and detect features for real-time prediction. 4. Demo of a VR application with real-time analytics feedback, which assists people to be trained in how to work with chemical laboratory equipment.

Paul Dix (Founder InfluxDB) - Organising Metrics at #DOXLON

Outlyer

Distributed Logistic Model Trees

Stratio

Classification algorithms play an important role in different business areas, such as fraud detection, cross selling or customer behavior. In the business context, interpretability is a very desirable property, sometimes even a hard requirement. However, interpretable algorithms are usually outperformed by other non-interpretable algorithms such as Random Forest. In this talk Antonio Soriano and Mateo Alvarez presented a distributed implementation in Spark of the Logistic Model Tree (LMT) algorithm (Landwehr, et al. (2005). Machine Learning, 59(1-2), 161-205.), which consists of a decision tree with logistic classifiers in the leaves. While being highly interpretable, the LMT consistently performs equally or better than other popular algorithms in several performance metrics such as accuracy, precision/recall or area under the ROC curve.

[Strata] Sparkta

Stratio

What's hot

Norikra: SQL Stream Processing In Ruby

SATOSHI TAGOMORI

Heuritech: Apache Spark REX

didmarin

Time Series Processing with Apache Spark

Josef Adersberger

Data analysis scala_spark

Yiguang Hu

Real-Time Streaming with Apache Spark Streaming and Apache Storm

Davorin Vukelic

Lightning fast analytics with Spark and Cassandra

nickmbailey

Stratio's Cassandra Lucene index: Geospatial use cases - Big Data Spain 2016

Stratio

Spark Structured Streaming

Revin Chalil

Spark Streaming with Cassandra

Jacek Lewandowski

Introduction to Apache Drill - interactive query and analysis at scaleMapR Technologies

Stream Processing made simple with Kafka

DataWorks Summit/Hadoop Summit

Real-time Meetup Stream Analysis

Deep Mehta

Realtime Statistics based on Apache Storm and RocketMQ

Xin Wang

MongoSF - mongodb @ foursquare

jorgeortiz85

Spark Streaming, Machine Learning and meetup.com streaming API.

Sergey Zelvenskiy

Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...

Alexey Kharlamov

Hadoop Integration in Cassandra

Jairam Chandar

Making your elastic cluster perform - Jettro Coenradie - Codemotion Amsterdam...

Codemotion

Closing the Loop in Extended Reality with Kafka Streams and Machine Learning ...

confluent

Paul Dix (Founder InfluxDB) - Organising Metrics at #DOXLON

Outlyer

What's hot (20)

Norikra: SQL Stream Processing In Ruby

Heuritech: Apache Spark REX

Time Series Processing with Apache Spark

Data analysis scala_spark

Real-Time Streaming with Apache Spark Streaming and Apache Storm

Lightning fast analytics with Spark and Cassandra

Stratio's Cassandra Lucene index: Geospatial use cases - Big Data Spain 2016

Spark Structured Streaming

Spark Streaming with Cassandra

Introduction to Apache Drill - interactive query and analysis at scale

Stream Processing made simple with Kafka

Real-time Meetup Stream Analysis

Realtime Statistics based on Apache Storm and RocketMQ

MongoSF - mongodb @ foursquare

Spark Streaming, Machine Learning and meetup.com streaming API.

Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...

Hadoop Integration in Cassandra

Making your elastic cluster perform - Jettro Coenradie - Codemotion Amsterdam...

Closing the Loop in Extended Reality with Kafka Streams and Machine Learning ...

Paul Dix (Founder InfluxDB) - Organising Metrics at #DOXLON

Viewers also liked

Distributed Logistic Model Trees

Stratio

[Strata] Sparkta

Stratio

Stratio CrossData: an efficient distributed datahub with batch and streaming ...

Stratio

A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)

Spark Summit

Multiplaform Solution for Graph Datasources

Stratio

One of the top banks in Europe, needed a system to provide better performance, scaling almost linearly with the increase in information to be analyzed, and allowing to move the processes that were currently being executed in the Host to a Big Data infrastructure. During a year we've worked on a system which is able to provide greater agility, flexibility and simplicity for the user to view information when profiling and is now able to analyze the structure of profile data. It's a powerful way to make online queries to a graph database, which is integrated with Apache Spark and different graph libraries. Basically, we get all the necessary information through Cypher queries which are sent to a Neo4j database. Using the last Big Data technologies like Spark Dataframe, HDFS, Stratio Intelligence or Stratio Crossdata, we have developed a solution which is able to obtain critical information for multiple datasources like text files o graph databases.

Lunch&Learn: Combinación de modelos

Stratio

Hoy en día la combinación de modelos de Machine Learning es muy popular. En la mayoría de los concursos de kaggle, los primeros clasificados suelen emplear alguna variante de estas técnicas. Hablamos de qué nos lleva a emplearlas, cuándo y cómo. Nos centramos en las técnicas de resampleado, como bagging y boosting, trabajamos el implementado de un AdaBoost sencillo sobre árboles binarios, y finalmente, vemos una aplicación de Random Forest para selección de variables. More information: www.stratio.com https://www.youtube.com/StratioBD

Stratio big data spainÁlvaro Agea Herradón

Meetup: Spark + Kerberos

Stratio

Los proyectos de Big Data han pasado de sus fases de POC, donde la seguridad ha sido, en el mejor de los casos, un aspecto secundario. Por ello las herramientas Big Data y más concretamente las usadas para procesar datos, deben ponerse al día en seguridad. Las herramientas como Spark, no están pensados para la seguridad. Por eso Abel y Jorge quieren compartir los hacks que han sido necesarios hacer a Spark para poder usar Kerberos para autenticarse contra servicios securizados.

Strtio Spark Streaming + Siddhi CEP Engine

Myung Ho Yun

Spark Streaming @ Berlin Apache Spark Meetup, March 2015

Stratio

Functional programming in scalaStratio

Google Cloud Dataflow and lightweight Lambda Architecture for Big Data AppTrieu Nguyen

Primeros pasos con Spark - Spark Meetup Madrid 30-09-2014

Stratio

Introduction to Asynchronous scalaStratio

Google Cloud Dataflow Two Worlds Become a Much Better One

DataWorks Summit

Apache Spark & Cassandra use case at Telefónica Cbs by Antonio Alcacer

Stratio

UNION BANCARIA EN LA UNION EUROPEA

Ramiro Ojeda

El modelo europeo de reporting y el lenguaje XBRL - Ignacio Boixo

Asociación XBRL España

Recuperación y Unión Bancaria Europea. Emilio Ontiveros

Universidad de Deusto - Deustuko Unibertsitatea - University of Deusto

La Unión Bancaria Europea

koball

Viewers also liked (20)

Distributed Logistic Model Trees

[Strata] Sparkta

Stratio CrossData: an efficient distributed datahub with batch and streaming ...

A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)

Multiplaform Solution for Graph Datasources

Lunch&Learn: Combinación de modelos

Stratio big data spain

Meetup: Spark + Kerberos

Strtio Spark Streaming + Siddhi CEP Engine

Spark Streaming @ Berlin Apache Spark Meetup, March 2015

Functional programming in scala

Google Cloud Dataflow and lightweight Lambda Architecture for Big Data App

Primeros pasos con Spark - Spark Meetup Madrid 30-09-2014

Introduction to Asynchronous scala

Google Cloud Dataflow Two Worlds Become a Much Better One

Apache Spark & Cassandra use case at Telefónica Cbs by Antonio Alcacer

UNION BANCARIA EN LA UNION EUROPEA

El modelo europeo de reporting y el lenguaje XBRL - Ignacio Boixo

Recuperación y Unión Bancaria Europea. Emilio Ontiveros

La Unión Bancaria Europea

More from Stratio

Mesos Meetup - Building an enterprise-ready analytics and operational ecosyst...

Stratio

On November 6th, we got together at Google Campus to talk about Mesos and DC/OS. Ignacio Mulas, Sparta & Spark Product Owner at Stratio, explained how to build an environment that can secure and govern its data for operational and analytical applications on top of DC/OS platform. He showed that analytical and machine learning pipelines can be combined with operational processes maintaining the security and providing governing tools to manage our data. He focused on the architecture and tools needed to achieve an ecosystem like this and we will show a demo of it. He also explained how we can develop our pipelines interactively with auto-discovered data catalogs and explore our results. Find out more: https://www.stratio.com/events/discover-how-to-deploy-a-secure-big-data-pipeline-with-dcos/

Can an intelligent system exist without awareness? BDS18

Stratio

Marco Baena, Head of AI at Stratio, presented at Big Data Spain 2018 "Can an intelligent system exist without awareness?" Description: Is a system without awareness a truly intelligent system? Here he showed why it is a mandatory approach to pursue awareness and how an Awareness-Centric model is the key of any competitive modern business. In addition to this, he also showed the technical benefits of such systems and how Stratio proposal is a reference when following this model.

Kafka and KSQL - Apache Kafka Meetup

Stratio

Wild Data - The Data Science Meetup

Stratio

On July 19th, we got together at Google Campus to talk about how to increase and complete existing data to improve Machine Learning Models. Fernando Velasco, Data Scientist at Stratio, and Raúl de la Fuente, Presales at Stratio, talked about techniques of image processing like Data Augmentation and other more modern techniques that involve the use of Deep Learning models. More info: http://www.stratio.com/blog/events/planet-data-scientist-live-meet-the-wild-data/

Using Kafka on Event-driven Microservices Architectures - Apache Kafka Meetup

Stratio

Ensemble methods in Machine Learning

Stratio

Within the use of Machine Learning models for prediction, one of the sets of techniques that stands out is the model combination. We will study these combinations with Fernando Velasco, Data Scientist at Stratio, who will explain what they are, why and when to use them. Two of the main general techniques will be explained: boosting and bagging and, finally, how to make feature selection through ensembles.

Stratio Sparta 2.0

Stratio

Looking to build Kappa architectures or SMACK applications or to use distributed technologies such as Spark, Kafka, Elasticsearch and Hadoop? Do you want to build your own data-centric platform? Are you lacking a development team with Scala and Spark skills? Are you managing to move towards full Digital Transformation? Have our questions stressed you out enough? Then you are ready for our latest release! Our next product release will include Sparta 2.0, ready to solve the above-mentioned issues. Stratio clients will be able to build Big Data processes and data pipeline workflows in minutes with an amazing UI, integrated with Spark, Kafka and several Big Data technologies. In this session, we will show how Sparta 2.0 and its workflow jobs are a key piece in a data-centric platform and how it is integrated with a PaaS (DC/OS). We will also show how Stratio has made sure all pieces within the platform, including Sparta 2.0 are secured. By: José Carlos García and Javier Yuste

Big Data Security: Facing the challenge

Stratio

A data-centric platform integrates multiple Big Data open source technologies. For example, at Stratio we use Spark, Kafka, Elastic search and many more. Most of these technologies do not offer native security. This lack of security, not only leaves companies open to critical risks like data leakage, unsecure communications or DoS attacks but is also a major barrier to complying with different regulations such as LOPD, PCI-DSS or the upcoming GDPR. This talk gives a technical and innovative overview of how companies can face the challenge of protecting the data and services that are in their data-centric platform, focusing on three main aspects: implementing network segmentation, managing AAA and securing data processing. By: Carlos Gómez

Operationalizing Big Data

Stratio

Our data lake is full of data, our Business intelligence is squeezing every byte of information and our operational applications are just great… why do I still feel I can do better? Having big data gives you a competitive advantage, but using big data in your daily operations will give you much more. Taking the best of both worlds, we aim for systems in which big data analysis is performed on operational data in real-time and our applications embed the extracted intelligence in their every-day operations. The good news is that combining both is perfectly possible using a data-centric approach together with well-known industry patterns and a few good practices. By: Nacho Mulas

Artificial Intelligence on Data Centric Platform

Stratio

Digital Transformation starts with data. What if a solution existed that put data at the center, in a single place, serving all applications around it? This training will include a demonstration in a distributed data-centric platform which provides a data intelligence layer, composed of artificial intelligence models able to make use of a whole company’s data. Nowadays, one of the most innovative techniques in the realm of artificial intelligence is Deep Neural Nets. Among the many applications, language modelling, machine translation and image generation are receiving particular attention. Deep nets are also powerful in predictive modelling ambits such as stock pricing and the energy industry. We will address a few case studies modeled with TensorFlow, running on Stratio’s data-centric product in a distributed cluster. By: Fernando Velasco

Introduction to Artificial Neural Networks

Stratio

“A Distributed Operational and Informational Technological Stack”

Stratio

By Loreto Fernández Costas and Adrián Doncel Gabaldó. Digital Transformation starts with data. What if a solution existed that put data at the center, in a single place, serving all applications around it – A distributed data centric solution that combined the operational and the informational, managed by a single data center operating system? This session will provide a detailed explanation of such a solution, bringing the concept of data centricity to life. We will cover the details of the array of open source technologies that come together to create a transformational solution to the historic problem of physical companies: From multiple data stores, distributed run-time engines and SQL engines based on Spark, to microservices, Machine Learning and Deep Learning Algorithms. Big Data 3.0 is just round the corner.

Meetup: Cómo monitorizar y optimizar procesos de Spark usando la Spark Web - ...

Stratio

Apache Spark ya es una realidad en el mundo de la informática y ahora necesitamos no sólo saber de lo que la tecnología es capaz, necesitamos hacerlo productivo. Para ello necesitamos saber cómo poder auditar cada uno de sus procesos de una manera sencilla y sin necesidad de conocimientos destacados de esta tecnología. Jorge López-Malla muestra qué herramientas proporciona el propio framework de Apache Spark para poder monitorizar el rendimiento de los algoritmos y cómo sacarle partido para mejorar los jobs de Apache Spark, tanto streaming como batch, y ver qué magia hace SparkSQL cuando se quiere hacer un simple join.

Advanced search and Top-K queries in Cassandra

Stratio

Why spark by Stratio - v.1.0

Stratio

On-the-fly ETL con EFK: ElasticSearch, Flume, Kibana

Stratio

Spark Summit - Stratio Streaming

Stratio

More from Stratio (17)

Mesos Meetup - Building an enterprise-ready analytics and operational ecosyst...

Can an intelligent system exist without awareness? BDS18

Kafka and KSQL - Apache Kafka Meetup

Wild Data - The Data Science Meetup

Using Kafka on Event-driven Microservices Architectures - Apache Kafka Meetup

Ensemble methods in Machine Learning

Stratio Sparta 2.0

Big Data Security: Facing the challenge

Operationalizing Big Data

Artificial Intelligence on Data Centric Platform

Introduction to Artificial Neural Networks

“A Distributed Operational and Informational Technological Stack”

Meetup: Cómo monitorizar y optimizar procesos de Spark usando la Spark Web - ...

Advanced search and Top-K queries in Cassandra

Why spark by Stratio - v.1.0

On-the-fly ETL con EFK: ElasticSearch, Flume, Kibana

Spark Summit - Stratio Streaming

Recently uploaded

MCQ Soil mechanics questions (Soil shear strength).pdf

Osamah Alsalih

Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B

Sreedhar Chowdam

AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf

SamSarthak3

H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf

MLILAB

The Benefits and Techniques of Trenchless Pipe Repair.pdf

Pipe Restoration Solutions

Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry. Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems. Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.

Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)

MdTanvirMahtab2

block diagram and signal flow graph representation

Divya Somashekar

Student information management system project report ii.pdf

Kamal Acharya

Final project report on grocery store management system..pdf

Kamal Acharya

In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services. Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options. In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website. This document will discuss each of the underlying technologies to create and implement an e- commerce website.

Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdf

fxintegritypublishin

Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.

Architectural Portfolio Sean Lockwood

seandesed

CME397 Surface Engineering- Professional Elective

karthi keyan

Cosmetic shop management system project report.pdf

Kamal Acharya

Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry. Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks. Data file handling has been effectively used in the program. The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.

The role of big data in decision making.

ankuprajapati0525

Democratizing Fuzzing at Scale by Abhishek Arya

abh.arya

Presented at NUS: Fuzzing and Software Security Summer School 2024 This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.

weather web application report.pdf

Pratik Pawar

Automobile Management System Project Report.pdf

Kamal Acharya

The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message. When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely. When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item. Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.

COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdf

Kamal Acharya

The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.

Immunizing Image Classifiers Against Localized Adversary Attacks

gerogepatton

This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.

Quality defects in TMT Bars, Possible causes and Potential Solutions.

PrashantGoswami42

Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.

Recently uploaded (20)

MCQ Soil mechanics questions (Soil shear strength).pdf

Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B

AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf

H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf

The Benefits and Techniques of Trenchless Pipe Repair.pdf

Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)

block diagram and signal flow graph representation

Student information management system project report ii.pdf

Final project report on grocery store management system..pdf

Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdf

Architectural Portfolio Sean Lockwood

CME397 Surface Engineering- Professional Elective

Cosmetic shop management system project report.pdf

The role of big data in decision making.

Democratizing Fuzzing at Scale by Abhishek Arya

weather web application report.pdf

Automobile Management System Project Report.pdf

COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdf

Immunizing Image Classifiers Against Localized Adversary Attacks

Quality defects in TMT Bars, Possible causes and Potential Solutions.