This document discusses using the Object Constraint Language (OCL) as a pivot language for polyglot graph databases. OCL could serve as a common query language that is translatable to different native graph languages. This would eliminate the need for code generation between languages and allow a graph database to support multiple query languages. The document also proposes extensions to OCL, such as adding syntax for graph patterns, to make it more suitable as a graph query language.
Given at Data Day Texas 2016.
Apache Spark has been hailed as a trail-blazing new tool for doing distributed data science. However, since it's so new, it can be difficult to set up and hard to use. In this talk, I'll discuss the journey I've had using Spark for data science at Bitly over the past year. I'll talk about the benefits of using Spark, the challenges I've had to overcome, the caveats for using a cutting-edge technology such as this, and my hopes for the Spark project as a whole.
Data Science at Scale: Using Apache Spark for Data Science at BitlySarah Guido
Given at Data Day Seattle 2015.
Bitly generates over 9 billion clicks on shortened links a month, as well as over 100 million unique link shortens. Analyzing data of this scale is not without its challenges. At Bitly, we have started adopting Apache Spark as a way to process our data. In this talk, I’ll elaborate on how I use Spark as part of my data science workflow. I’ll cover how Spark fits into our existing architecture, the kind of problems I’m solving with Spark, and the benefits and challenges of using Spark for large-scale data science.
Implementation details of Sparksee's graph database, learn how bitmaps store graph information and how this result in a lightweight & high-performance solution.
Given at Data Day Texas 2016.
Apache Spark has been hailed as a trail-blazing new tool for doing distributed data science. However, since it's so new, it can be difficult to set up and hard to use. In this talk, I'll discuss the journey I've had using Spark for data science at Bitly over the past year. I'll talk about the benefits of using Spark, the challenges I've had to overcome, the caveats for using a cutting-edge technology such as this, and my hopes for the Spark project as a whole.
Data Science at Scale: Using Apache Spark for Data Science at BitlySarah Guido
Given at Data Day Seattle 2015.
Bitly generates over 9 billion clicks on shortened links a month, as well as over 100 million unique link shortens. Analyzing data of this scale is not without its challenges. At Bitly, we have started adopting Apache Spark as a way to process our data. In this talk, I’ll elaborate on how I use Spark as part of my data science workflow. I’ll cover how Spark fits into our existing architecture, the kind of problems I’m solving with Spark, and the benefits and challenges of using Spark for large-scale data science.
Implementation details of Sparksee's graph database, learn how bitmaps store graph information and how this result in a lightweight & high-performance solution.
Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, ...Chris Fregly
This talk highlights the Data Sources API which participates in the Spark SQL DataFrame Catalyst Optimizer. We dive deep into the super-advanced Cassandra's open source implementation @ github.com/datastax/spark-cassandra-connector. We discuss data locality, cluster deployment - as well as the pros and cons of mixing OLAP and OLTP workloads.
We also implement a SimpleDataSource which is a basic implementation of the DataSources API.
All analysis is done with Apache Zeppelin.
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MorePaco Nathan
Spark and Databricks component of the O'Reilly Media webcast "2015 Data Preview: Spark, Data Visualization, YARN, and More", as a preview of the 2015 Strata + Hadoop World conference in San Jose http://www.oreilly.com/pub/e/3289
Cassandra Day SV 2014: Spark, Shark, and Apache CassandraDataStax Academy
This session covers our experience with using the Spark and Shark frameworks for running real-time queries on top of Cassandra data.We will start by surveying the current Cassandra analytics landscape, including Hadoop and HIVE, and touch on the use of custom input formats to extract data from Cassandra. We will then dive into Spark and Shark, two memory-based cluster computing frameworks, and how they enable often dramatic improvements in query speed and productivity, over the standard solutions today.
.net developer for Jupyter Notebook and Apache Spark and viceversaMarco Parenzan
Jupyter Notebooks and Apache Spark are first class citizens of the Data Science space, a truly requirement for the "modern" data scientist. But there was a requirement: being a python developer. Now Microsoft is investing on C# as another first class citizen in this space. Let's look what .net can do for notebooks and spark and what are notebooks and spark.
From POC to Spark Summit talk in under 12 months - how we selected Apache Spark as our main Data Engineering platform and deployed first large scale project using this technology.
Spark After Dark - LA Apache Spark Users Group - Feb 2015Chris Fregly
Spark After Dark is a mock dating site that uses the latest Spark libraries including Spark SQL, BlinkDB, Tachyon, Spark Streaming, MLlib, and GraphX to generate high-quality dating recommendations for its members and blazing fast analytics for its operators.
We begin with brief overview of Spark, Spark Libraries, and Spark Use Cases. In addition, we'll discuss the modern day Lambda Architecture that combines real-time and batch processing into a single system. Lastly, we present best practices for monitoring and tuning a highly-available Spark and Spark Streaming cluster.
There will be many live demos covering everything from basic topics such as ETL and data ingestion to advanced topics such as streaming, sampling, approximations, machine learning, textual analysis, and graph processing.
Spark after Dark by Chris Fregly of DatabricksData Con LA
Spark After Dark is a mock dating site that uses the latest Spark libraries, AWS Kinesis, Lambda Architecture, and Probabilistic Data Structures to generate dating recommendations.
There will be 5+ demos covering everything from basic data ETL to advanced data processing including Alternating Least Squares Machine Learning/Collaborative Filtering and PageRank Graph Processing.
There is heavy emphasis on Spark Streaming and AWS Kinesis.
Watch the video here
https://www.youtube.com/watch?v=g0i_d8YT-Bs
Introduction to NetGuardians' Big Data Software StackJérôme Kehrli
NetGuardians is executing it's Big Data Analytics Platform on three key Big Data components underneath: ElasticSearch, Apache Mesos and Apache Spark. This is a presentation of the behaviour of this software stack.
Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, ...Chris Fregly
This talk highlights the Data Sources API which participates in the Spark SQL DataFrame Catalyst Optimizer. We dive deep into the super-advanced Cassandra's open source implementation @ github.com/datastax/spark-cassandra-connector. We discuss data locality, cluster deployment - as well as the pros and cons of mixing OLAP and OLTP workloads.
We also implement a SimpleDataSource which is a basic implementation of the DataSources API.
All analysis is done with Apache Zeppelin.
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MorePaco Nathan
Spark and Databricks component of the O'Reilly Media webcast "2015 Data Preview: Spark, Data Visualization, YARN, and More", as a preview of the 2015 Strata + Hadoop World conference in San Jose http://www.oreilly.com/pub/e/3289
Cassandra Day SV 2014: Spark, Shark, and Apache CassandraDataStax Academy
This session covers our experience with using the Spark and Shark frameworks for running real-time queries on top of Cassandra data.We will start by surveying the current Cassandra analytics landscape, including Hadoop and HIVE, and touch on the use of custom input formats to extract data from Cassandra. We will then dive into Spark and Shark, two memory-based cluster computing frameworks, and how they enable often dramatic improvements in query speed and productivity, over the standard solutions today.
.net developer for Jupyter Notebook and Apache Spark and viceversaMarco Parenzan
Jupyter Notebooks and Apache Spark are first class citizens of the Data Science space, a truly requirement for the "modern" data scientist. But there was a requirement: being a python developer. Now Microsoft is investing on C# as another first class citizen in this space. Let's look what .net can do for notebooks and spark and what are notebooks and spark.
From POC to Spark Summit talk in under 12 months - how we selected Apache Spark as our main Data Engineering platform and deployed first large scale project using this technology.
Spark After Dark - LA Apache Spark Users Group - Feb 2015Chris Fregly
Spark After Dark is a mock dating site that uses the latest Spark libraries including Spark SQL, BlinkDB, Tachyon, Spark Streaming, MLlib, and GraphX to generate high-quality dating recommendations for its members and blazing fast analytics for its operators.
We begin with brief overview of Spark, Spark Libraries, and Spark Use Cases. In addition, we'll discuss the modern day Lambda Architecture that combines real-time and batch processing into a single system. Lastly, we present best practices for monitoring and tuning a highly-available Spark and Spark Streaming cluster.
There will be many live demos covering everything from basic topics such as ETL and data ingestion to advanced topics such as streaming, sampling, approximations, machine learning, textual analysis, and graph processing.
Spark after Dark by Chris Fregly of DatabricksData Con LA
Spark After Dark is a mock dating site that uses the latest Spark libraries, AWS Kinesis, Lambda Architecture, and Probabilistic Data Structures to generate dating recommendations.
There will be 5+ demos covering everything from basic data ETL to advanced data processing including Alternating Least Squares Machine Learning/Collaborative Filtering and PageRank Graph Processing.
There is heavy emphasis on Spark Streaming and AWS Kinesis.
Watch the video here
https://www.youtube.com/watch?v=g0i_d8YT-Bs
Introduction to NetGuardians' Big Data Software StackJérôme Kehrli
NetGuardians is executing it's Big Data Analytics Platform on three key Big Data components underneath: ElasticSearch, Apache Mesos and Apache Spark. This is a presentation of the behaviour of this software stack.
State of Play. Data Science on Hadoop in 2015 by SEAN OWEN at Big Data Spain ...Big Data Spain
http://www.bigdataspain.org/2014/conference/state-of-play-data-science-on-hadoop-in-2015-keynote
Machine Learning is not new. Big Machine Learning is qualitatively different: More data beats algorithm improvement, scale trumps noise and sample size effects, can brute-force manual tasks.
Session presented at Big Data Spain 2014 Conference
18th Nov 2014
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Slides: https://speakerdeck.com/bigdataspain/state-of-play-data-science-on-hadoop-in-2015-by-sean-owen-at-big-data-spain-2014
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal 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.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
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.
1. *Sparsity Technologies — Powering Extreme Data! sparsity–
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Sparksee Graph Database
Polyglot graph databases
using OCL as pivot
March 2016. Raquel Pau!
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Sparksee Graph Database!
• The development process
• The languages mismatch problem
• OCL as pivot graph query language
Agenda!
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º! Sparksee Graph Database
Why not UML/OCL as a pivot language?
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UML/OCL as a pivot language
1. Code generation becomes unnecessary.
2. A database could become polyglot due to its high expressivity.
3. Executable specifications => Specifications become code.
4. The same constructions are valid for constraints and update
operations.
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OCL as Graph Query Language: Desired extensions
• Graph patterns: OCL has not an specific syntax to express
graph patterns, but allows to define variables and their values.
• Supported types: Graph is needed to support graph operations
such as shortest path.
• Syntactic sugar: needed to express sorting clauses and
projections.