LarKC Tutorial at ISWC 2009 - Data Model
•Download as PPT, PDF•
0 likes•338 views
The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced “lark”), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. The LarKC platform is available at larkc.sourceforge.net. This talk, is part of a tutorial for early users of the LarKC platform, and describes the data model used within LarKC.
Report
Share
Report
Share
![Objectives ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
SparkR: Enabling Interactive Data Science at Scale
The document discusses SparkR, which enables interactive data science using R on Apache Spark clusters. SparkR allows users to create and manipulate resilient distributed datasets (RDDs) from R and run R analytics functions in parallel on large datasets. It provides examples of using SparkR for tasks like word counting on text data and digit classification using the MNIST dataset. The API is designed to be similar to PySpark for ease of use.
Introduction to SparkR
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.
Introduction to Spark R with R studio - Mr. Pragith
R is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical
software and data analysis.
RStudio IDE is a powerful and productive user interface for R.
It’s free and open source, and available on Windows, Mac, and Linux.
SparkR: Enabling Interactive Data Science at Scale on Hadoop
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.
SparkR: The Past, the Present and the Future-(Shivaram Venkataraman and Rui S...
SparkR allows users to perform large-scale data analysis from R. It provides a unified approach for working with big data using DataFrames. The current release of SparkR includes DataFrame support released in Spark 1.4 for improved performance. Future developments will expand SparkR's capabilities for large-scale machine learning, simple parallel programming APIs, and tighter integration with existing R packages. The open source project has an growing community of over 20 contributors working to advance SparkR.
Connecting Stream Reasoners on the Web
This document summarizes Jean-Paul Calbimonte's presentation on connecting stream reasoners on the web. It discusses representing data streams as RDF and using RDF stream processing systems. Key points include:
- RDF streams can be represented as sequences of timestamped RDF graphs.
- The W3C RSP community group is working to standardize RDF stream models and query languages.
- Producing RDF streams involves mapping live data sources to RDF and adding timestamps.
- Consuming RDF streams involves discovering stream metadata and endpoints to access the streams.
- Systems like TripleWave demonstrate approaches for spreading RDF streams on the web.
Triplewave: a step towards RDF Stream Processing on the Web
The slides of my talk at INSIGHT Centre for Data Analytics (in NUI Galway) where I presented TripleWave (http://streamreasoning.github.io/TripleWave/), an open-source framework to create and publish streams of RDF data.
A Data Frame Abstraction Layer for SparkR-(Chris Freeman, Alteryx)
SparkR provides an R interface to Apache Spark that allows R developers to leverage Spark's distributed processing capabilities through DataFrames. DataFrames impose a schema on data from RDDs, making the data easier to access and manipulate compared to raw RDDs. DataFrames in SparkR allow R-like syntax and interactions with data while leveraging Spark's optimizations by passing operations to the JVM. The speaker demonstrated SparkR DataFrame features and discussed the project's roadmap, including expanding SparkR to support Spark machine learning capabilities.
Recommended
SparkR: Enabling Interactive Data Science at Scale
The document discusses SparkR, which enables interactive data science using R on Apache Spark clusters. SparkR allows users to create and manipulate resilient distributed datasets (RDDs) from R and run R analytics functions in parallel on large datasets. It provides examples of using SparkR for tasks like word counting on text data and digit classification using the MNIST dataset. The API is designed to be similar to PySpark for ease of use.
Introduction to SparkR
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.
Introduction to Spark R with R studio - Mr. Pragith
R is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical
software and data analysis.
RStudio IDE is a powerful and productive user interface for R.
It’s free and open source, and available on Windows, Mac, and Linux.
SparkR: Enabling Interactive Data Science at Scale on Hadoop
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.
SparkR: The Past, the Present and the Future-(Shivaram Venkataraman and Rui S...
SparkR allows users to perform large-scale data analysis from R. It provides a unified approach for working with big data using DataFrames. The current release of SparkR includes DataFrame support released in Spark 1.4 for improved performance. Future developments will expand SparkR's capabilities for large-scale machine learning, simple parallel programming APIs, and tighter integration with existing R packages. The open source project has an growing community of over 20 contributors working to advance SparkR.
Connecting Stream Reasoners on the Web
This document summarizes Jean-Paul Calbimonte's presentation on connecting stream reasoners on the web. It discusses representing data streams as RDF and using RDF stream processing systems. Key points include:
- RDF streams can be represented as sequences of timestamped RDF graphs.
- The W3C RSP community group is working to standardize RDF stream models and query languages.
- Producing RDF streams involves mapping live data sources to RDF and adding timestamps.
- Consuming RDF streams involves discovering stream metadata and endpoints to access the streams.
- Systems like TripleWave demonstrate approaches for spreading RDF streams on the web.
Triplewave: a step towards RDF Stream Processing on the Web
The slides of my talk at INSIGHT Centre for Data Analytics (in NUI Galway) where I presented TripleWave (http://streamreasoning.github.io/TripleWave/), an open-source framework to create and publish streams of RDF data.
A Data Frame Abstraction Layer for SparkR-(Chris Freeman, Alteryx)
SparkR provides an R interface to Apache Spark that allows R developers to leverage Spark's distributed processing capabilities through DataFrames. DataFrames impose a schema on data from RDDs, making the data easier to access and manipulate compared to raw RDDs. DataFrames in SparkR allow R-like syntax and interactions with data while leveraging Spark's optimizations by passing operations to the JVM. The speaker demonstrated SparkR DataFrame features and discussed the project's roadmap, including expanding SparkR to support Spark machine learning capabilities.
Query Rewriting in RDF Stream Processing
This document discusses query rewriting in RDF stream processing. It presents StreamQR, a system that incorporates query rewriting techniques with an RDF stream processor (RSP) to answer queries over ontologies in streams. StreamQR rewrites queries using an ontology and registers the rewritten queries with an RSP. It achieves throughput comparable to no rewriting even for queries with many rewritings. StreamQR performance is evaluated under different workloads and compared to an approach using incremental reasoning. Query rewriting allows efficient query answering over ontologies in RSPs.
Adding Complex Data to Spark Stack by Tug Grall
This document discusses adding complex data to the Spark stack using Apache Drill. It provides an overview of Drill, how to integrate it with Spark, the current status and next steps. Drill allows SQL-based querying of structured, semi-structured and unstructured data across various data sources. It can be used as an input to Spark jobs and to query Spark RDDs. The integration provides benefits like flexibility, rich storage support and efficient distributed processing while combining Drill and Spark's capabilities.
final_copy_camera_ready_paper (7)
This document proposes a hypergraph-based approach for optimizing SPARQL queries on RDF data. It first transforms an RDF graph into a hypergraph by grouping subjects and objects connected by the same predicate under a hyperedge for that predicate. It then rearranges the patterns in a SPARQL query based on the size of corresponding hyperedges to build a query path for efficient processing. The query is executed by looping through the rearranged patterns and extracting required subjects and objects from the hypergraph representation to find matching triples. Experimental results show this approach performs better than existing systems like RDF-3x, Jena and AllegroGraph.
RSP-QL*: Querying Data-Level Annotations in RDF Streams
This document proposes an extension to RSP-QL called RSP-QL* that allows querying of statement-level annotations in RDF streams. RSP-QL* uses the RDF* model, which allows embedding RDF triples as the subject or object of other triples. This provides an efficient way to represent statement-level metadata in RDF. The semantics of RSP-QL are extended to support RSP-QL* patterns, which can include basic graph patterns, named graphs, windows and other operators. Future work includes adding more functionality to the RDF* model, prototyping an implementation, and evaluating performance.
First impressions of SparkR: our own machine learning algorithm
In june 2015, SparkR was first integrated into SparkR. At InfoFarm we strive to stay on top of new technologies, hence we have tried it out and implemented a few machine learning algorithms as well.
Dgraph: Graph database for production environment
Presented at the First openCypher Implementers Meeting in Walldorf, Germany, February 2017 @ http://www.opencypher.org/blog/2017/03/31/first-ocim-blog/
TripleWave: Spreading RDF Streams on the Web
TripleWave is an open-source framework for creating and publishing RDF streams over the Web. It converts various data sources like temporal RDF datasets and web streams into RDF streams. TripleWave makes these streams available via standard protocols and allows consuming applications to access the streams through pull via Linked Data principles or push using RSP services. The framework is implemented in NodeJS and available on GitHub to help spread the use of RDF streams on the semantic web.
Scalable Data Science in Python and R on Apache Spark
In the world of Data Science, Python and R are very popular. Apache Spark is a highly scalable data platform. How could a Data Scientist integrate Spark into their existing Data Science toolset? How does Python work with Spark? How could one leverage the rich 10000+ packages on CRAN for R?
We will start with PySpark, beginning with a quick walkthrough of data preparation practices and an introduction to Spark MLLib Pipeline Model. We will also discuss how to integrate native Python packages with Spark.
Compare to PySpark, SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages.
Python
R
Apache Spark
ML
DL
How to convert point cloud data into LAS or LAZ (including a python approach)
A helpful presentation for everyone wondering on how to convert their point cloud data into LAS or LAZ. It provides step by step instructions on the conversion of benchmark data sets with python.
Find more helpful articles on how to work the right way with point clouds on Pointly's blog on our website: www.pointly.ai
RDF Stream Processing: Let's React
The document discusses requirements and approaches for RDF stream processing (RSP). It covers the following key points in 3 sentences:
RSP aims to process continuous RDF streams to address scenarios like sensor data and social media. It involves querying streaming data, integrating streams with static data, and handling issues like imperfections. The document reviews existing RSP systems and languages, actor-based approaches, and the 8 requirements for real-time stream processing including keeping data moving, generating predictable outcomes, and responding instantaneously.
RDF Stream Processing Tutorial: RSP implementations
This document provides an overview of RDF stream processing and existing RDF stream processing engines. It discusses RDF streams and how sensor data can be represented as RDF streams. It also summarizes some existing RDF stream processing query languages and systems, including C-SPARQL, and the features they support like continuous execution, operators, and time-based windows. The document is intended as a tutorial for developers on working with RDF stream processing.
Performant data processing with PySpark, SparkR and DataFrame API
This document discusses using PySpark, SparkR and DataFrame APIs to perform efficient data processing with Apache Spark. It explains that while Python and R can be used with Spark, performance may be slower than Java and Scala since data needs to be transferred between the JVM and the non-JVM language runtime. DataFrame APIs allow working with data within the JVM, avoiding this overhead and providing near-native performance when using Python, R or other non-JVM languages. Examples demonstrate how to use DataFrames and SQL with filters to optimize performance before using user-defined functions that require data transfer. Ingesting data in a DataFrame-native format like Parquet is also recommended for efficiency.
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can
offer a richer representation by suggesting the potential group
structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a
parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of
single-linkage clustering algorithm due to its natural expression
of iterative process. Our algorithm can be deployed easily in
Amazon’s cloud environment. And a thorough performance
evaluation in Amazon’s EC2 verifies that the scalability of our
algorithm sustains when the datasets scale up.
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
Keynote provided on the Ordring 2013 workshop about the need for a W3C community group on RDF Stream Processing
A brief introduction to scalaz-stream
Scalaz-stream is a library for stream processing and incremental I/O in Scala that is resource-safe and composable. It defines a Process trait that represents stream transformations as pure values in a state machine with three possible states - Emit, Await, and Halt. Process transformations can be modified and composed using combinators like map, flatMap, filter, and pipe to feed the output of one process into the input of another. An example converts a file of Fahrenheit temperatures to Celsius using scalaz-stream to read lines, filter and map values, and write the output to a new file.
Linked Data Notifications for RDF Streams
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.
Introduction into scalable graph analysis with Apache Giraph and Spark GraphX
Graph relationships are everywhere. In fact, more often than not, analyzing relationships between points in your datasets lets you extract more business value from your data.
Consider social graphs, or relationships of customers to each other and products they purchase, as two of the most common examples. Now, if you think you have a scalability issue just analyzing points in your datasets, imagine what would happen if you wanted to start analyzing the arbitrary relationships between those data points: the amount of potential processing will increase dramatically, and the kind of algorithms you would typically want to run would change as well.
If your Hadoop batch-oriented approach with MapReduce works reasonably well, for scalable graph processing you have to embrace an in-memory, explorative, and iterative approach. One of the best ways to tame this complexity is known as the Bulk synchronous parallel approach. Its two most widely used implementations are available as Hadoop ecosystem projects: Apache Giraph (used at Facebook), and Apache GraphX (as part of a Spark project).
In this talk we will focus on practical advice on how to get up and running with Apache Giraph and GraphX; start analyzing simple datasets with built-in algorithms; and finally how to implement your own graph processing applications using the APIs provided by the projects. We will finally compare and contrast the two, and try to lay out some principles of when to use one vs. the other.
The Parquet Format and Performance Optimization Opportunities
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
End-to-end Data Pipeline with Apache Spark
This document discusses Apache Spark, a fast and general cluster computing system. It summarizes Spark's capabilities for machine learning workflows, including feature preparation, model training, evaluation, and production use. It also outlines new high-level APIs for data science in Spark, including DataFrames, machine learning pipelines, and an R interface, with the goal of making Spark more similar to single-machine libraries like SciKit-Learn. These new APIs are designed to make Spark easier to use for machine learning and interactive data analysis.
Using SparkR to Scale Data Science Applications in Production. Lessons from t...
R is a hugely popular platform for Data Scientists to create analytic models in many different domains. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform. With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
• Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
Challenging LarKC with Urban Computing
The document discusses the challenges of urban computing and how LarKC can help address them. It provides examples of how LarKC can cope with heterogeneous, large-scale, time-dependent, noisy and uncertain data that is common in urban computing applications. Specifically, it discusses a use case about planning travel from Varese to Milan and the different types of data involved, such as transportation schedules, traffic, and events. It outlines requirements for LarKC to handle issues like representing different data formats, reasoning with approximations, and accounting for changing and inconsistent information over time.
LarKC Tutorial at ISWC 2009 - Second Hands-on Scenario
The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced “lark”), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. The LarKC platform is available at larkc.sourceforge.net. This is the first of two hand-ons that introduce participants to working with directly LarKC code.
More Related Content
What's hot
Query Rewriting in RDF Stream Processing
This document discusses query rewriting in RDF stream processing. It presents StreamQR, a system that incorporates query rewriting techniques with an RDF stream processor (RSP) to answer queries over ontologies in streams. StreamQR rewrites queries using an ontology and registers the rewritten queries with an RSP. It achieves throughput comparable to no rewriting even for queries with many rewritings. StreamQR performance is evaluated under different workloads and compared to an approach using incremental reasoning. Query rewriting allows efficient query answering over ontologies in RSPs.
Adding Complex Data to Spark Stack by Tug Grall
This document discusses adding complex data to the Spark stack using Apache Drill. It provides an overview of Drill, how to integrate it with Spark, the current status and next steps. Drill allows SQL-based querying of structured, semi-structured and unstructured data across various data sources. It can be used as an input to Spark jobs and to query Spark RDDs. The integration provides benefits like flexibility, rich storage support and efficient distributed processing while combining Drill and Spark's capabilities.
final_copy_camera_ready_paper (7)
This document proposes a hypergraph-based approach for optimizing SPARQL queries on RDF data. It first transforms an RDF graph into a hypergraph by grouping subjects and objects connected by the same predicate under a hyperedge for that predicate. It then rearranges the patterns in a SPARQL query based on the size of corresponding hyperedges to build a query path for efficient processing. The query is executed by looping through the rearranged patterns and extracting required subjects and objects from the hypergraph representation to find matching triples. Experimental results show this approach performs better than existing systems like RDF-3x, Jena and AllegroGraph.
RSP-QL*: Querying Data-Level Annotations in RDF Streams
This document proposes an extension to RSP-QL called RSP-QL* that allows querying of statement-level annotations in RDF streams. RSP-QL* uses the RDF* model, which allows embedding RDF triples as the subject or object of other triples. This provides an efficient way to represent statement-level metadata in RDF. The semantics of RSP-QL are extended to support RSP-QL* patterns, which can include basic graph patterns, named graphs, windows and other operators. Future work includes adding more functionality to the RDF* model, prototyping an implementation, and evaluating performance.
First impressions of SparkR: our own machine learning algorithm
In june 2015, SparkR was first integrated into SparkR. At InfoFarm we strive to stay on top of new technologies, hence we have tried it out and implemented a few machine learning algorithms as well.
Dgraph: Graph database for production environment
Presented at the First openCypher Implementers Meeting in Walldorf, Germany, February 2017 @ http://www.opencypher.org/blog/2017/03/31/first-ocim-blog/
TripleWave: Spreading RDF Streams on the Web
TripleWave is an open-source framework for creating and publishing RDF streams over the Web. It converts various data sources like temporal RDF datasets and web streams into RDF streams. TripleWave makes these streams available via standard protocols and allows consuming applications to access the streams through pull via Linked Data principles or push using RSP services. The framework is implemented in NodeJS and available on GitHub to help spread the use of RDF streams on the semantic web.
Scalable Data Science in Python and R on Apache Spark
In the world of Data Science, Python and R are very popular. Apache Spark is a highly scalable data platform. How could a Data Scientist integrate Spark into their existing Data Science toolset? How does Python work with Spark? How could one leverage the rich 10000+ packages on CRAN for R?
We will start with PySpark, beginning with a quick walkthrough of data preparation practices and an introduction to Spark MLLib Pipeline Model. We will also discuss how to integrate native Python packages with Spark.
Compare to PySpark, SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages.
Python
R
Apache Spark
ML
DL
How to convert point cloud data into LAS or LAZ (including a python approach)
A helpful presentation for everyone wondering on how to convert their point cloud data into LAS or LAZ. It provides step by step instructions on the conversion of benchmark data sets with python.
Find more helpful articles on how to work the right way with point clouds on Pointly's blog on our website: www.pointly.ai
RDF Stream Processing: Let's React
The document discusses requirements and approaches for RDF stream processing (RSP). It covers the following key points in 3 sentences:
RSP aims to process continuous RDF streams to address scenarios like sensor data and social media. It involves querying streaming data, integrating streams with static data, and handling issues like imperfections. The document reviews existing RSP systems and languages, actor-based approaches, and the 8 requirements for real-time stream processing including keeping data moving, generating predictable outcomes, and responding instantaneously.
RDF Stream Processing Tutorial: RSP implementations
This document provides an overview of RDF stream processing and existing RDF stream processing engines. It discusses RDF streams and how sensor data can be represented as RDF streams. It also summarizes some existing RDF stream processing query languages and systems, including C-SPARQL, and the features they support like continuous execution, operators, and time-based windows. The document is intended as a tutorial for developers on working with RDF stream processing.
Performant data processing with PySpark, SparkR and DataFrame API
This document discusses using PySpark, SparkR and DataFrame APIs to perform efficient data processing with Apache Spark. It explains that while Python and R can be used with Spark, performance may be slower than Java and Scala since data needs to be transferred between the JVM and the non-JVM language runtime. DataFrame APIs allow working with data within the JVM, avoiding this overhead and providing near-native performance when using Python, R or other non-JVM languages. Examples demonstrate how to use DataFrames and SQL with filters to optimize performance before using user-defined functions that require data transfer. Ingesting data in a DataFrame-native format like Parquet is also recommended for efficiency.
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can
offer a richer representation by suggesting the potential group
structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a
parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of
single-linkage clustering algorithm due to its natural expression
of iterative process. Our algorithm can be deployed easily in
Amazon’s cloud environment. And a thorough performance
evaluation in Amazon’s EC2 verifies that the scalability of our
algorithm sustains when the datasets scale up.
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
Keynote provided on the Ordring 2013 workshop about the need for a W3C community group on RDF Stream Processing
A brief introduction to scalaz-stream
Scalaz-stream is a library for stream processing and incremental I/O in Scala that is resource-safe and composable. It defines a Process trait that represents stream transformations as pure values in a state machine with three possible states - Emit, Await, and Halt. Process transformations can be modified and composed using combinators like map, flatMap, filter, and pipe to feed the output of one process into the input of another. An example converts a file of Fahrenheit temperatures to Celsius using scalaz-stream to read lines, filter and map values, and write the output to a new file.
Linked Data Notifications for RDF Streams
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.
Introduction into scalable graph analysis with Apache Giraph and Spark GraphX
Graph relationships are everywhere. In fact, more often than not, analyzing relationships between points in your datasets lets you extract more business value from your data.
Consider social graphs, or relationships of customers to each other and products they purchase, as two of the most common examples. Now, if you think you have a scalability issue just analyzing points in your datasets, imagine what would happen if you wanted to start analyzing the arbitrary relationships between those data points: the amount of potential processing will increase dramatically, and the kind of algorithms you would typically want to run would change as well.
If your Hadoop batch-oriented approach with MapReduce works reasonably well, for scalable graph processing you have to embrace an in-memory, explorative, and iterative approach. One of the best ways to tame this complexity is known as the Bulk synchronous parallel approach. Its two most widely used implementations are available as Hadoop ecosystem projects: Apache Giraph (used at Facebook), and Apache GraphX (as part of a Spark project).
In this talk we will focus on practical advice on how to get up and running with Apache Giraph and GraphX; start analyzing simple datasets with built-in algorithms; and finally how to implement your own graph processing applications using the APIs provided by the projects. We will finally compare and contrast the two, and try to lay out some principles of when to use one vs. the other.
The Parquet Format and Performance Optimization Opportunities
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
End-to-end Data Pipeline with Apache Spark
This document discusses Apache Spark, a fast and general cluster computing system. It summarizes Spark's capabilities for machine learning workflows, including feature preparation, model training, evaluation, and production use. It also outlines new high-level APIs for data science in Spark, including DataFrames, machine learning pipelines, and an R interface, with the goal of making Spark more similar to single-machine libraries like SciKit-Learn. These new APIs are designed to make Spark easier to use for machine learning and interactive data analysis.
Using SparkR to Scale Data Science Applications in Production. Lessons from t...
R is a hugely popular platform for Data Scientists to create analytic models in many different domains. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform. With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
• Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
What's hot (20)
RSP-QL*: Querying Data-Level Annotations in RDF Streams
RSP-QL*: Querying Data-Level Annotations in RDF Streams
First impressions of SparkR: our own machine learning algorithm
First impressions of SparkR: our own machine learning algorithm
Scalable Data Science in Python and R on Apache Spark
Scalable Data Science in Python and R on Apache Spark
How to convert point cloud data into LAS or LAZ (including a python approach)
How to convert point cloud data into LAS or LAZ (including a python approach)
RDF Stream Processing Tutorial: RSP implementations
RDF Stream Processing Tutorial: RSP implementations
Performant data processing with PySpark, SparkR and DataFrame API
Performant data processing with PySpark, SparkR and DataFrame API
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East t...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
Introduction into scalable graph analysis with Apache Giraph and Spark GraphX
Introduction into scalable graph analysis with Apache Giraph and Spark GraphX
The Parquet Format and Performance Optimization Opportunities
The Parquet Format and Performance Optimization Opportunities
Using SparkR to Scale Data Science Applications in Production. Lessons from t...
Using SparkR to Scale Data Science Applications in Production. Lessons from t...
Viewers also liked
Challenging LarKC with Urban Computing
The document discusses the challenges of urban computing and how LarKC can help address them. It provides examples of how LarKC can cope with heterogeneous, large-scale, time-dependent, noisy and uncertain data that is common in urban computing applications. Specifically, it discusses a use case about planning travel from Varese to Milan and the different types of data involved, such as transportation schedules, traffic, and events. It outlines requirements for LarKC to handle issues like representing different data formats, reasoning with approximations, and accounting for changing and inconsistent information over time.
LarKC Tutorial at ISWC 2009 - Second Hands-on Scenario
The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced “lark”), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. The LarKC platform is available at larkc.sourceforge.net. This is the first of two hand-ons that introduce participants to working with directly LarKC code.
LarKC: the large knowledge collider
Brief presentation of vision and mission of the LarKC project, building the Large Knowledge Collider http://www.larkc.eu
Web Scale Reasoning and the LarKC Project
The LarKC project aims to build an integrated pluggable platform for large-scale reasoning. It supports parallelization, distribution, and remote execution. The LarKC platform provides a lightweight core that gives standardized interfaces for combining plug-in components, while the real work is done in the plug-ins. There are three types of LarKC users: those building plug-ins, configuring workflows, and using workflows.
Iswc 2009 LarKC Tutorial: Architecture
The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced “lark”), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. The LarKC platform is available at larkc.sourceforge.net. This talk, is part of a tutorial for early users of the LarKC platform, and describes the platform architecture.
Urban Computing in LarKC
Joint Work with Emanuele Della Valle
emanuele.dellavalle@polimi.it.
Presentation of the results of the Urban Computing use case of the LarKC project. Speech at the ITN Expo event (http://www.itnexpo.it/) on October 16th, 2009.
LarKC Tutorial at ISWC 2009 - Urban Computing
The document discusses understanding and manipulating urban computing workflows. It describes three workflows used in the Alpha Urban LarKC system: 1) a monument selection workflow, 2) an event selection workflow, and 3) a path finding workflow. Each workflow utilizes various LarKC plugins to integrate, transform, and reason over distributed data sources for responding to user queries about points of interest in a city.
Viewers also liked (7)
LarKC Tutorial at ISWC 2009 - Second Hands-on Scenario
LarKC Tutorial at ISWC 2009 - Second Hands-on Scenario
Similar to LarKC Tutorial at ISWC 2009 - Data Model
Spark and scala course content | Spark and scala course online training
The document outlines the course contents for a Spark & Scala course. It will cover topics such as describing Spark components like the driver program, worker, and RDDs. It will also cover loading and processing data with Spark, Spark SQL, building Spark applications, pair RDD operations, data frames, Spark streaming, GraphX, and MLlib. The course will also introduce Scala concepts such as expressions, functions, classes, pattern matching, generics, and concurrency.
Graph databases & data integration v2
A presentation on Graph Databases and how RDF particularly is a very good fit for integrating diverse data.
presented at VoxxedDays Athens
Sparql service-description
This document introduces SPARQL, the SPARQL query language used to retrieve and manipulate RDF data. It provides an example SPARQL query to return full names from a sample RDF graph. It then describes what a SPARQL Service Description is, which is a vocabulary for discovering and describing SPARQL services and endpoints. It outlines several properties and classes used in SPARQL Service Descriptions.
Apache spark - Architecture , Overview & libraries
This document provides an overview of Apache Spark, an open-source unified analytics engine for large-scale data processing. It discusses Spark's core APIs including RDDs and transformations/actions. It also covers Spark SQL, Spark Streaming, MLlib, and GraphX. Spark provides a fast and general engine for big data processing, with explicit operations for streaming, SQL, machine learning, and graph processing. The document includes installation instructions and examples of using various Spark components.
Coral & Transport UDFs: Building Blocks of a Postmodern Data Warehouse
These slides describe LinkedIn efforts in building a view virtualization layer that enables compute engines to access Hive views, reason about them semantically, and optionally rewrite them before presenting to various compute engines such as Spark, Hive, and Presto. Namely, we describe two frameworks: Coral for reasoning about views using their logical plans, and Transport UDFs for reasoning about UDFs.
Rdf And Rdf Schema For Ontology Specification
The document discusses RDF (Resource Description Framework) and RDF Schema for ontology specification on the Semantic Web. It provides an introduction to RDF and how it uses URIs to identify resources and assertions. It then discusses RDF applications for mobile terminals, RDF graph models, RDF/XML syntax, RDF vocabularies and schemas, and the RDF Schema language. It concludes with an overview of how OWL (Web Ontology Language) and OWL-S (Web Service Ontology) build upon RDF Schema to facilitate ontology specification and automation of web services.
A Hands On Overview Of The Semantic Web
The document provides an overview of the Semantic Web and introduces key concepts such as RDF, RDFS, SPARQL, OWL, and Linked Open Data. It begins with defining what the Semantic Web is, why it is useful, and how it differs from the traditional web by linking data rather than documents. It then covers RDF for representing data, RDFS for defining schemas, and SPARQL for querying RDF data. The document also discusses OWL for building ontologies and Linked Open Data initiatives that have published billions of RDF triples on the web.
Better {ML} Together: GraphLab Create + Spark
This document discusses using GraphLab Create and Apache Spark together for machine learning applications. It provides an overview of Spark and how to create resilient distributed datasets (RDDs) and perform parallel operations on clusters. It then lists many machine learning algorithms available in GraphLab Create, including recommender systems, classification, regression, text analysis, image analysis, and graph analytics. The document proposes using notebooks to build data science products that help deliver personalized experiences through ML and intelligent automation. It demonstrates clustering customer transactions from an expense reporting dataset to identify customer behavior patterns.
A hands on overview of the semantic web
This document provides an overview of the Semantic Web. It defines the Semantic Web as linking data to data using technologies like RDF, RDFS, OWL and SPARQL. It explains that RDF represents information as subject-predicate-object statements that can be queried using SPARQL. RDFS allows defining schemas and classes for RDF data, while OWL adds more expressiveness for defining complex ontologies. The document outlines popular Semantic Web tools, public ontologies, and companies working in this domain. It positions the Semantic Web as a way to represent and share data universally on the web.
AnzoGraph DB - SPARQL 101
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, provides a history lesson on the origins of SPARQL, including its roots in the Semantic Web, and how linked open data is used to create Knowledge Graphs. Then, he dives into "What is RDF?", "What is a URI?" and "What is SPARQL?", wrapping up with a real-world demonstration via a Zeppelin notebook.
Triplestore and SPARQL
This document provides an overview of the Semantic Web, RDF, SPARQL, and triplestores. It discusses how RDF structures and links data using subject-predicate-object triples. SPARQL is introduced as a standard query language for retrieving and manipulating data stored in RDF format. Popular triplestore implementations like Apache Jena and applications of linked data like DBPedia are also summarized.
RDF and Java
This document summarizes three popular Java frameworks for working with RDF and SPARQL: Jena, Sesame, and JRDF. It describes how each framework represents RDF data using a graph model with subjects, predicates, and objects. It also discusses how each framework supports querying RDF data using SPARQL or alternative query languages, and persisting RDF graphs to databases.
SPARQL and SQL: technical aspects and synergy
This document discusses the technical aspects and synergy between SPARQL and SQL. SPARQL is designed to query collections of RDF triples and easily traverse relationships, with a syntax resembling SQL. SPARQL can be translated to SQL and works with popular web protocols. It consists of pattern matching, solution modifiers, and output. SPARQL applications include interrogating RDF engines, and it can work with RDFS reasoning. The document also discusses how SPARQL relates to rules for the semantic web, and compares SPARQL and SQL for finding and matching data from tabular and graph-based representations.
Data translation with SPARQL 1.1
This document provides an outline for a WWW 2012 tutorial on schema mapping with SPARQL 1.1. The outline includes sections on why data integration is important, schema mapping, translating RDF data with SPARQL 1.1, and common mapping patterns. Mapping patterns discussed include simple renaming, structural patterns like renaming based on property existence or value, value transformation using SPARQL functions, and aggregation. The tutorial aims to show how SPARQL 1.1 can be used to express executable mappings between different data schemas and representations.
Introduction to Spark - DataFactZ
We are a company driven by inquisitive data scientists, having developed a pragmatic and interdisciplinary approach, which has evolved over the decades working with over 100 clients across multiple industries. Combining several Data Science techniques from statistics, machine learning, deep learning, decision science, cognitive science, and business intelligence, with our ecosystem of technology platforms, we have produced unprecedented solutions. Welcome to the Data Science Analytics team that can do it all, from architecture to algorithms.
Our practice delivers data driven solutions, including Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. We employ a number of technologies in the area of Big Data and Advanced Analytics such as DataStax (Cassandra), Databricks (Spark), Cloudera, Hortonworks, MapR, R, SAS, Matlab, SPSS and Advanced Data Visualizations.
This presentation is designed for Spark Enthusiasts to get started and details of the course are below.
1. Introduction to Apache Spark
2. Functional Programming + Scala
3. Spark Core
4. Spark SQL + Parquet
5. Advanced Libraries
6. Tips & Tricks
7. Where do I go from here?
RDF-Gen: Generating RDF from streaming and archival data
Presentation of RDF-Gen implemented in datAcron project (http://datAcron-project.eu) for converting archival and streaming data to RDF triples.
SPARQL and RDF query optimization
The document presents Kisung Kim's Ph.D defense presentation on techniques for processing SPARQL queries over RDF graphs stored in a relational database. It introduces an RDF triple filtering framework called R3F that uses the structural information of RDF graphs to filter irrelevant triples and reduce redundant intermediate results during query processing. Two indices, the RP-index and RG-index, are proposed as part of R3F to provide efficient filtering of triples based on path patterns and subgraph patterns in the RDF graph.
SPARQL 1.1 Update (2013-03-05)
SPARQL is a standard query language for RDF that has undergone two iterations (1.0 and 1.1) through the W3C process. SPARQL 1.1 includes updates to RDF stores, subqueries, aggregation, property paths, negation, and remote querying. It also defines separate specifications for querying, updating, protocols, graph store protocols, and federated querying. Apache Jena provides implementations of SPARQL 1.1 and tools like Fuseki for deploying SPARQL servers.
Intro to Apache Spark and Scala, Austin ACM SIGKDD, 7/9/2014
The document provides an introduction to Apache Spark and Scala. It begins with background information on Spark as a fast cluster computing engine for Hadoop. It then discusses Scala as an object-oriented and functional language that runs on the Java Virtual Machine. The majority of the document focuses on explaining functional programming concepts in Scala like functions, higher order functions, for-comprehensions, and foldLeft. It also discusses how Scala can be useful for Java/OO programmers, Hadoop/Spark developers, and data scientists. The presentation aims to provide an overview of both Spark and Scala.
Scala 20140715
The document provides an introduction to Apache Spark and Scala. It begins with background information on Spark as a fast cluster computing engine for Hadoop. It then discusses Scala as an object-oriented and functional language that runs on the Java Virtual Machine. The majority of the document focuses on explaining functional programming concepts in Scala like functions, higher order functions, for-comprehensions, and foldLeft. It also discusses how Scala can be useful for Java developers, Hadoop/Spark developers, and data scientists.
Similar to LarKC Tutorial at ISWC 2009 - Data Model (20)
Spark and scala course content | Spark and scala course online training
Spark and scala course content | Spark and scala course online training
Apache spark - Architecture , Overview & libraries
Apache spark - Architecture , Overview & libraries
Coral & Transport UDFs: Building Blocks of a Postmodern Data Warehouse
Coral & Transport UDFs: Building Blocks of a Postmodern Data Warehouse
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival data
Intro to Apache Spark and Scala, Austin ACM SIGKDD, 7/9/2014
Intro to Apache Spark and Scala, Austin ACM SIGKDD, 7/9/2014
Recently uploaded
KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH
INDIA`S OWN LITERARY GENIUS MR.KHUSHWANT SINGH WAS TRULY A VERY BRAVE SOUL AND WAS AWARDED WITH THE MAGIC OF WORDS BY GOD.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as discipline
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
How to Setup Default Value for a Field in Odoo 17
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
How to Fix [Errno 98] address already in use
This slide will represent the cause of the error “[Errno 98] address already in use” and the troubleshooting steps to resolve this error in Odoo.
How to Manage Reception Report in Odoo 17
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Recently uploaded (20)
KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH
KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Juneteenth Freedom Day 2024 David Douglas School District
Juneteenth Freedom Day 2024 David Douglas School District
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
LarKC Tutorial at ISWC 2009 - Data Model
- 1. LarKC Data Layer Early adopter’s workshop
- 6. Used Concepts in the Data Model NG1 NG3 NG4 NG5 NG2 Labelled groups of statements Labelled groups of statements
- 7. Supported Sets of Statements RDF data types Description Example Set of statement RDF statements s1, p1, o1, ng1 s2, p2, o2 s3, p3, o3, ng3, {group1} RDF graph Named graph s1, p1, o1, ng1 , {group1} s2, p2, o2, ng1 , {group2} s3, p3, o3, ng1 Dataset SPARQL dataset represents a collection of graphs s1, p1, o1, ng1 s2, p2, o2, ng2 s3, p3, o3, ng3 Labelled group of statements RDF group of statements s1, p1, o1, ng1, {group1} s2, p2, o2, ng2, {group1} s3, p3, o3, ng3, {group1}
- 8. Java Interfaces to Implement the RDF Types
- 9. Java Interfaces to Create the RDF Types
- 10. The Different Implementations Name Purpose Cost to pass 10 6 RDF statements Set of statements Services to return RDF data (e.g., SPARQL construct and describe queries) 350MB memory Named graph in memory Pass named graphs between plug-ins 350MB memory HTTP published RDF graph Load chunks of RDF from URL few KBs + time to load the data Named graph retrieved from a SPARQL endpoint Pass named graphs using a SPARQL endpoint few KBs + time to transfer the data + to filter the dataset with SPARQL SPARQL dataset Constrain RDF data exposed by a SPARQL endpoint with a list of graph names few KBs + time to transfer the data + to filter the dataset with SPARQL Labelled group of statements Pass arbitrary sets of RDF statements between plug-ins (e.g., graph priming) few KBs + time to associate the statements + time to filter the labelled group
- 11. Questions?