Data replication and synchronization tool
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This presentation discusses a data sharing synchronization tool, implemented using Infinispan. The focus is medical images and meta data such as the cancer imaging archive (TCIA)
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Data replication and synchronization tool
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PostGIS turns Postgres into a spatial database. It adds support for geographic objects, allowing location queries to be run in SQL.
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In this talk, we're going to first describe extension APIs. Then, we’re going to present four popular Postgres extensions, and demo their use.
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* HLL & TopN add approximation algorithms to Postgres. These algorithms are used when real-time responses matter more than exact results.
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PostgreSQL is becoming the relational database of choice. One important factor in the rising popularity of Postgres are the extension APIs. These APIs allow developers to extend any database sub-module’s behavior for higher performance, security, or new functionality. As a result, Postgres users have access to over a hundred extensions today, and more to come in the future.
In this talk, I’m going to first describe PostgreSQL’s extension APIs. These APIs are unique to Postgres, and have the potential to change the database landscape. Then, we’re going to present the four most popular Postgres extensions, show the use cases where they are applicable, and demo their usage.
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pgpartman makes creating and managing partitions in Postgres easy. Through careful partition management with pgpartman, Postgres offers 5-10x higher write and query performance for time-series data.
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Course Website http://pbdmng.datatoknowledge.it/
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2) It provides a customizable data adapter layer and optimized data access strategies for patterns like strided, column, and discrete reads to improve performance.
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Coherance in dissemination- Msis 2007
1) Statistics Denmark provides statistical data through a centralized dissemination system called StatBank, which hosts 1,500 tables covering all subjects. StatBank allows users to access and download data in a variety of formats for free.
2) While some data inputs and management are decentralized, output is centralized through StatBank to ensure coordination of structure, formatting and simultaneous releases.
3) Principles of the dissemination system include prioritizing electronic access over paper, using StatBank as the single source of official statistics for all publications and target audiences. Metadata is also centralized and stored once for reuse across outputs.
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This presentation contains a summary of the content provided in this research paper and was presented as a paper discussion for the course, Advanced Database Systems in Computer Science.
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hbaseconasia2019 Bridging the Gap between Big Data System Software Stack and ...
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...
SciMATE: A Novel MapReduce-Like Framework for Multiple Scientific Data Formats
SciMATE: A Novel MapReduce-Like Framework for Multiple Scientific Data Formats
A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
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Visualizing big data in the browser using spark
This document discusses using Spark to enable interactive visualization of big data in the browser. Spark can help address challenges of manipulating large datasets by caching data in memory to reduce latency, increasing parallelism, and summarizing, modeling, or sampling large datasets to reduce the number of data points. The goal is to put visualization back into the normal workflow of data analysis regardless of data size and enable sharing and collaboration through interactive and reproducible visualizations in the browser.
20160331 sa introduction to big data pipelining berlin meetup 0.3
This document discusses building data pipelines with Apache Spark and DataStax Enterprise (DSE) for both static and real-time data. It describes how DSE provides a scalable, fault-tolerant platform for distributed data storage with Cassandra and real-time analytics with Spark. It also discusses using Kafka as a messaging queue for streaming data and processing it with Spark. The document provides examples of using notebooks, Parquet, and Akka for building pipelines to handle both large static datasets and fast, real-time streaming data sources.
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...Ilkay Altintas, Ph.D.
cientific workflows are used by many scientific communities to capture, automate and standardize computational and data practices in science. Workflow-based automation is often achieved through a craft that combines people, process, computational and Big Data platforms, application-specific purpose and programmability, leading to provenance-aware archival and publications of the results. This talk summarizes varying and changing requirements for distributed workflows influenced by Big Data and heterogeneous computing architectures and present a methodology for workflow-driven science based on these maturing requirements.Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017
"Strategies for supporting near real time analytics, OLAP, and interactive data exploration" - Dr. Jeremy Engle, Engineering Manager Data Team at Jellyvision
Transforming Data Architecture Complexity at Sears - StampedeCon 2013
At the StampedeCon 2013 Big Data conference in St. Louis, Justin Sheppard discussed Transforming Data Architecture Complexity at Sears. High ETL complexity and costs, data latency and redundancy, and batch window limits are just some of the IT challenges caused by traditional data warehouses. Gain an understanding of big data tools through the use cases and technology that enables Sears to solve the problems of the traditional enterprise data warehouse approach. Learn how Sears uses Hadoop as a data hub to minimize data architecture complexity – resulting in a reduction of time to insight by 30-70% – and discover “quick wins” such as mainframe MIPS reduction.
Anusua Trivedi, Data Scientist at Texas Advanced Computing Center (TACC), UT ...
Building a Recommender System for Publications using Vector Space Model and Python:In recent years, it has become very common that we have access to large number of publications on similar or related topics. Recommendation systems for publications are needed to locate appropriate published articles from a large number of publications on the same topic or on similar topics. In this talk, I will describe a recommender system framework for PubMed articles. PubMed is a free search engine that primarily accesses the MEDLINE database of references and abstracts on life-sciences and biomedical topics. The proposed recommender system produces two types of recommendations – i) content-based recommendation and (ii) recommendations based on similarities with other users’ search profiles. The first type of recommendation, viz., content-based recommendation, can efficiently search for material that is similar in context or topic to the input publication. The second mechanism generates recommendations using the search history of users whose search profiles match the current user. The content-based recommendation system uses a Vector Space model in ranking PubMed articles based on the similarity of content items. To implement the second recommendation mechanism, we use python libraries and frameworks. For the second method, we find the profile similarity of users, and recommend additional publications based on the history of the most similar user. In the talk I will present the background and motivation for these recommendation systems, and discuss the implementations of this PubMed recommendation system with example.
This talk will cover, via live demo & code walk-through, the key lessons we’ve learned while building such real-world software systems over the past few years. We’ll incrementally build a hybrid machine learned model for fraud detection, combining features from natural language processing, topic modeling, time series analysis, link analysis, heuristic rules & anomaly detection. We’ll be looking for fraud signals in public email datasets, using Python & popular open-source libraries for data science and Apache Spark as the compute engine for scalable parallel processing.
Deep Learning on Apache® Spark™: Workflows and Best Practices
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™: Workflows and Best Practices
This document summarizes a presentation about deep learning workflows and best practices on Apache Spark. It discusses how deep learning fits within broader data pipelines for tasks like training and transformation. It also outlines recurring patterns for integrating Spark and deep learning frameworks, including using Spark for data parallelism and embedding deep learning transforms. The presentation provides tips for developers on topics like using GPUs with PySpark and monitoring deep learning jobs. It concludes by discussing challenges in the areas of distributed deep learning and Spark integration.
Deep Learning on Apache® Spark™ : Workflows and Best Practices
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
WorDS of Data Science in the Presence of Heterogenous Computing Architectures
ISUM 2015 Keynote
Summary: Computational and Data Science is about extracting knowledge from data and modeling. This end goal can only be achieved through a craft that combines people, processes, computational and Big Data platforms, application-specific purpose and programmability. Publications and provenance of the data products products leading to these publications are also important. With this in mind, this talk defines a terminology for computational and data science applications, and discuss why focusing on these concepts is important for executability and reproducibility in computational and data science.
Storage Systems For Scalable systems
Haytham ElFadeel presented on next-generation storage systems and key-value stores. He began with an overview of scalable systems and the need for both vertical and horizontal scalability. He discussed the limitations of traditional databases in scaling, including complexity, wasted features, and multi-step query processing. Key-value stores were presented as an alternative, offering simple interfaces and designs optimized for scaling across hundreds of machines. Performance comparisons showed key-value stores significantly outperforming databases. Systems discussed included Amazon Dynamo, Facebook Cassandra, and Redis.
Meta scale kognitio hadoop webinar
This webinar discusses tools for making big data easy to work with. It covers MetaScale Expertise, which provides Hadoop expertise and case studies. Kognitio Analytics is discussed as a way to accelerate Hadoop for organizations. The webinar agenda includes an introduction, presentations on MetaScale and Kognitio, and a question and answer session. Rethinking data strategies with Hadoop and using in-memory analytics are presented as ways to gain insights from large, diverse datasets.
Parallel Distributed Deep Learning on HPCC Systems
As part of the 2018 HPCC Systems Community Day event:
The training process for modern deep neural networks requires big data and large amounts of computational power. Combining HPCC Systems and Google’s TensorFlow, Robert created a parallel stochastic gradient descent algorithm to provide a basis for future deep neural network research, thereby helping to enhance the distributed neural network training capabilities of HPCC Systems.
Robert Kennedy is a first year Ph.D. student in CS at Florida Atlantic University with research interests in Deep Learning and parallel and distributed computing. His current research is in improving distributed deep learning by implementing and optimizing distributed algorithms.
Data Café — A Platform For Creating Biomedical Data Lakes
A podium abstract presented at AMIA 2016 Joint Summits on Translational Science. This discusses Data Café — A Platform For Creating Biomedical Data Lakes.
Introduction to Hadoop and MapReduce
This document provides an outline and introduction for a lecture on MapReduce and Hadoop. It discusses Hadoop architecture including HDFS and YARN, and how they work together to provide distributed storage and processing of big data across clusters of machines. It also provides an overview of MapReduce programming model and how data is processed through the map and reduce phases in Hadoop. References several books on Hadoop, MapReduce, and big data fundamentals.
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
Volunteers around the world increasingly act as human sensors to collect millions of data points. A team from the World Bank trained deep learning models, using Apache Spark and BigDL, to confirm that photos gathered through a crowdsourced data collection pilot matched the goods for which observations were submitted.
In this talk, Maurice Nsabimana, a statistician at the World Bank, and Jiao Wang, a software engineer on the Big Data Technology team at Intel, demonstrate a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world. BigDL is a distributed deep learning library designed from the ground up to run natively on Apache Spark. It enables data engineers and scientists to write deep learning applications in Scala or Python as standard Spark programs-without having to explicitly manage distributed computations. Attendees of this session will learn how to get started with BigDL, which runs in any Apache Spark environment, whether on-premise or in the Cloud.
Meta scale kognitio hadoop webinar
The webinar discusses how organizations can make big data easy to use with the right tools and talent. It presents on MetaScale's expertise in helping Sears Holdings implement Hadoop and how Kognitio's in-memory analytics platform can accelerate Hadoop for organizations. The webinar agenda includes an introduction, a case study on Sears Holdings' Hadoop implementation, an explanation of how Kognitio's platform accelerates Hadoop, and a Q&A session.
Data Lake Acceleration vs. Data Virtualization - What’s the difference?
Watch full webinar here: https://bit.ly/3hgOSwm
Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.
Building Big Data Streaming Architectures
In this talk we depict how to build streaming architectures connecting to Kafka with different technologies. Includes links to Github code samples.
Using graphs for recommendations
This document contains a presentation on using graph databases for recommendations. It begins with an introduction to graphs and graph theory, then discusses what graph databases are and how they are different from relational databases. It explains how graphs are well-suited for complex querying and representing connected data. The presentation describes how recommendation systems work and how graph algorithms and storing recommendation data in a graph structure provide benefits like real-time recommendations, navigating relationships between items, and efficient operations. It concludes with a demonstration, examples, and discussing future events.
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20160331 sa introduction to big data pipelining berlin meetup 0.3
20160331 sa introduction to big data pipelining berlin meetup 0.3
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...
Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017
Jeremy Engle's slides from Redshift / Big Data meetup on July 13, 2017
Transforming Data Architecture Complexity at Sears - StampedeCon 2013
Transforming Data Architecture Complexity at Sears - StampedeCon 2013
Anusua Trivedi, Data Scientist at Texas Advanced Computing Center (TACC), UT ...
Anusua Trivedi, Data Scientist at Texas Advanced Computing Center (TACC), UT ...
Deep Learning on Apache® Spark™: Workflows and Best Practices
Deep Learning on Apache® Spark™: Workflows and Best Practices
Deep Learning on Apache® Spark™: Workflows and Best Practices
Deep Learning on Apache® Spark™: Workflows and Best Practices
Deep Learning on Apache® Spark™ : Workflows and Best Practices
Deep Learning on Apache® Spark™ : Workflows and Best Practices
WorDS of Data Science in the Presence of Heterogenous Computing Architectures
WorDS of Data Science in the Presence of Heterogenous Computing Architectures
Parallel Distributed Deep Learning on HPCC Systems
Parallel Distributed Deep Learning on HPCC Systems
Data Café — A Platform For Creating Biomedical Data Lakes
Data Café — A Platform For Creating Biomedical Data Lakes
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
Data Lake Acceleration vs. Data Virtualization - What’s the difference?
Data Lake Acceleration vs. Data Virtualization - What’s the difference?
More from Pradeeban Kathiravelu, Ph.D.
Google Summer of Code_2023.pdf
Google Summer of Code (GSoC) is a remote open-source internship program funded by Google, for contributors to remotely work with an open source organization (and get paid) over a summer.
https://kkpradeeban.blogspot.com/2022/11/google-summer-of-code-gsoc-2023.html
Google Summer of Code (GSoC) 2022
GSoC 2022 comes with more changes and flexibility. This presentation aims to give an introduction to the contributors and what to expect this summer.
https://kkpradeeban.blogspot.com/2022/01/google-summer-of-code-gsoc-2022.html
Google Summer of Code (GSoC) 2022
This document provides information about Google Summer of Code (GSoC) 2022. It discusses why students should participate in GSoC, the application timeline and process, tips for finding projects and communicating with mentors, expectations during the coding and evaluation periods, and opportunities to continue contributing to open source projects after GSoC. The overall goal is to help potential contributors understand what is required to be accepted into and succeed in GSoC.
Niffler: A DICOM Framework for Machine Learning and Processing Pipelines.
Niffler: A DICOM Framework for Machine Learning and Processing Pipelines.Pradeeban Kathiravelu, Ph.D.
Niffler is an efficient DICOM Framework for machine learning pipelines and processing workflows on metadata. It facilitates efficient transfer of DICOM images on-demand and real-time from PACS to the research environments, to run processing workflows and machine learning pipelines.
https://github.com/Emory-HITI/Niffler/Google summer of code (GSoC) 2021
This is an introductory presentation to GSoC 2021. This year there were a few specific changes to GSoC compared to the past years. Specifically, workload and the student stipend have been made half in 2021 compared to the previous years.
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology ...
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology ...Pradeeban Kathiravelu, Ph.D.
We propose Niffler (https://github.com/Emory-HITI/Niffler), an open-source ML framework that runs in research
clusters by receiving images in real-time using DICOM protocol from hospitals' PACS.Google Summer of Code (GSoC) 2020 for mentors
This presentation aims to introduce GSoC to new mentors and mentoring organizations. More details - https://kkpradeeban.blogspot.com/2019/12/google-summer-of-code-gsoc-2020-for.html
Google Summer of Code (GSoC) 2020
An introductory presentation to Google Summer of Code (GSoC), focusing on the year 2020. More information can be found at https://kkpradeeban.blogspot.com/search/label/GSoC
Data Services with Bindaas: RESTful Interfaces for Diverse Data Sources
The diversity of data management systems affords developers the luxury of building heterogeneous architectures to address the unique needs of big data. It allows one to mix-n-match systems that can store, query, update, and process data based on specific use cases. However, this heterogeneity brings
with it the burden of developing custom interfaces for each data management system. Existing big data frameworks fall short in mitigating these challenges imposed. In this paper, we present Bindaas, a secure and extensible big data middleware that offers uniform access to diverse data sources. By providing a RESTful web service interface to the data sources, Bindaas exposes query, update, store, and delete functionality of the data sources as data service APIs, while providing turn-key support for standard operations involving access control and audit-trails. The research community has deployed Bindaas in
various production environments in healthcare. Our evaluations highlight the efficiency of Bindaas in serving concurrent requests to data source instances with minimal overheads.
The UCLouvain Public Defense of my EMJD-DC Double Doctorate Ph.D. degree
The UCLouvain Public Defense of my EMJD-DC Double Doctorate Ph.D. degreePradeeban Kathiravelu, Ph.D.
This is the 2nd defense of my Ph.D. double degree.
More details - https://kkpradeeban.blogspot.com/2019/08/my-phd-defense-software-defined-systems.html
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Compos...
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Compos...Pradeeban Kathiravelu, Ph.D.
Presentation slides with the script.
More details:
https://kkpradeeban.blogspot.com/2019/07/my-phd-defense-software-defined-systems.htmlMy Ph.D. Defense - Software-Defined Systems for Network-Aware Service Composi...
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Composi...Pradeeban Kathiravelu, Ph.D.
The presentation slides of my Ph.D. thesis. For more information - https://kkpradeeban.blogspot.com/2019/07/my-phd-defense-software-defined-systems.htmlUCL Ph.D. Confirmation 2018
My presentation for the UCLouvain Ph.D. Confirmation
https://kkpradeeban.blogspot.com/2018/01/ucl-phd-confirmation.html
Software-Defined Systems for Network-Aware Service Composition and Workflow P...
Software-Defined Systems for Network-Aware Service Composition and Workflow P...Pradeeban Kathiravelu, Ph.D.
The presentation slides of my Ph.D. thesis proposal ("CAT" as known in my university). I received a score of 18/20.
Supervisors:
Prof. Luís Veiga (IST, ULisboa)
Prof. Peter Van Roy (UCLouvain)
Jury:
Prof. Javid Taheri (Karlstad University)
Prof. Fernando Mira da Silva (IST, ULisboa)Moving bits with a fleet of shared virtual routers
This is my presentation at IFIP Networking 2018 in Zurich.
In this paper, we propose a cloud-assisted network as an alternative connectivity provider.
More details: https://kkpradeeban.blogspot.com/2018/05/moving-bits-with-fleet-of-shared.html
Software-Defined Data Services: Interoperable and Network-Aware Big Data Exec...
Software-Defined Data Services: Interoperable and Network-Aware Big Data Exec...Pradeeban Kathiravelu, Ph.D.
Services that access or process a large volume of data are known as data services. Big data frameworks consist of diverse storage media and heterogeneous data formats. Through their service-based approach, data services offer a standardized execution model to big data frameworks. Software-Defined Networking (SDN) increases the programmability of the network, by unifying the control plane centrally, away from the distributed data plane devices. In this paper, we present Software-Defined Data Services (SDDS), extending the data services with the SDN paradigm. SDDS consists of two aspects. First, it models the big data executions as data services or big services composed of several data services. Then, it orchestrates the services centrally in an interoperable manner, by logically separating the executions from the storage. We present the design of an SDDS orchestration framework for network-aware big data executions in data centers. We then evaluate the performance of SDDS through microbenchmarks on a prototype implementation. By extending SDN beyond data centers, we can deploy SDDS in broader execution environments.
https://kkpradeeban.blogspot.com/2018/04/software-defined-data-services.htmlOn-Demand Service-Based Big Data Integration: Optimized for Research Collabor...
On-Demand Service-Based Big Data Integration: Optimized for Research Collabor...Pradeeban Kathiravelu, Ph.D.
This is the presentation of DMAH workshop in conjunction with VLDB'17. This describes my work during my stay at Emory BMI.
More information: https://kkpradeeban.blogspot.com/2017/08/on-demand-service-based-big-data.htmlScalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...Pradeeban Kathiravelu, Ph.D.
This is a poster I presented at ACRO Summer School at Karlstad University. This presents my PhD work.
More details: http://kkpradeeban.blogspot.com/2017/07/my-first-polygonal-journey.htmlSoftware-Defined Inter-Cloud Composition of Big Services
This is the presentation I did to the audience of EMJD-DC Spring Event 2017 Brussels to discuss my research. http://kkpradeeban.blogspot.be/2017/05/emjd-dc-spring-event-2017.html
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...Pradeeban Kathiravelu, Ph.D.
This document summarizes the PhD work of Pradeeban Kathiravelu on improving scalability and resilience in multi-tenant distributed clouds. It describes two approaches: 1) SMART uses SDN to provide differentiated quality of service and service level agreements by dynamically diverting and cloning priority network flows. 2) Mayan componentizes big data services as microservices that can be executed in a network-aware and scalable way across distributed clouds. Evaluation shows these approaches improve speedup and ensure SLAs for critical flows compared to network-agnostic distributed execution.More from Pradeeban Kathiravelu, Ph.D. (20)
Niffler: A DICOM Framework for Machine Learning and Processing Pipelines.
Niffler: A DICOM Framework for Machine Learning and Processing Pipelines.
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology ...
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology ...
Data Services with Bindaas: RESTful Interfaces for Diverse Data Sources
Data Services with Bindaas: RESTful Interfaces for Diverse Data Sources
The UCLouvain Public Defense of my EMJD-DC Double Doctorate Ph.D. degree
The UCLouvain Public Defense of my EMJD-DC Double Doctorate Ph.D. degree
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Compos...
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Compos...
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Composi...
My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Composi...
Software-Defined Systems for Network-Aware Service Composition and Workflow P...
Software-Defined Systems for Network-Aware Service Composition and Workflow P...
Moving bits with a fleet of shared virtual routers
Moving bits with a fleet of shared virtual routers
Software-Defined Data Services: Interoperable and Network-Aware Big Data Exec...
Software-Defined Data Services: Interoperable and Network-Aware Big Data Exec...
On-Demand Service-Based Big Data Integration: Optimized for Research Collabor...
On-Demand Service-Based Big Data Integration: Optimized for Research Collabor...
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...
Software-Defined Inter-Cloud Composition of Big Services
Software-Defined Inter-Cloud Composition of Big Services
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...
Scalability and Resilience of Multi-Tenant Distributed Clouds in the Big Serv...
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Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
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were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
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https://ashpc.eu/
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Data replication and synchronization tool
- 1. Data Replication and Synchronization Tool Ashish Sharma Pradeeban Kathiravelu PPoowweerrppooiinntt TTeemmppllaatteess 1
- 2. Introduction • Data is huge • Consumers often share a sub set of data with others. – Pointers to data, actually. • Medical data is structured in hierarchies. Powerpoint Templates 2
- 3. Motivation • Creating and sharing pointers to interesting sub sets of data. • Data Sharing Synchronization System – Fault-tolerant. – In-Memory. • Generic, while targeting the medical images and meta data. – The Cancer Imaging Archive Powerpoint Templates 3 (TCIA)
- 4. Solution Architecture • Users create, share, and update replica sets from a data source. • Infinispan In-Memory Data Grid (version 6.0.2) to store the replica sets. Fig 1. Deployment Architecture Powerpoint Templates 4
- 5. Execution Flow • Publisher-Consumer API to consume the replica sets and Data Provider API to communicate with the data source. Powerpoint Fig 2. Execution Templates Flow 5
- 6. Design Fig 3. Back-end Class Hierarchy • DataProSpecs API createReplicaSet getReplicaSet updateReplicaSet duplicateReplicaSet deleteReplicaSet getRawData Powerpoint Templates 6
- 7. Extensibility • Not tightly coupled to the technology. – Other data-grids • Hazelcast, Terracotta Big Memory, Oracle Coherence – Persistence • Integration to SQL or NoSQL solutions such as Mongo DB. –Data sources other than TCIA. Powerpoint Templates 7
- 8. What Infinispan offers? • High Performance and Scalability. • Fault-tolerance – Multiple nodes with TCP-IP or Multicast based JGroups clustering configurations. • Distributed Execution. – Optimized for single node as a local cache as well as a multiple-node execution. • MapReduce Framework. Powerpoint Templates 8
- 9. What Infinispan offers? • High Performance and Scalable. • Fault-tolerant – Multiple nodes with TCP-IP or Multicast based JGroups clustering configurations. Thank you! • Distributed Execution. – Optimized for single node as a local cache as well as a multiple-node execution. • MapReduce Framework. Powerpoint Templates 9
- 10. What Infinispan offers? • High Performance and Scalable. • Fault-tolerant – Multiple nodes with TCP-IP or Multicast based JGroups clustering configurations. Thank you! • Distributed Execution. – Optimized for single node as a local cache as well as a multiple-node execution. • MapReduce Framework. Powerpoint Templates 10