paleofire R package presentation given at the Global Paleofire Working Group workshop at Harvard Forest (NSF-PAGES-GPWG, 28 sept 2015): Paleofire: data-model comparisons for the past millenium
This document discusses a dataset of shipping and weather data from 1662-1855 collected from captains' logs. It contains details on ships, routing, locations, and weather parameters. The data is stored in a file geodatabase with associated lookup tables for wind force and direction from different maritime agencies. Joining these lookup tables doubled the number of feature classes as the wind force and direction were merged into single columns. Diagrams of the data model are also included.
Apache Sirona is an open source monitoring solution for Java applications. It provides simple Gauge and Counter objects to collect metrics. Gauges measure values like memory usage and thread counts, while Counters aggregate metrics like response times and concurrency levels. Metrics can be stored in memory, Cassandra, or Graphite. A central Collector webapp is available to view aggregated reports from multiple instances. Sirona uses a plugin architecture and aims to integrate monitoring natively into applications without external dependencies.
2021 Dask Summit - Using STAC to catalog SpatioTemporal datasetsRob Emanuele
The document discusses using the SpatioTemporal Asset Catalog (STAC) to catalog geospatial datasets. STAC defines JSON schemas to encode metadata about spatiotemporal data like remote sensing imagery. This allows datasets like the European Space Agency's Sentinel-2 satellite data, containing petabytes of images, to be more easily searched. The STAC API also defines standards for searching and discovering STAC metadata. Tools like PySTAC and pystac-client make it easier to work with STAC catalogs and APIs in Python. Open questions remain around best representing multi-dimensional datasets like Zarr in STAC.
Thorny path to the Large-Scale Graph Processing (Highload++, 2014)Alexey Zinoviev
Alexey Zinoviev presented this paper on the Highload++ conference http://www.highload.ru/2014/abstracts/1516.html
This paper covers next topics: Pregel, Graph Theory, Giraph, Okapi, GraphX, GraphChi, Spark, Shrotest Path Problem, Road Network, Road Graph
Remember the last time you tried to write a MapReduce job (obviously something non trivial than a word count)? It sure did the work, but has lot of pain points from getting an idea to implement it in terms of map reduce. Did you wonder how life will be much simple if you had to code like doing collection operations and hence being transparent* to its distributed nature? Did you want/hope for more performant/low latency jobs? Well, seems like you are in luck.
In this talk, we will be covering a different way to do MapReduce kind of operations without being just limited to map and reduce, yes, we will be talking about Apache Spark. We will compare and contrast Spark programming model with Map Reduce. We will see where it shines, and why to use it, how to use it. We’ll be covering aspects like testability, maintainability, conciseness of the code, and some features like iterative processing, optional in-memory caching and others. We will see how Spark, being just a cluster computing engine, abstracts the underlying distributed storage, and cluster management aspects, giving us a uniform interface to consume/process/query the data. We will explore the basic abstraction of RDD which gives us so many awesome features making Apache Spark a very good choice for your big data applications. We will see this through some non trivial code examples.
Session at the IndicThreads.com Confence held in Pune, India on 27-28 Feb 2015
http://www.indicthreads.com
http://pune15.indicthreads.com
Hadoop and Hive Development at Facebookelliando dias
Facebook generates large amounts of user data daily from activities like status updates, photo uploads, and shared content. This data is stored in Hadoop using Hive for analytics. Some key facts:
- Facebook adds 4TB of new compressed data daily to its Hadoop cluster.
- The cluster has 4800 cores and 5.5PB of storage across 12TB nodes.
- Hive is used for over 7500 jobs daily and by around 200 engineers/analysts monthly.
- Performance improvements to Hive include lazy deserialization, map-side aggregation, and joins.
DARTS: Differentiable Architecture Search at 社内論文読み会Masashi Shibata
This document summarizes the DARTS paper, which proposes differentiable architecture search (DARTS) to relax the discrete search space of neural architectures into a continuous space. DARTS uses continuous relaxation to replace the discrete choice of architectures with a softmax over all possibilities. It then performs bi-level optimization over the architecture hyperparameters and network weights to find optimal architectures. DARTS searches over architectures made up of repeating building blocks called cells, and achieves state-of-the-art results on CIFAR-10 using 8 normal cells and 8 reduction cells.
This document discusses a dataset of shipping and weather data from 1662-1855 collected from captains' logs. It contains details on ships, routing, locations, and weather parameters. The data is stored in a file geodatabase with associated lookup tables for wind force and direction from different maritime agencies. Joining these lookup tables doubled the number of feature classes as the wind force and direction were merged into single columns. Diagrams of the data model are also included.
Apache Sirona is an open source monitoring solution for Java applications. It provides simple Gauge and Counter objects to collect metrics. Gauges measure values like memory usage and thread counts, while Counters aggregate metrics like response times and concurrency levels. Metrics can be stored in memory, Cassandra, or Graphite. A central Collector webapp is available to view aggregated reports from multiple instances. Sirona uses a plugin architecture and aims to integrate monitoring natively into applications without external dependencies.
2021 Dask Summit - Using STAC to catalog SpatioTemporal datasetsRob Emanuele
The document discusses using the SpatioTemporal Asset Catalog (STAC) to catalog geospatial datasets. STAC defines JSON schemas to encode metadata about spatiotemporal data like remote sensing imagery. This allows datasets like the European Space Agency's Sentinel-2 satellite data, containing petabytes of images, to be more easily searched. The STAC API also defines standards for searching and discovering STAC metadata. Tools like PySTAC and pystac-client make it easier to work with STAC catalogs and APIs in Python. Open questions remain around best representing multi-dimensional datasets like Zarr in STAC.
Thorny path to the Large-Scale Graph Processing (Highload++, 2014)Alexey Zinoviev
Alexey Zinoviev presented this paper on the Highload++ conference http://www.highload.ru/2014/abstracts/1516.html
This paper covers next topics: Pregel, Graph Theory, Giraph, Okapi, GraphX, GraphChi, Spark, Shrotest Path Problem, Road Network, Road Graph
Remember the last time you tried to write a MapReduce job (obviously something non trivial than a word count)? It sure did the work, but has lot of pain points from getting an idea to implement it in terms of map reduce. Did you wonder how life will be much simple if you had to code like doing collection operations and hence being transparent* to its distributed nature? Did you want/hope for more performant/low latency jobs? Well, seems like you are in luck.
In this talk, we will be covering a different way to do MapReduce kind of operations without being just limited to map and reduce, yes, we will be talking about Apache Spark. We will compare and contrast Spark programming model with Map Reduce. We will see where it shines, and why to use it, how to use it. We’ll be covering aspects like testability, maintainability, conciseness of the code, and some features like iterative processing, optional in-memory caching and others. We will see how Spark, being just a cluster computing engine, abstracts the underlying distributed storage, and cluster management aspects, giving us a uniform interface to consume/process/query the data. We will explore the basic abstraction of RDD which gives us so many awesome features making Apache Spark a very good choice for your big data applications. We will see this through some non trivial code examples.
Session at the IndicThreads.com Confence held in Pune, India on 27-28 Feb 2015
http://www.indicthreads.com
http://pune15.indicthreads.com
Hadoop and Hive Development at Facebookelliando dias
Facebook generates large amounts of user data daily from activities like status updates, photo uploads, and shared content. This data is stored in Hadoop using Hive for analytics. Some key facts:
- Facebook adds 4TB of new compressed data daily to its Hadoop cluster.
- The cluster has 4800 cores and 5.5PB of storage across 12TB nodes.
- Hive is used for over 7500 jobs daily and by around 200 engineers/analysts monthly.
- Performance improvements to Hive include lazy deserialization, map-side aggregation, and joins.
DARTS: Differentiable Architecture Search at 社内論文読み会Masashi Shibata
This document summarizes the DARTS paper, which proposes differentiable architecture search (DARTS) to relax the discrete search space of neural architectures into a continuous space. DARTS uses continuous relaxation to replace the discrete choice of architectures with a softmax over all possibilities. It then performs bi-level optimization over the architecture hyperparameters and network weights to find optimal architectures. DARTS searches over architectures made up of repeating building blocks called cells, and achieves state-of-the-art results on CIFAR-10 using 8 normal cells and 8 reduction cells.
The document provides an overview of Heat, OpenStack's orchestration service. It describes Heat's integration with other OpenStack services like Nova, Neutron, Glance, etc. It outlines new features in the Havana release like improved networking support, initial support for a native template language (HOT), and integration with Ceilometer for monitoring. It also describes provider resources which allow users and deployers to define custom resource types and nested stack templates. Finally it lists some planned improvements for the Icehouse release, including further development of the HOT DSL and engine scalability.
Working with OpenStreetMap using Apache Spark and GeotrellisRob Emanuele
The document discusses OpenStreetMap (OSM) and OSMesa, a framework for distributed processing of OSM data. It describes the OSM data model including nodes, ways, relations, and tags. It then discusses a use case where OSM change history needed to be processed at scale to backfill missing maps statistics. OSMesa was developed to handle this using Apache Spark and GeoTrellis on AWS. It can generate vector tiles, statistics, and other outputs from the full OSM history dataset in an efficient distributed manner. The future of OSMesa includes improved validation workflows, machine learning applications, and data science on OSM data.
This document discusses using Linked Data Notifications (LDN) for RDF data streams. It proposes modeling RDF streams as identified Web resources with input and output endpoints. Streams can be discovered and their endpoints retrieved. Data can be sent to input endpoints and retrieved from output endpoints. Queries can be registered against streams to generate output streams. The approach uses existing standards like LDP and aims to provide a simple, generic protocol for decentralized communication between heterogeneous RDF stream processors and consumers.
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
This document provides an overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop was developed based on Google's MapReduce algorithm and how it uses HDFS for scalable storage and MapReduce as an execution engine. Key components of Hadoop architecture include HDFS for fault-tolerant storage across data nodes and the MapReduce programming model for parallel processing of data blocks. The document also gives examples of how MapReduce works and industries that use Hadoop for big data applications.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.
Cardinality Estimation through Histogram in Apache Spark 2.3 with Ron Hu and ...Databricks
Apache Spark 2.2 shipped with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Skewed data distributions are often inherent in many real world applications. In order to deal with skewed distributions effectively, we added equal-height histograms to Apache Spark 2.3. Leveraging reliable statistics and histogram helps Spark make better decisions in picking the most optimal query plan for real world scenarios.
In this talk, we’ll take a deep dive into how Spark’s Cost-Based Optimizer estimates the cardinality and size of each database operator. Specifically, for skewed distribution workload such as TPC-DS, we will show histogram’s impact on query plan change, hence leading to performance gain.
Apache Flink Deep-Dive @ Hadoop Summit 2015 in San Jose, CARobert Metzger
Flink is a unified stream and batch processing framework that natively supports streaming topologies, long-running batch jobs, machine learning algorithms, and graph processing through a pipelined dataflow execution engine. It provides high-level APIs, automatic optimization, efficient memory management, and fault tolerance to execute all of these workloads without needing to treat the system as a black box. Flink achieves native support through its ability to execute everything as data streams, support iterative and stateful computation through caching and managed state, and optimize jobs through cost-based planning and local execution strategies like sort merge join.
SparkR: Enabling Interactive Data Science at Scale on HadoopDataWorks Summit
SparkR enables interactive data science at scale on Hadoop by providing an R interface to Apache Spark. Some key points:
- SparkR allows users to manipulate distributed datasets (RDDs) using familiar R operations like map, filter, reduceByKey.
- It integrates R and Spark by running R code on Spark executors via JNI, allowing R scripts to process large datasets in parallel.
- Examples show how to do tasks like word count and logistic regression on Spark using R code, demonstrating the ability to scale R for data science on big data.
This document introduces R and its integration with SparkR and Spark's MLlib machine learning library. It provides an overview of R and some of its most common data types like vectors, matrices, lists, and data frames. It then discusses how SparkR allows R to leverage Apache Spark's capabilities for large-scale data processing. SparkR exposes Spark's RDD API as distributed lists in R. The document also gives examples of using SparkR for tasks like word counting. It provides an introduction to machine learning concepts like supervised and unsupervised learning, and gives Naive Bayes classification as an example algorithm. Finally, it discusses how MLlib can currently be accessed from R through rJava until full integration with SparkR is completed.
GeoMesa is an open-source project that provides scalable geospatial analytics on large datasets. It allows querying and analyzing data stored in Apache Accumulo using a geospatial index. GeoMesa implements the GeoTools API and supports point, line, polygon, raster, and time-enabled data through flexible space-filling curves. It enables distributed computation and analytics through features like multi-step query planning, secondary indexes, and integration with frameworks like Spark and streaming APIs. The project is developed and supported by a community including LocationTech.
Apache Carbondata: An Indexed Columnar File Format for Interactive Query with...Spark Summit
Realtime analytics over large datasets has become an increasing wide-spread demand, over the past several years, Hadoop ecosystem has been continuously evolving, even complex queries over large datasets can be realized in an interactive fashion with distributed processing framework like Apache Spark, new paradigm of efficient storage were introduced as well to facilitate data processing framework, such as Apache Parquet, ORC provide fast scan over columnar data format, and Apache Hbase offers fast ingest and millisecond scale random access.
In this talk, we will outline Apache Carbondata, a new addition to open source Hadoop ecosystem which is an indexed columnar file format aimed for bridging the gap to fully enable real-time analytics abilities. It has been deeply integrated with Spark SQL and enables dramatic acceleration of query processing by leveraging efficient encoding/compression and effective predicate push down through Carbondata’s multi-level index technique.
Geographica: A Benchmark for Geospatial RDF Stores - ISWC 2013Kostis Kyzirakos
The document introduces Geographica, a benchmark for evaluating geospatial RDF stores. It consists of real-world and synthetic workloads. The real-world workload tests primitive spatial functions and simulates applications like reverse geocoding. The synthetic workload allows varying thematic and spatial selectivity of queries on synthetic geographic feature datasets. The benchmark was used to evaluate the performance of Strabon, Parliament and uSeekM on spatial queries and joins. Results showed differences in performance between systems and opportunities for further optimizing geospatial querying capabilities.
GeoMesa presentation from LocationTech Tour - DC - November, 14th 2013. Presented by Anthony Fox (@algoriffic) of CCRi.
GeoMesa is an open source project providing spatio-temporal indexing, querying, and visualizing capabilities to Accumulo. Learn more at http://geomesa.github.io/
Scalable high-dimensional indexing with HadoopDenis Shestakov
This document discusses scaling image indexing and search using Hadoop on the Grid5000 platform. The approach indexes over 100 million images (30 billion features) using MapReduce. Experiments indexing 1TB and 4TB of images on up to 100 nodes are described. Search quality and throughput for batches up to 12,000 query images are evaluated. Limitations of HDFS block size on scaling and processing over 10TB are discussed along with ideas to improve scalability and handle larger query batches.
Kurator is an open-source workflow platform for data curation tools. It aims to detect and flag data quality issues, repair issues when possible with human curation as needed, and track provenance of automatic and human edits. Kurator uses scientific workflow systems like Kepler to automate computational aspects of curation. It also employs script-based approaches and YesWorkflow annotations to provide workflow views and capture provenance from scripts. This allows leveraging existing tools and programming expertise while providing workflow benefits such as automation, scaling, and provenance tracking.
Materials Project computation and database infrastructureAnubhav Jain
The document describes the Materials Project computation infrastructure, which uses the Atomate framework to automatically run density functional theory simulations on over 85,000 materials in a high-throughput manner, with the results stored in a MongoDB database for users to explore and analyze in order to accelerate materials innovation. The Materials Project infrastructure aims to make it easy for researchers to generate large amounts of computational data on materials properties through standardized and scalable workflows.
The document provides an overview of Heat, OpenStack's orchestration service. It describes Heat's integration with other OpenStack services like Nova, Neutron, Glance, etc. It outlines new features in the Havana release like improved networking support, initial support for a native template language (HOT), and integration with Ceilometer for monitoring. It also describes provider resources which allow users and deployers to define custom resource types and nested stack templates. Finally it lists some planned improvements for the Icehouse release, including further development of the HOT DSL and engine scalability.
Working with OpenStreetMap using Apache Spark and GeotrellisRob Emanuele
The document discusses OpenStreetMap (OSM) and OSMesa, a framework for distributed processing of OSM data. It describes the OSM data model including nodes, ways, relations, and tags. It then discusses a use case where OSM change history needed to be processed at scale to backfill missing maps statistics. OSMesa was developed to handle this using Apache Spark and GeoTrellis on AWS. It can generate vector tiles, statistics, and other outputs from the full OSM history dataset in an efficient distributed manner. The future of OSMesa includes improved validation workflows, machine learning applications, and data science on OSM data.
This document discusses using Linked Data Notifications (LDN) for RDF data streams. It proposes modeling RDF streams as identified Web resources with input and output endpoints. Streams can be discovered and their endpoints retrieved. Data can be sent to input endpoints and retrieved from output endpoints. Queries can be registered against streams to generate output streams. The approach uses existing standards like LDP and aims to provide a simple, generic protocol for decentralized communication between heterogeneous RDF stream processors and consumers.
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
This document provides an overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop was developed based on Google's MapReduce algorithm and how it uses HDFS for scalable storage and MapReduce as an execution engine. Key components of Hadoop architecture include HDFS for fault-tolerant storage across data nodes and the MapReduce programming model for parallel processing of data blocks. The document also gives examples of how MapReduce works and industries that use Hadoop for big data applications.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.
Cardinality Estimation through Histogram in Apache Spark 2.3 with Ron Hu and ...Databricks
Apache Spark 2.2 shipped with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Skewed data distributions are often inherent in many real world applications. In order to deal with skewed distributions effectively, we added equal-height histograms to Apache Spark 2.3. Leveraging reliable statistics and histogram helps Spark make better decisions in picking the most optimal query plan for real world scenarios.
In this talk, we’ll take a deep dive into how Spark’s Cost-Based Optimizer estimates the cardinality and size of each database operator. Specifically, for skewed distribution workload such as TPC-DS, we will show histogram’s impact on query plan change, hence leading to performance gain.
Apache Flink Deep-Dive @ Hadoop Summit 2015 in San Jose, CARobert Metzger
Flink is a unified stream and batch processing framework that natively supports streaming topologies, long-running batch jobs, machine learning algorithms, and graph processing through a pipelined dataflow execution engine. It provides high-level APIs, automatic optimization, efficient memory management, and fault tolerance to execute all of these workloads without needing to treat the system as a black box. Flink achieves native support through its ability to execute everything as data streams, support iterative and stateful computation through caching and managed state, and optimize jobs through cost-based planning and local execution strategies like sort merge join.
SparkR: Enabling Interactive Data Science at Scale on HadoopDataWorks Summit
SparkR enables interactive data science at scale on Hadoop by providing an R interface to Apache Spark. Some key points:
- SparkR allows users to manipulate distributed datasets (RDDs) using familiar R operations like map, filter, reduceByKey.
- It integrates R and Spark by running R code on Spark executors via JNI, allowing R scripts to process large datasets in parallel.
- Examples show how to do tasks like word count and logistic regression on Spark using R code, demonstrating the ability to scale R for data science on big data.
This document introduces R and its integration with SparkR and Spark's MLlib machine learning library. It provides an overview of R and some of its most common data types like vectors, matrices, lists, and data frames. It then discusses how SparkR allows R to leverage Apache Spark's capabilities for large-scale data processing. SparkR exposes Spark's RDD API as distributed lists in R. The document also gives examples of using SparkR for tasks like word counting. It provides an introduction to machine learning concepts like supervised and unsupervised learning, and gives Naive Bayes classification as an example algorithm. Finally, it discusses how MLlib can currently be accessed from R through rJava until full integration with SparkR is completed.
GeoMesa is an open-source project that provides scalable geospatial analytics on large datasets. It allows querying and analyzing data stored in Apache Accumulo using a geospatial index. GeoMesa implements the GeoTools API and supports point, line, polygon, raster, and time-enabled data through flexible space-filling curves. It enables distributed computation and analytics through features like multi-step query planning, secondary indexes, and integration with frameworks like Spark and streaming APIs. The project is developed and supported by a community including LocationTech.
Apache Carbondata: An Indexed Columnar File Format for Interactive Query with...Spark Summit
Realtime analytics over large datasets has become an increasing wide-spread demand, over the past several years, Hadoop ecosystem has been continuously evolving, even complex queries over large datasets can be realized in an interactive fashion with distributed processing framework like Apache Spark, new paradigm of efficient storage were introduced as well to facilitate data processing framework, such as Apache Parquet, ORC provide fast scan over columnar data format, and Apache Hbase offers fast ingest and millisecond scale random access.
In this talk, we will outline Apache Carbondata, a new addition to open source Hadoop ecosystem which is an indexed columnar file format aimed for bridging the gap to fully enable real-time analytics abilities. It has been deeply integrated with Spark SQL and enables dramatic acceleration of query processing by leveraging efficient encoding/compression and effective predicate push down through Carbondata’s multi-level index technique.
Geographica: A Benchmark for Geospatial RDF Stores - ISWC 2013Kostis Kyzirakos
The document introduces Geographica, a benchmark for evaluating geospatial RDF stores. It consists of real-world and synthetic workloads. The real-world workload tests primitive spatial functions and simulates applications like reverse geocoding. The synthetic workload allows varying thematic and spatial selectivity of queries on synthetic geographic feature datasets. The benchmark was used to evaluate the performance of Strabon, Parliament and uSeekM on spatial queries and joins. Results showed differences in performance between systems and opportunities for further optimizing geospatial querying capabilities.
GeoMesa presentation from LocationTech Tour - DC - November, 14th 2013. Presented by Anthony Fox (@algoriffic) of CCRi.
GeoMesa is an open source project providing spatio-temporal indexing, querying, and visualizing capabilities to Accumulo. Learn more at http://geomesa.github.io/
Scalable high-dimensional indexing with HadoopDenis Shestakov
This document discusses scaling image indexing and search using Hadoop on the Grid5000 platform. The approach indexes over 100 million images (30 billion features) using MapReduce. Experiments indexing 1TB and 4TB of images on up to 100 nodes are described. Search quality and throughput for batches up to 12,000 query images are evaluated. Limitations of HDFS block size on scaling and processing over 10TB are discussed along with ideas to improve scalability and handle larger query batches.
Kurator is an open-source workflow platform for data curation tools. It aims to detect and flag data quality issues, repair issues when possible with human curation as needed, and track provenance of automatic and human edits. Kurator uses scientific workflow systems like Kepler to automate computational aspects of curation. It also employs script-based approaches and YesWorkflow annotations to provide workflow views and capture provenance from scripts. This allows leveraging existing tools and programming expertise while providing workflow benefits such as automation, scaling, and provenance tracking.
Materials Project computation and database infrastructureAnubhav Jain
The document describes the Materials Project computation infrastructure, which uses the Atomate framework to automatically run density functional theory simulations on over 85,000 materials in a high-throughput manner, with the results stored in a MongoDB database for users to explore and analyze in order to accelerate materials innovation. The Materials Project infrastructure aims to make it easy for researchers to generate large amounts of computational data on materials properties through standardized and scalable workflows.
This tool solves a real problem in the environmental inventory industry and makes a valuable open data set more accessible.
The NJDEP maintains a statewide wildlife habitat data set that details conservation requirements related to environmental regulations. This is an open data set, but accessibility is limited since working with the one million habitat areas often requires knowledge of GIS software. Using desktop GIS software, a site-specific search is a time-intensive process, taking minutes or hours to run geoprocessing operations for specific properties.
Now, a user can draw a custom area in a browser window and return results in seconds.
Learn about how the project was built in this presentation.
A Lightweight Infrastructure for Graph AnalyticsDonald Nguyen
Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this pa- per, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system.
We demonstrate the capabilities of this infrastructure in three ways. First, we implement more sophisticated algorithms for some of the graph analytics problems tack- led by previous DSLs and show that end-to-end performance can be improved by orders of magnitude even on power-law graphs, thanks to the better algorithms facilitated by a more general programming model. Second, we show that, even when an algorithm can be expressed in existing DSLs, the implementation of that algorithm in the more general system can be orders of magnitude faster when the input graphs are road networks and similar graphs with high diameter, thanks to more sophisticated scheduling. Third, we implement the APIs of three existing graph DSLs on top of the common infrastructure in a few hundred lines of code and show that even for power-law graphs, the performance of the resulting implementations often exceeds that of the original DSL systems, thanks to the lightweight infrastructure.
Supercharging your Apache OODT deployments with the Process Control SystemChris Mattmann
The document discusses the Process Control System (PCS), a component of the Apache OODT framework. PCS provides capabilities for data management, pipeline processing, and resource management. It has been deployed for several NASA Earth science missions to automate processing and manage large volumes of science data. Customizing PCS for a new mission involves configuring servers, specifying product metadata and processing rules, and defining compute resource policies.
PostgreSQL 9.4, 9.5 and Beyond @ COSCUP 2015 TaipeiSatoshi Nagayasu
The document provides an overview of new features in PostgreSQL versions 9.4 and 9.5, including improvements to NoSQL support with JSONB and GIN indexes, analytics functions like aggregation and materialized views, SQL features like UPSERT, security with row level access policies, replication capabilities using logical decoding, and infrastructure to support parallelization. It also outlines the status and changes between versions, and resources for using and learning about PostgreSQL.
Workflow Support for Continuous Data Quality Control in a FilteredPush Network
J. Hanken, D. Lowery, B. Ludäscher, J. Macklin, T. McPhillips, P. Morris, B. Morris, T. Song
Presentation given at TDWG 2014
Jönköping, Sweden
PyTorch is an open-source machine learning library for Python. It is primarily developed by Facebook's AI research group. The document discusses setting up PyTorch, including installing necessary packages and configuring development environments. It also provides examples of core PyTorch concepts like tensors, common datasets, and constructing basic neural networks.
Software tools for calculating materials properties in high-throughput (pymat...Anubhav Jain
This document discusses software tools for automating materials simulations. It introduces pymatgen, atomate, and FireWorks which can be used together to define a workflow of calculations, execute the workflow on supercomputers, and recover from errors or failures. The tools allow researchers to focus on designing and analyzing simulations rather than manual setup and execution of jobs. Workflows in atomate can compute many materials properties including elastic tensors, band structures, and transport coefficients. Parameters are customizable but sensible defaults are provided. FireWorks then executes the workflows across multiple supercomputing clusters.
This document outlines the agenda for a two-day workshop on learning R and analytics. Day 1 will introduce R and cover data input, quality, and exploration. Day 2 will focus on data manipulation, visualization, regression models, and advanced topics. Sessions include lectures and demos in R. The goal is to help attendees learn R in 12 hours and gain an introduction to analytics skills for career opportunities.
Using the Data Cube vocabulary for Publishing Environmental Linked Data on la...Laurent Lefort
Canberra Semantic Web Meetup.
Initiatives have been launched to develop semantic vocabularies representing statistical classifications and discovery metadata. Tools are also being created by statistical organizations to support the publication of dimensional data conforming to the Data Cube specification, now in Last Call at W3C.
The meeting will be an opportunity to hear about two semantic Web and Linked Data initiatives for statistical data that are driven by the Australian Government. The Bureau of Meteorlogy and CSIRO have recently released a Linked Data version of the ACORN-SAT historical climate data at http://lab.environment.data.gov.au and the ABS has released the Census data modelled in the Data Cube vocabulary which is part of a challenge the ABS is organising in context of the SemStats Workshop (http://www.datalift.org/en/event/semstats2013/challenge) at the International Semantic Web Conference (ISWC) in Sydney (http://iswc2013.semanticweb.org).
Come along to hear about these two projects, the challenges encountered and the solutions developed.
The National Digital Stewardship Residency at PBSsquaredsong
The document summarizes the National Digital Stewardship Residency at PBS, which aims to:
1) Develop selection criteria for at-risk media held by PBS to prioritize for digitization.
2) Create a digitization workflow to digitize the selected at-risk media.
3) Make recommendations on digital preservation policies based on challenges faced in preserving over 100,000 tapes in remote storage and migrating legacy data to improve search capabilities.
10-31-13 “Researcher Perspectives of Data Curation” Presentation SlidesDuraSpace
“Hot Topics: The DuraSpace Community Webinar Series, " Series Six: Research Data in Repositories” Curated by David Minor, Research Data Curation Program, UC San Diego Library. Webinar 3: “Researcher Perspectives of Data Curation”
Presented by: David Minor, Research Data Curation Program, UC San Diego Library, Dick Norris, Professor, Scripps Institution of Oceanography & Rick Wagner, Data Scientist, San Diego Supercomputer Center.
This document describes tools for publishing Linked Open Statistical Data (LOSD). It outlines the LOSD publishing pipeline, which includes stages for mapping government datasets to the RDF Data Cube schema, building RDF data cubes from the datasets, and exploring the data cubes. Tools are demonstrated for assisted schema mapping using OpenRefine, building RDF data cubes from CSV files, and exploring data cubes using a web-based explorer with pivot tables, maps, and other visualizations. The tools are designed to help publish open government statistical data according to the RDF Data Cube standard.
Visualising the Australian open data and research data landscapeJonathan Yu
"Visualising the Australian open data and research data landscape" at C3DIS May 2018 in Melbourne. In this talk, we presented work around the visualisation of an survey of open government and research data in Australia. This features a first attempt at formalising a quantitative based approach to measuring the data ecosystem in Australia.
This document provides an overview of a course on Bayesian phylogenetics and the BEAST software package. The course covers introductory topics on Bayesian analysis and BEAST, as well as more advanced analyses including incorporating temporal and trait data. The document outlines the organization and topics to be covered in lectures, including why Bayesian methods are well-suited for pathogen evolution analysis and an introduction to Markov chain Monte Carlo sampling. It also provides information on setting up BEAST analyses using BEAUti, evaluating runs in Tracer, and summarizing runs using LogCombiner and TreeAnnotator.
The document is a presentation on Hadoop and MapReduce frameworks. It begins with an agenda that includes background on Hadoop, its architecture including HDFS and MapReduce, example jobs, Karmasphere Studio tool, and related technologies. It then goes into more detail on topics like motivation for Hadoop due to big data, HDFS and MapReduce frameworks, example jobs like word count and max/sum functions written in MapReduce, and use of Karmasphere Studio for local development and testing of MapReduce jobs.
OSDC 2018 | Lifecycle of a resource. Codifying infrastructure with Terraform ...NETWAYS
Immutable infrastructure is a way to success, but what about the lifecycle of individual resources. This talk is about evolution of resources, code structure, Terraform coding tricks, composition and refactoring.
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptxshubhijain836
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
Mechanics:- Simple and Compound PendulumPravinHudge1
a compound pendulum is a physical system with a more complex structure than a simple pendulum, incorporating its mass distribution and dimensions into its oscillatory motion around a fixed axis. Understanding its dynamics involves principles of rotational mechanics and the interplay between gravitational potential energy and kinetic energy. Compound pendulums are used in various scientific and engineering applications, such as seismology for measuring earthquakes, in clocks to maintain accurate timekeeping, and in mechanical systems to study oscillatory motion dynamics.
Evaluation and Identification of J'BaFofi the Giant Spider of Congo and Moke...MrSproy
ABSTRACT
The J'BaFofi, or "Giant Spider," is a mainly legendary arachnid by reportedly inhabiting the dense rain forests of
the Congo. As despite numerous anecdotal accounts and cultural references, the scientific validation remains more elusive.
My study aims to proper evaluate the existence of the J'BaFofi through the analysis of historical reports,indigenous
testimonies and modern exploration efforts.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Presentation of our paper, "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection", by K. Tsigos, E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris. Presented at the ACM Int. Workshop on Multimedia AI against Disinformation (MAD’24) of the ACM Int. Conf. on Multimedia Retrieval (ICMR’24), Thailand, June 2024. https://doi.org/10.1145/3643491.3660292 https://arxiv.org/abs/2404.18649
Software available at https://github.com/IDT-ITI/XAI-Deepfakes
Dr. Firoozeh Kashani-Sabet is an innovator in Middle Eastern Studies and approaches her work, particularly focused on Iran, with a depth and commitment that has resulted in multiple book publications. She is notable for her work with the University of Pennsylvania, where she serves as the Walter H. Annenberg Professor of History.
Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...Creative-Biolabs
Neutralizing antibodies, pivotal in immune defense, specifically bind and inhibit viral pathogens, thereby playing a crucial role in protecting against and mitigating infectious diseases. In this slide, we will introduce what antibodies and neutralizing antibodies are, the production and regulation of neutralizing antibodies, their mechanisms of action, classification and applications, as well as the challenges they face.
2. Motivations
• Regroup all analytical methods within a single
environment
• Ease analysis steps
• Share analytical methods within the paleofire
community
• Promote GCD usage and associated analyses for
ecologists, modellers, etc.
• R is free, the paleofire package is under GNU GPL3
3. Some stats and dates
• Proof of concept elaborated during the GCD meeting in Salt
Lake city in May 2013
• 7 versions: currently 1.1.6 (since 8 Jan. 2014)
• 21 functions
• 736 charcoal series in the GCD package (v3)
• 48 pages of help
• 1 tutorial and manuscript in Computers and Geosciences
(Nov. 2014)
4. Number of downloads from
10/2014 to 09/2015
Number of downloads per day from the Rstudio mirror
Total: 5138
6. paleofire main
functionalities
• Charcoal series (or sites) selection
• Transformation of charcoal data
• pfTransform (e.g. Power et al. 2008)
• Compositing i.e. construction of temporal trends
• pfCompositeLF (e.g. Daniau et al. 2012)
• Mapping: gridding and spatio temporal interpolation
• pfDotMap, pfGridding, pfSimpleGrid
7. paleofire main
functionalities
• Tests
• pfKruskall
• Miscellaneous
• pfToKml (export sites to Google
Earth)
• pfPublication (extract
publication data)
• potveg (extract biome information)
• etc..
8. Better than words: some
examples
# Install and load paleofire
install.packages("paleofire")
library(paleofire) # Load the package
# Select all sites and plot them:
all_sites <- pfSiteSel()
plot(all_sites)
10. Ex: Select sites in eastern
North America
# Sites in Eastern North America
NA <- pfSiteSel(lat>30,long<(-50),long>-170)
# Retrieve the potential vegetations of those sites using
the classification of Levavasseur et al. (2012)
NA_veg <- potveg(NA,classif="l12")
plot(NA_veg)
11. Ex: Select sites in eastern North
America
+ and in the boreal forest
12. Ex: Select sites in eastern North
America
+ and in the boreal forest
+ and add one unpublished site
# Create a vector with location of files
loc=c(“path/site1.csv”,”path/site2.csv”)
# Create an object
mysites=pfAddData(files=loc, type=“CharAnalysis”)
13. Transform charcoal series
and produce a composite curve
# Because of taphonomy, units, methods, etc.
# series needs to be homogenized:
BNA_trans <- pfTransform(BNA, add=mysites,
method=c("MinMax", “Box-Cox" ,"Z-Score"))
# Look at Power et al. (2008) for details
# Compositing:
BNA_comp <- pfCompositeLF(BNA_trans,
tarAge=seq(-50,11700,20),
hw=250,nboot=1000)
plot(BNA_comp)
14. Transform charcoal series
and produce a composite curve
# Compositing:
BNA_comp <- pfCompositeLF(BNA_trans,
tarAge=seq(-50,11700,20),
hw=250,nboot=1000)
16. Ex: Map charcoal anomalies
at 6 ka BP in Europe
ID <- pfSiteSel(id_region==‘EURO’)
TR <- pfTransform(ID,method=c("MinMax", “Box-Cox" ,"Z-
Score"))
# Spatio temporal interpolation using a tricube weight
function
Grd1<-pfGridding(TR, age=6000,
cell_size=200000,time_buffer=500, distance_buffer=300000)
plot(grd1)
18. Ex: Map charcoal anomalies
at 6 ka BP in Europe
# Same procedure but using lat-long WGS84 coordinates (5° grid
here); to do this first update paleofire using the GitHub
repos:
install.packages(‘devtools’)
library(devtools)
install_github(‘paleofire/paleofire’)
p=pfGridding(TR,cell_size=5,time_buffer =50,distance_buffer =
300000, age=6000,proj4='+proj=longlat +ellps=WGS84
+datum=WGS84 +no_defs’)
plot(p)
# Save the result as a netcdf file
writeRaster(p$raster,file="path/filename.nc",format='CDF')
19. Go further…
• Next version will link to the http://paleofire.org
website and online GCD
• Github: http://github.com/paleofire
• CRAN http://cran.r-
project.org/web/packages/paleofire/