This tutorial demonstrates how to use Google Refine for data cleansing and enrichment. It shows how to import data, perform faceting to identify issues, remove redundancies, cluster similar values, use expressions to transform data, link data to external sources for augmentation, and export the refined data. Functions like numeric faceting, text faceting, and timeline faceting are covered. The tutorial also provides an example of analyzing Twitter and Facebook data using Google Refine.
General overview of the Big Data Concept.
Presentation of the Hierarchical Linear Subspace Indexing Method to perform exact similarity search in high dimensional data
General overview of the Big Data Concept.
Presentation of the Hierarchical Linear Subspace Indexing Method to perform exact similarity search in high dimensional data
Large-Scale Data Extraction, Structuring and Matching using Python and SparkDeep Kayal
Matching data collections with the aim to augment and integrate the information for any available data point that lies in two or more of these collections, is a problem that nowadays arises often. Notable examples of such data points are scientific publications for which metadata and data are kept in various repositories, and users’ profiles, whose metadata and data exist in several social networks or platforms.
In our case, collections were as follows: (1) A large dump of compressed data files on s3 containing archives in the form of zips, tars, bzips and gzips, which were expected to contain published papers in the form of xmls and pdfs, amongst other files, and (2) A large store of xmls in the form of xmls, some of which are to be matched to Collection 1.
The problems, then, are: (1) How to best unzip the compressed archives and extract the relevant files? (2) How to extract meta-information from the xml or pdf files? (3) How to match the meta-information from the two different collections? And all of these must be done in a big-data environment.
The presentation will describe the solution process and the use of python and Spark in the large-scale unzipping and extraction of files from archives, and how metadata was then extracted from the files to perform the matches on.
Big Data Day LA 2015 - Scalable and High-Performance Analytics with Distribut...Data Con LA
"R is the most popular language in the data-science community with 2+ million users and 6000+ R packages. R’s adoption evolved along with its easy-to-use statistical language, graphics, packages, tools and active community. In this session we will introduce Distributed R, a new open-source technology that solves the scalability and performance limitations of vanilla R. Since R is single-threaded and does not scale to accommodate large datasets, Distributed R addresses many of R’s limitations. Distributed R efficiently shares sparse structured data, leverages multi-cores, and dynamically partitions data to mitigate load imbalance.
In this talk, we will show the promise of this approach by demonstrating how important machine learning and graph algorithms can be expressed in a single framework and are substantially faster under Distributed R. Additionally, we will show how Distributed R complements Vertica, a state-of-the-art columnar analytics database, to deliver a full-cycle, fully integrated, data “prep-analyze-deploy” solution."
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
Finding The Perfect Donor Database In An Imperfect World4Good.org
There are hundreds of donor databases on the market. Each has its own strengths and weaknesses, fans and foes. The challenge is to find a system with strengths that meet your needs, weaknesses that won’t get in your way, at a price you can afford.
This workshop will cover the basic concepts you will need to evaluate your options and make an informed decision.
Big Data Ecosystem for Data-Driven Decision MakingAbzetdin Adamov
The extremely fast grow of Internet Services, Web and Mobile Applications and advance of the related Pervasive, Ubiquity and Cloud Computing concepts have stumulated production of tremendous amounts of data partially available online (call metadata, texts, emails, social media updates, photos, videos, location, etc.). Even with the power of today’s modern computers it still big challenge for business and government organizations to manage, search, analyze, and visualize this vast amount of data as information. Data-Intensive computing which is intended to address this problems become quite intense during the last few years yielding strong results. Data intensive computing framework is a complex system which includes hardware, software, communications, and Distributed File System (DFS) architecture.
Just small part of this huge amount is structured (Databases, XML, logs) or semistructured (web pages, email), over 90% of this information is unstructured, what means data does not have predefined structure and model. Generally, unstructured data is useless unless applying data mining and analysis techniques. At the same time, just in case if you can process and understand your data, this data worth anything, otherwise it becomes useless.
Similar to Google refine from a business perspective (20)
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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
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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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Generating a custom Ruby SDK for your web service or Rails API using Smithy
Google refine from a business perspective
1. Vinod Gupta School of Management, IIT Kharagpur
Google Refine Analysis
A Business Perspective
April, 08 2012
Sathishwaran.R - 10BM60079
Vijaya Prabhu - 10BM60097
This Tutorial was created using Google Refine Version 2.5 on a Windows 7 platform
2. Data Cleansing
• Data cleansing is identifying the wrong or inaccurate
records in the data set and making appropriate
corrections to the records.
• It involves identifying incomplete, inaccurate, and
incorrect parts of data and then either replacing them
with correct data or deleting the incorrect data
• Data cleansing results in data which is consistent with
the other standard data and is useful for performing
various analysis
• The error in the data could be due to data entry error
by the user, failure during transmission of data or
improper data definitions.
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3. Need for Data Cleansing
• Incorrect or inaccurate data may lead to false
conclusions and can cause investments to be
misdirected in finance.
• Also government needs accurate data on
population and census for directing the funds to
the deserving areas.
• Many organizations tap into customer
information. If the data is not accurate, for eg. If
the address is not accurate then the business
runs the risk of send wrong information, thus
losing customers.
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4. Challenges Data Cleansing
• Loss of Information: In many cases the record may be
incomplete, hence the whole record may require to be
deleted which leads to loss of information. It could
become costly if huge number of data is deleted.
• Maintenance of Data: Once the data is cleansed then
any change in the data specification needs to affect
only the new values. Hence data management
solutions should be designed in such a way that the
process of data entry and retrieval are altered to
provide correct data.
• Data cleansing is an iterative process which needs
significant work in exploration and corrction of entries.
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5. About Google Refine
• Google Refine is a powerful tool that can be effectively
used for data cleansing.
• It helps in working with raw data, cleaning it
up, transforming from one format to
other, encompassing it with web services and linking it
to databases.
• It is very easy to use and has a web interface.
• It is freely available and works well with any browser.
• Google Refine is a desktop application and it runs a
small web server on your system and we need to point
our browser to the server to use refine.
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6. Getting Started - Installation
1. Download the zip file (appropriate Windows,
Mac, Linux versions) from the link
http://code.google.com/p/google-
refine/wiki/Downloads?tm=2
2. Uncompress the files from the zip file.
3. Run the “google-refine.exe” file.
4. A command window opens and Google refine
runs taking the user to the home page in the
default browser.
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8. Importing Data
• Google Refine supports TSV, CSV, Excel (.xls
and .xlsx), JSON, XML, and Google data
document formats.
• Once imported the data is in Google Refine’s
own data format.
• We have used TSV data on Disasters
worldwide from 1900-2008 available from
http://www.infochimps.com/datasets/disaster
s-worldwide-from-1900-2008 for the tutorial.
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13. Faceting
• Faceting is about seeing the big picture and
filtering based on rows to work on data you
want to change in bulk.
• We can create a facet for a column to get the
details about that column and then we can
filter to a subset of rows with a constraint.
• We can perform text facet, Numeric
facet, timeline facet and scatterplot facet. Also
various customized facets can be designed.
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34. Data Augmentation
• Reconciliation option in Google refine allows
data to be linked to web pages. Suppose we
want details on the country where the
calamity has struck we can perform the
following steps
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