In computer science and mathematics, graphs are abstract data structures that model structural relationships among objects. They are now widely used for data modeling in application domains for which identifying relationship patterns, rules, and anomalies is useful. These domains include the web graph,
social networks,etc. The ever increasing size of graph structured data for these applications creates a critical need for scalable systems that can process large amounts of it efficiently. The project aims at making a benchmarking tool for testing the performance of graph algorithms like BFS, Pagerank, DFS. with
MapReduce, Giraph, GraphLab and testing which approach works better on what kind of graphs.
Using Trimble TX9 terrestrial laser scanner my surveying team scanned an active runway in Western Australia to a tolerance spec of 3mm. The teams were working a live site providing aircraft right of way meant the teams had to setup and takedown scanner and targets to yield to any aircraft movements around the site and airspace. Surveyors took a ground based approach over drone UAS to maintain tighter vertical control than can be achieved using drone capture. We were tasked with looking for deviations, rutting and areas to derive Pavement Condition Index (PCI) criteria for their asset.
Once the data was captured surveying teams utilized TopoDot to assemble the raw scans into a consolidated model. They then attempted to use the pavement roughness algorithms in the software against the close to 3.4b points of classified data but had to split the datasets into halves and quads in order for the processing runs to complete. The Bentley product has an inbuilt “Road condition tool” which reports on pavement roughness characteristics but has preset expected pavement widths, roads not runway widths, set in the software. We explained to our surveyors that the algorithms might run faster in another product. It allowed us to explore FME as a point cloud processing workflow using feature tables functionality to quickly generate the statistics required for reporting deliverables using the entire dataset in one process.
Learn how to get started making Leaflet maps through R statistical software. Sounds crazy? Maybe. But the R package leafletR allows people familiar with R, but maybe not so much with HTML and Javascript coding, to make a basic Leaflet map (interactive, slipping web map) quickly with minimal knowledge of other programming languages.
Example code posted here: https://github.com/MicheleTobias/RCode
Using Trimble TX9 terrestrial laser scanner my surveying team scanned an active runway in Western Australia to a tolerance spec of 3mm. The teams were working a live site providing aircraft right of way meant the teams had to setup and takedown scanner and targets to yield to any aircraft movements around the site and airspace. Surveyors took a ground based approach over drone UAS to maintain tighter vertical control than can be achieved using drone capture. We were tasked with looking for deviations, rutting and areas to derive Pavement Condition Index (PCI) criteria for their asset.
Once the data was captured surveying teams utilized TopoDot to assemble the raw scans into a consolidated model. They then attempted to use the pavement roughness algorithms in the software against the close to 3.4b points of classified data but had to split the datasets into halves and quads in order for the processing runs to complete. The Bentley product has an inbuilt “Road condition tool” which reports on pavement roughness characteristics but has preset expected pavement widths, roads not runway widths, set in the software. We explained to our surveyors that the algorithms might run faster in another product. It allowed us to explore FME as a point cloud processing workflow using feature tables functionality to quickly generate the statistics required for reporting deliverables using the entire dataset in one process.
Learn how to get started making Leaflet maps through R statistical software. Sounds crazy? Maybe. But the R package leafletR allows people familiar with R, but maybe not so much with HTML and Javascript coding, to make a basic Leaflet map (interactive, slipping web map) quickly with minimal knowledge of other programming languages.
Example code posted here: https://github.com/MicheleTobias/RCode
Using R to Visualize Spatial Data: R as GIS - Guy LansleyGuy Lansley
This talk demonstrates some of the benefits of using R to visualize spatial data efficiently and clearly.
It was originally presented by Guy Lansley (UCL and the Consumer Data Research Centre) to the GIS for Social Data and Crisis Mapping Workshop at the University of Kent.
Slides of the presentation of the paper Document Representation Refinement for Precise Region Description by Christian Clausner, Stefan Pletschacher and Apostolos Antonacopoulos. #digidays
LiDAR (“Light Detection and Ranging”) is a method of remote sensing that uses light to measure ranges. LiDAR systems generate many component measurements that result in valuable spatial data.
All of this information results in massive files that are bursting with potential, but limited in use by their size and complexity.
In this webinar, learn how data integration techniques can help you get the most out of LiDAR and point cloud data. We’ll cover how to:
- Quickly process point clouds and integrate them with other data sources.
- Use LiDAR for 3D city modelling.
- Make a digital terrain and surface model from a point cloud.
- Integrate programs like LAStools into your workflows
By applying data integration automation, you save time, reduce manual effort, and ensure you get the most out of your LiDAR data.
Density functions are not suitable for sparse matrices, instead we can estimate covariance and apply L1 for sparsity. This makes for a graphical lasso!!
Time travel and time series analysis with pandas + statsmodelsAlexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge.
Pandas is a powerful framework for working with time series data and can make your life a lot easier.
This talks will feature:
how to analyse periodical data with pandas
read and write data in various formats
how to mangle, reshape and pivot
gain insights with statsmodels (e.g. seasonality)
caveats when working with timed data
visualize your data on the fly
One of the most promising areas in the world of NoSQL - graphs based storage and processing system which based on the theory of graphs. Neo4J - is, perhaps, the most popular Graph database at the moment. It provides high performance data storage and working with graphs, using various Java APIs and declarative query language Cypher.
Adobe, Cisco, classmates.com, Deutsche telecom and many others are using Neo4J.
Using R to Visualize Spatial Data: R as GIS - Guy LansleyGuy Lansley
This talk demonstrates some of the benefits of using R to visualize spatial data efficiently and clearly.
It was originally presented by Guy Lansley (UCL and the Consumer Data Research Centre) to the GIS for Social Data and Crisis Mapping Workshop at the University of Kent.
Slides of the presentation of the paper Document Representation Refinement for Precise Region Description by Christian Clausner, Stefan Pletschacher and Apostolos Antonacopoulos. #digidays
LiDAR (“Light Detection and Ranging”) is a method of remote sensing that uses light to measure ranges. LiDAR systems generate many component measurements that result in valuable spatial data.
All of this information results in massive files that are bursting with potential, but limited in use by their size and complexity.
In this webinar, learn how data integration techniques can help you get the most out of LiDAR and point cloud data. We’ll cover how to:
- Quickly process point clouds and integrate them with other data sources.
- Use LiDAR for 3D city modelling.
- Make a digital terrain and surface model from a point cloud.
- Integrate programs like LAStools into your workflows
By applying data integration automation, you save time, reduce manual effort, and ensure you get the most out of your LiDAR data.
Density functions are not suitable for sparse matrices, instead we can estimate covariance and apply L1 for sparsity. This makes for a graphical lasso!!
Time travel and time series analysis with pandas + statsmodelsAlexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge.
Pandas is a powerful framework for working with time series data and can make your life a lot easier.
This talks will feature:
how to analyse periodical data with pandas
read and write data in various formats
how to mangle, reshape and pivot
gain insights with statsmodels (e.g. seasonality)
caveats when working with timed data
visualize your data on the fly
One of the most promising areas in the world of NoSQL - graphs based storage and processing system which based on the theory of graphs. Neo4J - is, perhaps, the most popular Graph database at the moment. It provides high performance data storage and working with graphs, using various Java APIs and declarative query language Cypher.
Adobe, Cisco, classmates.com, Deutsche telecom and many others are using Neo4J.
Big Graph Analytics Systems (Sigmod16 Tutorial)Yuanyuan Tian
In recent years we have witnessed a surging interest in developing Big Graph processing systems. To date, tens of Big Graph systems have been proposed. This tutorial provides a timely and comprehensive review of existing Big Graph systems, and summarizes their pros and cons from various perspectives. We start from the existing vertex-centric systems, which which a programmer thinks intuitively like a vertex when developing parallel graph algorithms. We then introduce systems that adopt other computation paradigms and execution settings. The topics covered in this tutorial include programming models and algorithm design, computation models, communication mechanisms, out-of-core support, fault tolerance, dynamic graph support, and so on. We also highlight future research opportunities on Big Graph analytics.
Things to Consider When Selling Your House (Spring 2015)
This “Seller Guide” will help you simply and effectively explain the current market to potential sellers,
GM Diet is one such diet which helps you to lose weight easily without any strenuous effort and the results of the diet has made wonders and now many people have trust on this 7 days diet plan. GM Diet not only helps you to lose weight with ease but also in short span of time. It gives quick and beneficial results.
Graphs, Edges & Nodes - Untangling the Social WebJoël Perras
Many of the most popular web applications today deal with highly organized and structured data that represent entities, and the relationships between these entities. LinkedIn can tell you how many degrees of separation there are between yourself and the CEO of Samsung, Facebook can figure out people that you might already know, Digg can recommend article submissions that you might like, and LastFM suggests music based on your current listening habits.
We’ll take a look at the basic theory behind how some of these features can be implemented (no computer science degree required!), and then dig in to a few practical implementations using PHP & and a relational database, as well as with Redis. Lastly, we’ll take a quick look at the current landscape of graph-based datastores that simplify many of these operations.
ملخص الرسالة المقدمة من الباحث أحمد المباريدي المعيد بكلية التربية جامعة السويس للحصول على درجة الماجستير في التربية
تخصص تكنولوجيا التعليم
بعنوان تصميم بيئة تعلم تفاعلية قائمة على تطبيقات الويب 2.0 لتنمية بعض مهارات إنتاج برمجيات الوسائط المتعددة لدى طلاب شعبة تكنولوجيا التعليم
Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15MLconf
GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics: With increasing interest in large-scale distributed graph analytics for machine learning and data mining, more data scientists and developers are struggling to achieve high performance without sacrificing productivity on large graph problems. In this talk, I will discuss our solution to this problem: GraphMat. Using generalized sparse matrix-based primitives, we are able to achieve performance that is very close to hand-optimized native code, while allowing users to write programs using the familiar vertex-centric programming paradigm. I will show how we optimized GraphMat to achieve this performance on distributed platforms and provide programming examples. We have integrated GraphMat with Apache Spark in a manner that allows the combination to outperform all other distributed graph frameworks. I will explain the reasons for this performance and show that our approach achieves very high hardware efficiency in both single-node and distributed environments using primitives that are applicable to many machine learning and HPC problems. GraphMat is open source software and available for download.
Data Science is concerned with the analysis of large amounts of data. When the volume of data is really large, it requires the use of cooperating, distributed machines. The most popular method of doing this is Hadoop, a collection of programs to perform computations on connected machines in a cluster. Hadoop began life as an open-source implementation of MapReduce, an idea first developed and implemented by Google for its own clusters. Though Hadoop's MapReduce is Java-based, and quite complex, this talk focuses on the "streaming" facility, which allows Python programmers to use MapReduce in a clean and simple way. We will present the core ideas of MapReduce and show you how to implement a MapReduce computation using Python streaming. The presentation will also include an overview of the various components of the Hadoop "ecosystem."
NYC Data Science Academy is excited to welcome Sam Kamin who will be presenting an Introduction to Hadoop for Python Programmers a well as a discussion of MapReduce with Streaming Python.
Sam Kamin was a professor in the University of Illinois Computer Science Department. His research was in programming languages, high-performance computing, and educational technology. He taught a wide variety of courses, and served as the Director of Undergraduate Programs. He retired as Emeritus Associate Professor, and worked at Google until taking his current position as VP of Data Engineering in NYC Data Science Academy.
--------------------------------------
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
Converting between CAD and GIS is a common requirement for projects involving infrastructure, buildings, city plans, and more. Unfortunately, the workflow presents many challenges, like translating geometry, attributes, annotations, symbology, geolocation, and other elements. So how do you allow data to flow freely between these disparate data types, without losing the precision offered by CAD and the spatial context offered by GIS?
This webinar will explore the power of automated data integration workflows for CAD and GIS. First, we’ll discuss challenges and scenarios for CAD-to-GIS translations, and demo how to use FME to power a digital plan submission portal that validates CAD data and integrates it into the central GIS repository. Next, we’ll discuss challenges and scenarios for GIS-to-CAD conversions, and demo how to build an automated FME workflow for requesting CAD data from GIS.
Note: You have to download the slides and use either powerpoint or google slides to make the links clickable.
Machine Learning + Graph Databases for Better Recommendations
Presented by Chris Woodward
ATO 2022 - Machine Learning + Graph Databases for Better Recommendations (3)....ArangoDB Database
Note: You have to download the slides and use either powerpoint or google slides to make the links clickable.
ATO 2022 - Machine Learning + Graph Databases for Better Recommendations (3).pptx
STIC-D: algorithmic techniques for efficient parallel pagerank computation on...Subhajit Sahu
Authors:
Paritosh Garg
Kishore Kothapalli
Publication:
ICDCN '16: Proceedings of the 17th International Conference on Distributed Computing and Networking. January 2016.
Article No.: 15 Pages 1–10
https://doi.org/10.1145/2833312.2833322
Note: You have to download the slides and use either powerpoint or google slides to make the links clickable.
Machine Learning + Graph Databases for Better Recommendations
Presented by Chris Woodward
Maximize the possibilities of your LiDAR data with FME. Through demos, you’ll learn how to extract the full value of point clouds by quickly processing and combining them with other data sources. We’ll also show you real-world examples using LiDAR for 3D city modelling & viewshed analysis, with specific takeaways that can be applied to your own data. Plus, find out how to integrate command-line programs like LAStools into your FME workflow.
On Traffic-Aware Partition and Aggregation in Map Reduce for Big Data Applica...dbpublications
The MapReduce programming model simplifies
large-scale data processing on commodity cluster by
exploiting parallel map tasks and reduces tasks.
Although many efforts have been made to improve
the performance of MapReduce jobs, they ignore the
network traffic generated in the shuffle phase, which
plays a critical role in performance enhancement.
Traditionally, a hash function is used to partition
intermediate data among reduce tasks, which,
however, is not traffic-efficient because network
topology and data size associated with each key are
not taken into consideration. In this paper, we study
to reduce network traffic cost for a MapReduce job
by designing a novel intermediate data partition
scheme. Furthermore, we jointly consider the
aggregator placement problem, where each
aggregator can reduce merged traffic from multiple
map tasks. A decomposition-based distributed
algorithm is proposed to deal with the large-scale
optimization problem for big data application and an
online algorithm is also designed to adjust data
partition and aggregation in a dynamic manner.
Finally, extensive simulation results demonstrate that
our proposals can significantly reduce network traffic
cost under both offline and online cases.
Many computational solutions can be expressed as Di
rected Acyclic Graph (DAG), in which
nodes represent tasks to be executed and edges repr
esent precedence constraints among tasks.
A Cluster of processors is a shared resource among
several users and hence the need for a
scheduler which deals with multi-user jobs presente
d as DAGs. The scheduler must find the
number of processors to be allotted for each DAG an
d schedule tasks on allotted processors. In
this work, a new method to find optimal and maximum
number of processors that can be allotted
for a DAG is proposed. Regression analysis is used
to find the best possible way to share
available processors, among suitable number of subm
itted DAGs. An instance of a scheduler
for each DAG, schedules tasks on the allotted proce
ssors. Towards this end, a new framework
to receive online submission of DAGs, allot process
ors to each DAG and schedule tasks, is
proposed and experimented using a simulator. This s
pace-sharing of processors among multiple
DAGs shows better performance than the other method
s found in literature. Because of space-
sharing, an online scheduler can be used for each D
AG within the allotted processors. The use
of online scheduler overcomes the drawbacks of stat
ic scheduling which relies on inaccurate
estimated computation and communication costs. Thus
the proposed framework is a promising
solution to perform online scheduling of tasks usin
g static information of DAG, a kind of hybrid
scheduling
.
MULTIPLE DAG APPLICATIONS SCHEDULING ON A CLUSTER OF PROCESSORScscpconf
Many computational solutions can be expressed as Directed Acyclic Graph (DAG), in which
nodes represent tasks to be executed and edges represent precedence constraints among tasks.
A Cluster of processors is a shared resource among several users and hence the need for a
scheduler which deals with multi-user jobs presented as DAGs. The scheduler must find the
number of processors to be allotted for each DAG and schedule tasks on allotted processors. In
this work, a new method to find optimal and maximum number of processors that can be allotted
for a DAG is proposed. Regression analysis is used to find the best possible way to share
available processors, among suitable number of submitted DAGs. An instance of a scheduler
for each DAG, schedules tasks on the allotted processors. Towards this end, a new framework
to receive online submission of DAGs, allot processors to each DAG and schedule tasks, is
proposed and experimented using a simulator. This space-sharing of processors among multiple
DAGs shows better performance than the other methods found in literature. Because of spacesharing,
an online scheduler can be used for each DAG within the allotted processors. The use
of online scheduler overcomes the drawbacks of static scheduling which relies on inaccurate
estimated computation and communication costs. Thus the proposed framework is a promising
solution to perform online scheduling of tasks using static information of DAG, a kind of hybrid
scheduling.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
SparkNet implements a scalable, distributed algorithm to train deep neural networks that can be applied to existing batch processing frameworks like MapReduce and Spark.
Work by researchers at UC Berkeley.
Similar to Benchmarking Tool for Graph Algorithms (20)
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
Benchmarking Tool for Graph Algorithms
1. BenchMarking Tool for
Graph Algorithms
IIIT-H Cloud Computing - Major Project
By:
Abhinaba Sarkar 201405616
Malavika Reddy 201201193
Yash Khandelwal 201302164
Nikita Kad 201330030
2. Description
● In computer science and mathematics, graphs are abstract data structures that model
structural relationships among objects. They are now widely used for data modeling in
application domains for which identifying relationship patterns, rules, and anomalies is useful.
● These domains include the web graph, social networks,etc. The ever-increasing size of graph-
structured data for these applications creates a critical need for scalable systems that can
process large amounts of it efficiently.
● The project aims at making a benchmarking tool for testing the performance of graph
algorithms like BFS, Pagerank,etc. with MapReduce, Giraph, GraphLab and Neo4j and testing
which approach works better on what kind of graphs.
3. Motivation
● Analyze the runtime of different types of graph algorithms on different
types of distributed systems.
● Performing computation on a graph data structure requires processing at
each node.
● Each node contains node-specific data as well as links (edges) to other
nodes. So computation must traverse the graph which will take a huge
amount of time.
4. Approach
The BFS/SSSP algorithm is broken in 2 tasks:
● Map Task:In each Map task, we discover all the neighbors of the node currently in queue (we
used color encoding GRAY for nodes in queue) and add them to our graph.
● Reduce Task:In each Reduce task, we set the correct level of the nodes and update the graph.
The pagerank algorithm is also broken in 2 steps:
● Map Task: Each page emit its neighbours and current pagerank.
● Reduce Task: For each key(page) new page rank is calculated using pagerank emitted in the
map task.
○ PR(A)=(1-d) + d(PR(T1)/C(T1) + ... +PR(Tn)/C(Tn)) Where - C(P) is the cardinality (out-
degree) of page P, d is the damping (“random URL”) factor.
Dijkstra:
● Map task : In each of the map tasks, neighbors are discovered and put into
the queue with color coding gray.
● Reduce task : In each of the reduce tasks, we select the nodes according to
the shortest distances from the current node.
5. Approach contd.
Giraph and Hadoop
All the computations are done on a cluster of 2 nodes
Graphlab
All the computations are performed on single machine
6. Applications
In today’s world, dynamic social graphs (like:
linkedin, twitter and facebook) are not feasible to
process in single node. Therefore we need to
benchmark the runtime of different graph
algorithms in distributed system.
Example graph: LinkedIn’s social graph
7. Complexity
● BFS: The complexity of standard BFS algorithm is O(V+E) but because of
the overhead of read/write in distributed computing, the order reaches O
(E*Depth).
● Similar is the case for Dijkstra’s algorithm. But number of iterations will be
higher than BFS.
● Page Rank: The Complexity of pagerank in distributed system is –
(No. of Node + No. of Relations)*Iterations
8. Benchmarking - Giraph
Nodes Time
1000 4 min
7.836 sec
1 million 10 min
11.443sec
Nodes Time
1000 3 min 5.655
sec
1 million 11 min 0.05
sec
Nodes Time
1000 5 min
12.111 sec
1 million 16 min
8.652 sec
BFS Dijkstra Pagerank
9. Nodes Time
1000 6.029 sec
10,000 20.154 sec
1 million 1 min 11.124
sec
Nodes Time
1000 4.852 sec
10,000 13.029 sec
1 million 1 min 10.576sec
Page-Rank
Dijkstra
Benchmarking - Graphlab
10. Benchmarking - Hadoop
Nodes Time
1000 4 min
7.836 sec
1 million 10 min
11.443sec
Nodes Time
1000 3 min 5.655
sec
1 million 11 min 0.05
sec
BFS Dijkstra Pagerank
Nodes Time
1000 5 min
12.111 sec
1 million 16 min
8.652 sec
BFS and Dijkstra’s runtime depend on the depth of the input graph.
11. Problems we faced
● Poor locality of memory access.
● Very little work per vertex.
● Changing degree of parallelism.
● Running over many machines makes the problem worse
12. Conclusion and Future Work
● Although GraphLab is fast, there is constraint on memory as it requires as much memory to
contain the edges and their associated values of any single vertex in the graph.
● From the experimental results, it is seen that the time taken for pagerank algorithm is directly
proportional to the number of relations in the graph when the number of nodes and iterations
are constant. This explains the huge difference in time.
● The runtime of BFS is directly proportional to the depth of the graph. So, greater the depth,
more will be the number of iterations and hence more time.
Future Work:
Taking the input graph from file adds a huge overhead of reading and writing to files in each
iteration, so if somehow we can store the graph and its properties in a Database, the read/write
overhead will be gone and the query time will be reduced. So,we plan to include Database in it.