The document describes the process of cleaning up and migrating a university library patron database from using social security numbers to using unique identification numbers. It involved identifying and merging duplicate patron records, updating old records to use the new IDs, handling records missing data fields, and ensuring each patron only had one active barcode to work with a new checkout system. The process took over a year and involved repeatedly running custom scripts to identify and fix issues in an iterative way until the database was thoroughly cleaned up and migrated to the new ID system.
.the seven habits of highly effective peopleshriyapen
This document summarizes and reviews Stephen R. Covey's book "The Seven Habits of Highly Effective People". It provides biographical details about Covey, including that he had a BS in business administration and an MBA from Harvard. The book outlines Covey's seven habits and has been recommended by the reviewer because it is easy to understand, the habits seem simple, and it is full of integrity and humanity.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
The document is a slide presentation about building effective family habits based on Stephen Covey's book "The 7 Habits of Highly Effective Families". It discusses 7 key habits: 1) be proactive, 2) begin with the end in mind by creating a family mission statement, 3) put first things first by prioritizing family, 4) think win-win by finding solutions that benefit all family members, 5) seek first to understand others then to be understood, 6) synergize by celebrating differences, and 7) sharpen the saw by renewing the family spirit through traditions. The overarching message is that developing these habits can help families function effectively and build strong relationships.
This document discusses the importance of data quality and provides tips for ensuring high quality data. It notes that while data can be very useful, it is only valuable if it is clean and structured. When extracting large amounts of data, it recommends developing extractors, combining extractors, and automating the extraction process. For scaling operations, having processes to clean, validate, and maintain data quality is crucial. The document offers suggestions for writing effective XPaths and regex expressions to extract the right data. It also stresses the importance of measuring data quality through completeness, coverage, and detecting anomalies both during and after the extraction process.
In partnership with a leading global technology analyst firm, Dun & Bradstreet commissioned a new study to examine how Customer Data Management (CDM) impacts business development and overall performance. This exclusive study proves that smart CDM is essential for driving growth and staying ahead of the data explosion
The document provides an overview of the 7 Habits of Highly Effective Teens. It discusses what habits are and how they are formed. It then describes each of the 7 habits in detail: 1) Be Proactive, 2) Begin with the End in Mind, 3) Put First Things First, 4) Think Win-Win, 5) Seek First to Understand, Then to Be Understood, 6) Synergize, and 7) Sharpen the Saw. Exercises and examples are provided for applying each habit to improve effectiveness and relationships. The habits teach skills like time management, goal-setting, communication, teamwork and continual self-improvement.
As Twitch grew, both the amount of data we received and the number of employees interested in the data grew rapidly. In order to continue empowering decision making as we scaled, we turned to using Druid and Imply to provide self service analytics to both our technical and non technical staff allowing them to drill into high level metrics in lieu of reading generated reports.
In this talk, learn how Twitch implemented a common analytics platform for the needs of many different teams supporting hundreds of users, thousands of queries, and ~5 billion events each day. This session will explain our Druid architecture in detail, including:
-The end-to-end architecture deployed on Amazon that includes Kinesis, RDS, S3, Druid, Pivot and Tableau
-How the data is brought together to deliver a unified view of live customer engagement and historical trends
-Operational best practices we learnt scaling Druid
-An example walk through using the platform
.the seven habits of highly effective peopleshriyapen
This document summarizes and reviews Stephen R. Covey's book "The Seven Habits of Highly Effective People". It provides biographical details about Covey, including that he had a BS in business administration and an MBA from Harvard. The book outlines Covey's seven habits and has been recommended by the reviewer because it is easy to understand, the habits seem simple, and it is full of integrity and humanity.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
The document is a slide presentation about building effective family habits based on Stephen Covey's book "The 7 Habits of Highly Effective Families". It discusses 7 key habits: 1) be proactive, 2) begin with the end in mind by creating a family mission statement, 3) put first things first by prioritizing family, 4) think win-win by finding solutions that benefit all family members, 5) seek first to understand others then to be understood, 6) synergize by celebrating differences, and 7) sharpen the saw by renewing the family spirit through traditions. The overarching message is that developing these habits can help families function effectively and build strong relationships.
This document discusses the importance of data quality and provides tips for ensuring high quality data. It notes that while data can be very useful, it is only valuable if it is clean and structured. When extracting large amounts of data, it recommends developing extractors, combining extractors, and automating the extraction process. For scaling operations, having processes to clean, validate, and maintain data quality is crucial. The document offers suggestions for writing effective XPaths and regex expressions to extract the right data. It also stresses the importance of measuring data quality through completeness, coverage, and detecting anomalies both during and after the extraction process.
In partnership with a leading global technology analyst firm, Dun & Bradstreet commissioned a new study to examine how Customer Data Management (CDM) impacts business development and overall performance. This exclusive study proves that smart CDM is essential for driving growth and staying ahead of the data explosion
The document provides an overview of the 7 Habits of Highly Effective Teens. It discusses what habits are and how they are formed. It then describes each of the 7 habits in detail: 1) Be Proactive, 2) Begin with the End in Mind, 3) Put First Things First, 4) Think Win-Win, 5) Seek First to Understand, Then to Be Understood, 6) Synergize, and 7) Sharpen the Saw. Exercises and examples are provided for applying each habit to improve effectiveness and relationships. The habits teach skills like time management, goal-setting, communication, teamwork and continual self-improvement.
As Twitch grew, both the amount of data we received and the number of employees interested in the data grew rapidly. In order to continue empowering decision making as we scaled, we turned to using Druid and Imply to provide self service analytics to both our technical and non technical staff allowing them to drill into high level metrics in lieu of reading generated reports.
In this talk, learn how Twitch implemented a common analytics platform for the needs of many different teams supporting hundreds of users, thousands of queries, and ~5 billion events each day. This session will explain our Druid architecture in detail, including:
-The end-to-end architecture deployed on Amazon that includes Kinesis, RDS, S3, Druid, Pivot and Tableau
-How the data is brought together to deliver a unified view of live customer engagement and historical trends
-Operational best practices we learnt scaling Druid
-An example walk through using the platform
1. The document discusses the steps in completing the accounting cycle, including preparing adjusting and closing entries from a work sheet.
2. It provides examples of adjusting entries for supplies, prepaid insurance, unearned rent, wages payable, fees revenue, and depreciation expense using a sample work sheet.
3. The work sheet is used to incorporate adjustments into the trial balance to produce adjusted account balances and financial statements.
2022-11, AACL, Named Entity Recognition in Twitter: A Dataset and Analysis on...asahiushio1
Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (this https URL) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found athttps://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).
This talk articulates 1) what is a blockchain 2) why it is interesting 3) talks through use-cases grounded in real world projects. 4) Highlights questions government leaders should ask before deciding to use a blockchain.
The document is a presentation on analyzing consumption trends with alternative data like point-of-sale (POS) data. It discusses how POS data from supermarkets can be analyzed to understand product purchase trends over time and location. It then shows how POS data for items like masks, cup noodles, hand soap and toilet paper was analyzed during the February 2020 COVID-19 pandemic. Time series charts were created using Matplotlib to visualize purchase growth trends before and during the pandemic. Maps were also created using the Deck.gl library to visualize year-over-year purchase trends by prefecture in Japan.
[DSC Europe 23][Cryptica] Jovan_Milovanovic-Bank_Statement_Data_Analysis.pdfDataScienceConferenc1
This document summarizes a presentation about utilizing bank statement data to gain real-time insights into companies' financial health. It discusses how Finspot, a fintech company, wants to develop a tool called "Heartbeat" that parses bank statement data and produces indicators to assess economic risks. The tool would standardize raw data, map transactions to financial statements, create customized indicators, and use machine learning to predict defaults. Potential applications include improved risk assessments, timely loan suggestions, and fraud detection to help financial institutions.
The document describes Krist Wongsuphasawat's background and work in data visualization. It notes that he has a PhD in Computer Science from the University of Maryland, where he studied information visualization. He currently works as a data visualization scientist at Twitter, where he builds internal tools to analyze log data and monitor changes over time. Some of his projects include Scribe Radar, which allows users to search through and visualize client event data in order to find patterns and monitor effects of product changes. The document provides details on his approaches for dealing with large log datasets and visualizing user activity sequences.
This document provides an overview of how auditors can use audit command language (ACL) software to analyze data and detect anomalies and fraud. It discusses getting started with ACL, obtaining basic information from data, looking for anomalies, detailed transaction analysis, and provides an example case study of how ACL was used to detect procurement card fraud. The case study involved analyzing 1,840 transactions worth $209,403 over two years, compiling evidence from receipts and online orders, and identifying control issues that allowed the fraud to occur. Corrective actions were then implemented, including improving control structures and ongoing monitoring.
Using Visualizations to Monitor Changes and Harvest Insights from a Global-sc...Krist Wongsuphasawat
Slides from my talk at the IEEE Conference on Visual Analytics Science and Technology (VAST) 2014 in Paris, France.
ABSTRACT
Logging user activities is essential to data analysis for internet products and services.
Twitter has built a unified logging infrastructure that captures user activities across all clients it owns, making it one of the largest datasets in the organization.
This paper describes challenges and opportunities in applying information visualization to log analysis at this massive scale, and shows how various visualization techniques can be adapted to help data scientists extract insights.
In particular, we focus on two scenarios:\ (1) monitoring and exploring a large collection of log events, and (2) performing visual funnel analysis on log data with tens of thousands of event types.
Two interactive visualizations were developed for these purposes:
we discuss design choices and the implementation of these systems, along with case studies of how they are being used in day-to-day operations at Twitter.
Orange Asset Manager helps you Tracking, Cost variation, monitor and control your assets throughout their different years and entire life cycle.
Barcode generation is done by system itself, Good graphical user interface for easily Data Entry, Facility for maintaining three levels of asset, Attach all asset related information to asset records, Main Types of depreciation methods,Will manage all the company wise, transaction year wise records.
Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 150 countries coverage for patents and trademarks. Lighthouse IP data is available via several partners. The company is headquartered in Amsterdam-Schiphol in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.
Patent Global Bibliographic and Legal Data Collection (DIAMOND)
Lighthouse IP currently directly sources the official gazette of over 150 different countries.Data is created manually from the gazettes as soon as they are published. This enables Lighthouse IP to provide a stable and reliable data feed for many additional national offices available from other sources. The collection includes inventions and utility models. There are two separate data products offered, one for the bibliographic data and one for the legal data. The data is delivered in XML format base on the WIPO standards ST.9 and ST.17 for bibliographic and legal event data published in the gazettes. English translations and original text data are provided along with normalized names of persons and companies.
Patent Full Text Collection
Lighthouse IP has specialized in creating the largest collection of full text searchable patents. Currently the coverage offers access to more than 70 authorities. All of these XML files include original versions of the patent document and machine translations into English. Updating for all authorities is regular and in line with the publication frequency of the national office.
Trademark Collection
The Lighthouse IP Trademark Collection contains the data of 150 authorities. For all authorities backfile and frontfile are created, and made available in data feeds based on XML structures. Including translated owner names, classes, lists of goods and services and all relevant meta data, this collection is unique for use in trademark research.
PatentWarehouse.com ™ Image and XML archive
Via the PatentWarehouse.com platform Lighthouse IP provides API access to the world’s largest repository of patent publications in PDF format. For all its full text holdings Lighthouse IP has added the original full records and machine translations in XML format.
This document discusses setting up a relational database for a department store. It notes that following the store's expansion to new locations, there is a need for an enterprise-wide database to store sales and transaction data, especially with anticipated increased sales from marketing activities. When designing the database, important steps include supporting data accuracy, avoiding redundant data, and enabling enterprise-level reporting. The document discusses defining the relationships between entities like customers, products, sales and stores at varying levels of detail.
Chapter 9 Exercise 31. Liquidity ratios. Edison, Stagg, and Thor.docxchristinemaritza
This document discusses a project to develop an improved version of Microsoft Windows 10 Enterprise Client. It provides key details about the project, including objectives to create a more usable, secure and integrated operating system. It outlines projected costs of $70,450 which include research, staffing, testing equipment and software. It also discusses necessary resources like human resources, computers and finances. A responsibility assignment matrix is included to define roles for tasks. The critical path focuses on defining requirements, integration testing, and security/compatibility testing to ensure the new software meets needs.
Subscribed 2017: Building a Data Pipeline to Engage and Retain Your SubscribersZuora, Inc.
A customer data integration strategy has many benefits. With a "system of truth" for your subscribers, you can weave together data from multiple sources to create a single subscriber "picture"; compute metrics that show you how subscriber behavior changes over time; and ultimately be better able to make decisions about engagement and retention risk. In this session, we'll cover these topics as well as how to iterate and improve your customer data pipeline once it's in place.
TIBCO provides an analytics platform that delivers business value across the analytics spectrum from descriptive to predictive to prescriptive analytics. The platform includes Spotfire for visual analytics, predictive analytics using R scripting, and real-time event processing capabilities. It can consume and analyze various data sources including big data. The platform enables different types of users from data scientists to analysts to business users.
Jeremiah O'Connor & David Maynor - Chasing the Crypto Workshop: Tracking Fina...NoNameCon
This document summarizes a presentation given by Cisco researchers on tracking a Ukrainian Bitcoin phishing ring. It discusses how the researchers used DNS data and analysis to link phishing domains to a criminal group called Coinhoarder. The researchers found the group was using lookalike domains to steal users' Bitcoin credentials from popular wallets and exchanges. They traced the domains and ransom payments and found the operation involved multiple actors conducting phishing, ransomware attacks, and money laundering.
The document discusses various types of reports that can be generated in the Horizon library system, ranging from easy reports that provide quick answers to more difficult reports requiring SQL queries. It provides examples of easy reports to find borrower information or item circulation statistics and describes using the Item_Report tool to filter items. More complex reports may involve saving data to files and editing them in other programs. The most difficult reports involve hidden database tables only accessible by system administrators or statistics that have been collapsed over time.
This document provides information for the complete UMUC ACCT 220 course, including all discussions, quizzes, homework assignments, and the final exam from February 2016. It discusses selecting a publicly traded company to study, posting the company name and details in discussion forums, and analyzing the company's financial statements. The homework assignments guide the student through analyzing various sections of the company's 10-K report, including the income statement, balance sheet, statement of cash flows, and notes. The document is intended to provide all materials needed to complete the coursework for UMUC ACCT 220.
This document provides information for the complete UMUC ACCT 220 course, including all discussions, quizzes, homework assignments, and the final exam from February 2016. It discusses selecting a publicly traded company to study, posting the company name and details in discussion forums, and analyzing the company's financial statements. The homework assignments guide the student through analyzing various sections of the company's 10-K report, including the income statement, balance sheet, statement of cash flows, and notes. The document is intended to provide all materials needed to complete the coursework for UMUC ACCT 220.
Automating a Vendor File Load Process with Perl and Shell ScriptingRoy Zimmer
The document describes automating the process of retrieving vendor files from an FTP site, processing the files, splitting them, editing records, and loading them into a library system. Perl scripts and shell scripts are used to log into the FTP site, find the needed files, split files based on invoice numbers, edit records, prepare them for loading, and perform the loading. Passwords are automatically changed every two months using additional scripts. The overall process is designed to run hands-off on a regular schedule.
1. The document discusses the steps in completing the accounting cycle, including preparing adjusting and closing entries from a work sheet.
2. It provides examples of adjusting entries for supplies, prepaid insurance, unearned rent, wages payable, fees revenue, and depreciation expense using a sample work sheet.
3. The work sheet is used to incorporate adjustments into the trial balance to produce adjusted account balances and financial statements.
2022-11, AACL, Named Entity Recognition in Twitter: A Dataset and Analysis on...asahiushio1
Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (this https URL) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found athttps://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).
This talk articulates 1) what is a blockchain 2) why it is interesting 3) talks through use-cases grounded in real world projects. 4) Highlights questions government leaders should ask before deciding to use a blockchain.
The document is a presentation on analyzing consumption trends with alternative data like point-of-sale (POS) data. It discusses how POS data from supermarkets can be analyzed to understand product purchase trends over time and location. It then shows how POS data for items like masks, cup noodles, hand soap and toilet paper was analyzed during the February 2020 COVID-19 pandemic. Time series charts were created using Matplotlib to visualize purchase growth trends before and during the pandemic. Maps were also created using the Deck.gl library to visualize year-over-year purchase trends by prefecture in Japan.
[DSC Europe 23][Cryptica] Jovan_Milovanovic-Bank_Statement_Data_Analysis.pdfDataScienceConferenc1
This document summarizes a presentation about utilizing bank statement data to gain real-time insights into companies' financial health. It discusses how Finspot, a fintech company, wants to develop a tool called "Heartbeat" that parses bank statement data and produces indicators to assess economic risks. The tool would standardize raw data, map transactions to financial statements, create customized indicators, and use machine learning to predict defaults. Potential applications include improved risk assessments, timely loan suggestions, and fraud detection to help financial institutions.
The document describes Krist Wongsuphasawat's background and work in data visualization. It notes that he has a PhD in Computer Science from the University of Maryland, where he studied information visualization. He currently works as a data visualization scientist at Twitter, where he builds internal tools to analyze log data and monitor changes over time. Some of his projects include Scribe Radar, which allows users to search through and visualize client event data in order to find patterns and monitor effects of product changes. The document provides details on his approaches for dealing with large log datasets and visualizing user activity sequences.
This document provides an overview of how auditors can use audit command language (ACL) software to analyze data and detect anomalies and fraud. It discusses getting started with ACL, obtaining basic information from data, looking for anomalies, detailed transaction analysis, and provides an example case study of how ACL was used to detect procurement card fraud. The case study involved analyzing 1,840 transactions worth $209,403 over two years, compiling evidence from receipts and online orders, and identifying control issues that allowed the fraud to occur. Corrective actions were then implemented, including improving control structures and ongoing monitoring.
Using Visualizations to Monitor Changes and Harvest Insights from a Global-sc...Krist Wongsuphasawat
Slides from my talk at the IEEE Conference on Visual Analytics Science and Technology (VAST) 2014 in Paris, France.
ABSTRACT
Logging user activities is essential to data analysis for internet products and services.
Twitter has built a unified logging infrastructure that captures user activities across all clients it owns, making it one of the largest datasets in the organization.
This paper describes challenges and opportunities in applying information visualization to log analysis at this massive scale, and shows how various visualization techniques can be adapted to help data scientists extract insights.
In particular, we focus on two scenarios:\ (1) monitoring and exploring a large collection of log events, and (2) performing visual funnel analysis on log data with tens of thousands of event types.
Two interactive visualizations were developed for these purposes:
we discuss design choices and the implementation of these systems, along with case studies of how they are being used in day-to-day operations at Twitter.
Orange Asset Manager helps you Tracking, Cost variation, monitor and control your assets throughout their different years and entire life cycle.
Barcode generation is done by system itself, Good graphical user interface for easily Data Entry, Facility for maintaining three levels of asset, Attach all asset related information to asset records, Main Types of depreciation methods,Will manage all the company wise, transaction year wise records.
Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 150 countries coverage for patents and trademarks. Lighthouse IP data is available via several partners. The company is headquartered in Amsterdam-Schiphol in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.
Patent Global Bibliographic and Legal Data Collection (DIAMOND)
Lighthouse IP currently directly sources the official gazette of over 150 different countries.Data is created manually from the gazettes as soon as they are published. This enables Lighthouse IP to provide a stable and reliable data feed for many additional national offices available from other sources. The collection includes inventions and utility models. There are two separate data products offered, one for the bibliographic data and one for the legal data. The data is delivered in XML format base on the WIPO standards ST.9 and ST.17 for bibliographic and legal event data published in the gazettes. English translations and original text data are provided along with normalized names of persons and companies.
Patent Full Text Collection
Lighthouse IP has specialized in creating the largest collection of full text searchable patents. Currently the coverage offers access to more than 70 authorities. All of these XML files include original versions of the patent document and machine translations into English. Updating for all authorities is regular and in line with the publication frequency of the national office.
Trademark Collection
The Lighthouse IP Trademark Collection contains the data of 150 authorities. For all authorities backfile and frontfile are created, and made available in data feeds based on XML structures. Including translated owner names, classes, lists of goods and services and all relevant meta data, this collection is unique for use in trademark research.
PatentWarehouse.com ™ Image and XML archive
Via the PatentWarehouse.com platform Lighthouse IP provides API access to the world’s largest repository of patent publications in PDF format. For all its full text holdings Lighthouse IP has added the original full records and machine translations in XML format.
This document discusses setting up a relational database for a department store. It notes that following the store's expansion to new locations, there is a need for an enterprise-wide database to store sales and transaction data, especially with anticipated increased sales from marketing activities. When designing the database, important steps include supporting data accuracy, avoiding redundant data, and enabling enterprise-level reporting. The document discusses defining the relationships between entities like customers, products, sales and stores at varying levels of detail.
Chapter 9 Exercise 31. Liquidity ratios. Edison, Stagg, and Thor.docxchristinemaritza
This document discusses a project to develop an improved version of Microsoft Windows 10 Enterprise Client. It provides key details about the project, including objectives to create a more usable, secure and integrated operating system. It outlines projected costs of $70,450 which include research, staffing, testing equipment and software. It also discusses necessary resources like human resources, computers and finances. A responsibility assignment matrix is included to define roles for tasks. The critical path focuses on defining requirements, integration testing, and security/compatibility testing to ensure the new software meets needs.
Subscribed 2017: Building a Data Pipeline to Engage and Retain Your SubscribersZuora, Inc.
A customer data integration strategy has many benefits. With a "system of truth" for your subscribers, you can weave together data from multiple sources to create a single subscriber "picture"; compute metrics that show you how subscriber behavior changes over time; and ultimately be better able to make decisions about engagement and retention risk. In this session, we'll cover these topics as well as how to iterate and improve your customer data pipeline once it's in place.
TIBCO provides an analytics platform that delivers business value across the analytics spectrum from descriptive to predictive to prescriptive analytics. The platform includes Spotfire for visual analytics, predictive analytics using R scripting, and real-time event processing capabilities. It can consume and analyze various data sources including big data. The platform enables different types of users from data scientists to analysts to business users.
Jeremiah O'Connor & David Maynor - Chasing the Crypto Workshop: Tracking Fina...NoNameCon
This document summarizes a presentation given by Cisco researchers on tracking a Ukrainian Bitcoin phishing ring. It discusses how the researchers used DNS data and analysis to link phishing domains to a criminal group called Coinhoarder. The researchers found the group was using lookalike domains to steal users' Bitcoin credentials from popular wallets and exchanges. They traced the domains and ransom payments and found the operation involved multiple actors conducting phishing, ransomware attacks, and money laundering.
The document discusses various types of reports that can be generated in the Horizon library system, ranging from easy reports that provide quick answers to more difficult reports requiring SQL queries. It provides examples of easy reports to find borrower information or item circulation statistics and describes using the Item_Report tool to filter items. More complex reports may involve saving data to files and editing them in other programs. The most difficult reports involve hidden database tables only accessible by system administrators or statistics that have been collapsed over time.
This document provides information for the complete UMUC ACCT 220 course, including all discussions, quizzes, homework assignments, and the final exam from February 2016. It discusses selecting a publicly traded company to study, posting the company name and details in discussion forums, and analyzing the company's financial statements. The homework assignments guide the student through analyzing various sections of the company's 10-K report, including the income statement, balance sheet, statement of cash flows, and notes. The document is intended to provide all materials needed to complete the coursework for UMUC ACCT 220.
This document provides information for the complete UMUC ACCT 220 course, including all discussions, quizzes, homework assignments, and the final exam from February 2016. It discusses selecting a publicly traded company to study, posting the company name and details in discussion forums, and analyzing the company's financial statements. The homework assignments guide the student through analyzing various sections of the company's 10-K report, including the income statement, balance sheet, statement of cash flows, and notes. The document is intended to provide all materials needed to complete the coursework for UMUC ACCT 220.
Similar to Taking Your Customers to the Cleaners: Historical Patron Data Cleanup and Routine Purge Preparation (20)
Automating a Vendor File Load Process with Perl and Shell ScriptingRoy Zimmer
The document describes automating the process of retrieving vendor files from an FTP site, processing the files, splitting them, editing records, and loading them into a library system. Perl scripts and shell scripts are used to log into the FTP site, find the needed files, split files based on invoice numbers, edit records, prepare them for loading, and perform the loading. Passwords are automatically changed every two months using additional scripts. The overall process is designed to run hands-off on a regular schedule.
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The document discusses implementing automated cross-platform report generation using SQL, PL/SQL, and Perl on Unix and Windows systems. It provides an overview of using cron jobs on Unix to run scripts that generate reports from a database, FTP the reports to a Windows PC, and use WinBatch scripts to format and print the reports. Examples of code for generating reports using SQL, PL/SQL and Perl are also included.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Taking Your Customers to the Cleaners: Historical Patron Data Cleanup and Routine Purge Preparation
1. Taking Your Customers to the Cleaners: Historical Patron Data Cleanup and Routine Purge Preparation Roy Zimmer Western Michigan University
2. About 5 or 6 years ago… No more SSN switch to using WIN WIN is our Western Identification Number
3. About 5 or 6 years ago… No more SSN switch to using WIN Banner WIN is our Western Identification Number
4. About 5 or 6 years ago… No more SSN switch to using WIN Banner New campus ID cards WIN is our Western Identification Number
5. A few less years ago… Rewrote the patron update process to use Banner
6. A few less years ago… Rewrote the patron update process to use Banner Started thinking about not being SSN-based
7. 2007-2008 The WIN had become available in the data feeds for our patron update. Needed to change Institution ID interim step: arbitrary 14-digits -> WIN final step: WIN -> Bronco NetID Patron update was switched from being SSN-based to WIN-based. BroncoNetID is our single signon ID
8. Summer 2008 – What we started with Have data for about 74,000 patrons. About 183,000 barcodes (less than half are active!).
9. Summer 2008 – What we started with Have data for about 74,000 patrons. About 183,000 barcodes (less than half are active!). Several thousand duplicate records, one with SSN, one with WIN (in the SSAN field) The older duplicate record typically had charges, amounts owed, etc.
10. 2008: August – October Most of my time was spent on the cleanup… Dali
12. (WINs & SSNs above are not real) Sample output used one day
13. Our first run came up with 3489 duplicate patron records.
14. We created a program that used the LB4020 report as input to identify patron records that we wanted to alter – call it LB4020fix. These records needed to be extracted from Voyager for modification and re-import. Modify me with LB4020fix
15. Voyager has a patron extract utility, but it doesn’t extract all relevant data for a patron. We’d started using our own – patronsif.pl - years ago.
16. Voyager has a patron extract utility, but it doesn’t extract all relevant data for a patron. We’d started using our own – patronsif.pl - years ago. Voyager extract (Pptrnextr) Up to 3 patron-barcode + group combinations Similarly limited number of addresses WMU extract (patronsif.pl) Unlimited patron-barcode + group combinations Unlimited number of addresses + - + - -> +
17. Voyager has a patron extract utility, but it doesn’t extract all relevant data for a patron. We’d started using our own – patronsif.pl - years ago. For the patron cleanup we incorporated patronsif.pl into LB4020fix. Patron notes field problem: CR+LF stored if user pressed the RETURN key creates unwanted extra lines within a record drop_crlf utility replaces “CR+LF” with “space+space”
18. LB4020fix reads the duplicate report (LB4020) and extracts patron sif format data for the duplicate records. SIF-A new WIN-based records BroncoNetID in InstitutionID change expiredate to 1981.01.01 SIF-B old SSN-based records change InstitutionID to current BroncoNetID SIF-C new WIN-based records have the current update, expire, and purge dates and BroncoNetID The heart of the cleanup process
19. SIF-A new WIN-based records BroncoNetID in InstitutionID change expiredate to 1981.01.01 SIF-B old SSN-based records change InstitutionID to current BroncoNetID SIF-C new WIN-based records have the current update, expire, and purge dates and BroncoNetID update, key on SSN purge on expiredate 1982.01.01 [remove new records] 1 LB4020fix reads the duplicate report (LB4020) and extracts patron sif format data for the duplicate records. The heart of the cleanup process
20. SIF-A new WIN-based records BroncoNetID in InstitutionID change expiredate to 1981.01.01 SIF-B old SSN-based records change InstitutionID to current BroncoNetID SIF-C new WIN-based records have the current update, expire, and purge dates and BroncoNetID update, key on SSN purge on expiredate 1982.01.01 [remove new records] update, key on SSN [prep old records to be “new”] 1 2 LB4020fix reads the duplicate report (LB4020) and extracts patron sif format data for the duplicate records. The heart of the cleanup process
21. SIF-A new WIN-based records BroncoNetID in InstitutionID change expiredate to 1981.01.01 SIF-B old SSN-based records change InstitutionID to current BroncoNetID SIF-C new WIN-based records have the current update, expire, and purge dates and BroncoNetID update, key on SSN purge on expiredate 1982.01.01 [remove new records] update, key on SSN [prep old records to be “new”] update, key on InstID [unify old records with new data] 1 2 3 LB4020fix reads the duplicate report (LB4020) and extracts patron sif format data for the duplicate records. The heart of the cleanup process
22. SIF-A new WIN-based records have current BroncoNetID change expiredate to 1981.01.01 SIF-B old SSN-based records change InstitutionID to current BroncoNetID SIF-C new WIN-based records have the current update, expire, and purge dates and BroncoNetID update, key on SSN purge on expiredate 1982.01.01 [remove new records] update, key on SSN [prep old records to be “new”] update, key on InstID [unify old records with new data] 1 2 3 LB4020fix reads the duplicate report (LB4020) and extracts patron sif format data for the duplicate records. The heart of the cleanup process This clean-up process, with variations, was repeated many times. Details omitted here for the sake of brevity (and sanity).
23. Several things went awry along the way. Not all records could be matched up with a WIN or SSN (as reported by LB4020), so those had to be handled by assigning temporary SSNs, WINs, and/or Institution IDs.
24. Several things went awry along the way. Not all records could be matched up with a WIN or SSN (as reported by LB4020), so those had to be handled by assigning temporary SSNs, WINs, and/or Institution IDs. At another point, the interim records used in the process weren’t deleted during a purge. Those had to be detected, reassigned an older expiration date (1971.01.01), and carefully purged before proceeding.
26. We added the expiration date to the duplicate detector, LB4020. Now we could see that all the SSN-based records were expired, or about to be.
27. We added the expiration date to the duplicate detector, LB4020. Now we could see that all the SSN-based records were expired, or about to be. At this time we discovered that new WIN-based records were coming in as duplicates to SSN-based records that were typically set to expire 2008.09.08.
28. We added the expiration date to the duplicate detector, LB4020. Now we could see that all the SSN-based records were expired, or about to be. At this time we discovered that new WIN-based records were coming in as duplicates to SSN-based records that were typically set to expire 2008.09.08. This had to change!
29. We added the expiration date to the duplicate detector, LB4020. Now we could see that all the SSN-based records were expired, or about to be. At this time we discovered that new WIN-based records were coming in as duplicates to SSN-based records that were typically set to expire 2008.09.08. This had to change! And the semester was about to start…
30. Yes, we did avert disaster. But we had more problems. Early September…
31. Yes, we did avert disaster. But we had more problems. The duplicate detection report, which had grown to 60 pages, was now down to 1. The next day it had grown to 3 pages. Early September…
32. Yes, we did avert disaster. But we had more problems. The duplicate detection report, which had grown to 60 pages, was now down to 1. The next day it had grown to 3 pages. Some records not having all fields populated on the LB4020 duplicate detector caused problems. Also had to fix duplicate records where the SSAN field was null. Early September…
33. We removed several hundred obsolete records that had neither WIN nor SSN. Discovered records that had no Institution ID – yet another problem. Mid September…
34. We removed several hundred obsolete records that had neither WIN nor SSN. Discovered records that had no Institution ID – yet another problem. We are now down to 1 SSN-based record. Mid September… This person had our assigned WIN being the same as the SSN. Not supposed to happen! Identified 15 more such instances and submitted them to I.T. for correction.
35. Found some more SSN-based records – don’t know why they still existed – and converted them to being WIN-based. October… Flipped the “switch” so that we no longer get SSNs for our patron update.
36. Still had records from our NOTIS era – pre Summer 1998 Purged them if they: did not have life-time borrowing privileges did not have an SSN recorded did have an Institution ID Legacy data
39. 3M SelfCheck requires 1 active barcode per patron. We had 11058 patrons with multiple active barcodes.
40. 3M SelfCheck requires 1 active barcode per patron. We had 11058 patrons with multiple active barcodes. Wrote a program to whittle that down. Got them reduced to 300, but the next day, it was up to 1777!
41. 3M SelfCheck requires 1 active barcode per patron. We had 11058 patrons with multiple active barcodes. Wrote a program to whittle that down. Got them reduced to 300, but the next day, it was up to 1777! Under control now, with patrononeactive.pl, running Monday – Friday. This keeps only the most current active barcode for a patron.
42. 3M SelfCheck requires 1 active barcode per patron. We had 11058 patrons with multiple active barcodes. Wrote a program to whittle that down. Got them reduced to 300, but the next day, it was up to 1777! Under control now, with patrononeactive.pl, running Monday – Friday. This keeps only the most current active barcode for a patron. Forgot about those patron records without an Institution ID. Had 882 of them. Fixed them.
43. We looked at records created before 2008, those that had no SSN but did have an Institution ID. Extracted these records, modified them: expiredate = createdate purgedate = expiredate + 4 years Reimported these records. They should disappear with future annual patron purges. An eye towards the future…
44. We still had 11,696 records with no SSN (nor WIN). We expect most of these to be routinely purged in the future, leaving us with 456. What we ended with
45. We still had 11,696 records with no SSN (nor WIN). We expect most of these to be routinely purged in the future, leaving us with 456. When we started, we had about 250,000 patron records. We now have about 68,000. Duplicate records are routinely dealt with. We filter out all but the single most current active barcode for a patron. We will have annual patron purges. What we ended with
46. Know what you’re starting with. Keep your goal in mind. Figure out a good solution. Be flexible. Be ready for mistakes. Watch out for new/current data undoing your changes. Know when you’re done. Worthwhile points…
47. patronsif.pl drop_crlf lb4020.pl lb4020fix.pl patrononeactive.pl patrononactive.ksh Contact me if you would like to get any of the above. Resources
48. patronsif.pl as listed, gets patron data and puts it in patron SIF format. institution ID based. gets all patron+barcode groupings. (not site-specific) drop_crlf shell script that contains this line: perl -pi -e's// /g' $1 replaces CR+LF combination with two spaces. (this is useful anytime you use patronsif.pl) Some details on the resources…
49. lb4020.pl detects duplicate patron records. shows: name, expired (Y/N), SSAN, expire date, modify date, institution ID WMU-specific: indicates whether SSN or WIN in SSAN. modification required for your institution. lb4020fix.pl control structure around patronsif.pl code that uses lb4020.pl output as starting point for the fixing process. creates one or more patron SIF files for fixing data. use drop_crlf if necessary. Some details on the resources…
50. patrononeactive.pl queries Voyager, checking patrons’ active barcodes. if more than one is found, changes all but the most recent active barcodes to other . check the code carefully as it may need modification for your use. (incorporates patronsif.pl code) patrononeactive.ksh combines patrononeactive.pl and drop_crlf in a script suitable for cron use Some details on the resources…