Project report on the design and build of a data warehouse from unstructured and structured data sources (Quandl, yelp and UK Office for National Statistics) using SQL Server 2016, MongoDB and IBM Watson. Design and implementation of business intelligence visualisations using Tableau to answer cross domain business questions
Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
A data warehouse is a large collection of integrated data from multiple sources that is structured for analysis and reporting. It allows users to gain insights from historical data to support business decisions and identify trends. Data is extracted from operational systems, transformed for consistency and quality, and loaded into the data warehouse where it is stored in a multidimensional structure to enable analysis. This involves fact and dimension tables along with techniques like denormalization to optimize query performance.
The document discusses the key concepts and components of a data warehouse. It defines a data warehouse as a subject-oriented, integrated, non-volatile, and time-variant collection of data used for decision making. The document outlines the typical characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. It also describes the common components of a data warehouse such as the source data, data staging, data storage, information delivery, and metadata. Finally, the document provides examples of applications and uses of data warehouses.
The document discusses the basic structure of a data warehouse, including extracting source data, processing and storing data in a data staging area, populating data marts from the data warehouse, and providing user access through query and reporting tools. It also covers dimensional modeling, building conformed dimensions across data marts, handling slowly changing dimensions, and designing descriptive dimension tables.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
This document outlines a course on data warehousing and data mining. It introduces key concepts like relational databases, data warehouses, dimensional modeling, and data mining techniques. It also details the course objectives, schedule, assignments, and policies. The goal is for students to gain experience applying data mining methods and understanding the relationship between data mining and other fields.
Project report on the design and build of a data warehouse from unstructured and structured data sources (Quandl, yelp and UK Office for National Statistics) using SQL Server 2016, MongoDB and IBM Watson. Design and implementation of business intelligence visualisations using Tableau to answer cross domain business questions
Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
A data warehouse is a large collection of integrated data from multiple sources that is structured for analysis and reporting. It allows users to gain insights from historical data to support business decisions and identify trends. Data is extracted from operational systems, transformed for consistency and quality, and loaded into the data warehouse where it is stored in a multidimensional structure to enable analysis. This involves fact and dimension tables along with techniques like denormalization to optimize query performance.
The document discusses the key concepts and components of a data warehouse. It defines a data warehouse as a subject-oriented, integrated, non-volatile, and time-variant collection of data used for decision making. The document outlines the typical characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. It also describes the common components of a data warehouse such as the source data, data staging, data storage, information delivery, and metadata. Finally, the document provides examples of applications and uses of data warehouses.
The document discusses the basic structure of a data warehouse, including extracting source data, processing and storing data in a data staging area, populating data marts from the data warehouse, and providing user access through query and reporting tools. It also covers dimensional modeling, building conformed dimensions across data marts, handling slowly changing dimensions, and designing descriptive dimension tables.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
This document outlines a course on data warehousing and data mining. It introduces key concepts like relational databases, data warehouses, dimensional modeling, and data mining techniques. It also details the course objectives, schedule, assignments, and policies. The goal is for students to gain experience applying data mining methods and understanding the relationship between data mining and other fields.
Data warehouse-dimensional-modeling-and-designSarita Kataria
This document provides an overview of data warehousing, dimensional modeling, and online analytical processing (OLAP). It defines key concepts in data warehousing like the data mart, metadata, cube, extraction transformation and loading (ETL), and data mining. Dimensional modeling is presented as an important technique for data warehouse design that uses facts, dimensions, and star or snowflake schemas. Finally, the document discusses OLAP features like multidimensional views and time intelligence, and different OLAP system types including multidimensional, relational, and hybrid OLAP.
1. The document discusses methodological approaches for data warehousing projects, including conceptual design using the dimensional fact model and logical design using star schemas.
2. It compares top-down and bottom-up approaches, noting that bottom-up incrementally builds data marts and is lower cost but may provide only a partial view, while top-down provides a complete picture but is higher cost with long implementations.
3. The document also discusses supply-driven and demand-driven design methodologies, noting the pros and cons of each approach depending on the availability of data sources and user requirements.
This document provides a project report on data warehousing. It includes an abstract describing data warehousing and how it transforms operational databases into informational warehouses for analysis. It also describes the introduction, background, architecture, advantages, and conclusion of data warehousing. The report is submitted by Sana Alvi and includes references.
This document introduces an online course on data warehousing from Edureka. It provides an overview of key topics that will be covered in the course, including what a data warehouse is, its architecture, the ETL process, and modeling dimensions and facts. It also shows examples of using PostgreSQL to create tables and Talend to populate them as part of a hands-on project in the course. The course modules will cover data warehousing introduction, dimensions and facts, normalization, modeling, ETL concepts, and a project building a data warehouse using Talend.
The document defines and describes key concepts related to data warehousing. It provides definitions of data warehousing, data warehouse features including being subject-oriented, integrated, and time-variant. It discusses why data warehousing is needed, using scenarios of companies wanting consolidated sales reports. The 3-tier architecture of extraction/transformation, data warehouse storage, and retrieval is covered. Data marts are defined as subsets of the data warehouse. Finally, the document contrasts databases with data warehouses and describes OLAP operations.
This document provides an overview of data mining and data warehousing. It discusses the history and evolution of databases from the 1960s to today. Data mining is defined as using automated tools to extract hidden patterns from large databases to address the problem of data explosion. Descriptive and predictive models are used in data mining. Data warehousing involves integrating data from multiple sources into a centralized database to support analysis and decision making.
This document provides an overview of big data adoption and analytics technologies. It discusses prerequisites for organizations adopting big data such as data governance frameworks and skillsets. It also outlines the typical big data analytics lifecycle including stages like data identification, analysis, and utilization of results. Finally, it describes various enterprise technologies that support big data analytics like extract-transform-load (ETL) processes, data warehouses, online transaction processing (OLTP), and online analytical processing (OLAP).
The document provides an introduction to data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-varying, and non-volatile collection of data used for organizational decision making. It describes key characteristics of a data warehouse such as maintaining historical data, facilitating analysis to improve understanding, and enabling better decision making. It also discusses dimensions, facts, ETL processes, and common data warehouse architectures like star schemas.
Gulabs Ppt On Data Warehousing And Mininggulab sharma
The document provides an overview of data warehousing, decision support, and OLAP. It discusses how a data warehouse can integrate data from various operational sources to provide a single point of access for analysis. It also compares the differences between operational databases designed for transactions versus data warehouses designed for analytics and decision making. Key points covered include data extraction, transformation and loading into the warehouse, as well as refresh strategies to propagate changes from source systems.
This document provides an introduction to data warehousing. It discusses why data warehouses are used, as they allow organizations to store historical data and perform complex analytics across multiple data sources. The document outlines common use cases and decisions in building a data warehouse, such as normalization, dimension modeling, and handling changes over time. It also notes some potential issues like performance bottlenecks and discusses strategies for addressing them, such as indexing and considering alternative data storage options.
(1) The document discusses data warehousing, business intelligence, and their relationship to addressing challenges from multiple data sources.
(2) A layered scalable architecture is presented as a reference architecture for data warehouses to provide reliable, consistent, and understandable data from different source systems.
(3) Big data is also discussed in relation to data warehousing, noting differences in schema and consistency needs between traditional warehouses and big data systems handling high volumes and varieties of data.
This document discusses multidimensional databases and provides comparisons to relational databases. It describes how multidimensional databases are optimized for data warehousing and online analytical processing (OLAP) applications. Key aspects covered include dimensional modeling using star and snowflake schemas, data storage in cubes with dimensions and members, and performance benefits of multidimensional databases for interactive analysis of large datasets to support decision making.
This document discusses data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used for analysis and decision making. A key aspect of data warehousing is the multidimensional data model which organizes data into cubes with facts and dimensions for analysis. Common schemas include star schemas with dimensions connected to a central fact table and snowflake schemas which normalize dimensional hierarchies.
The document describes a proposed data warehouse for a collection agency. It would help the agency identify profitable clients and account types, measure employee performance, and make better strategic decisions. The data warehouse would collect, categorize, and analyze data on accounts, clients, collection methods, employees, and more. Key performance indicators would measure costs and revenues by category, compare employee collections to targets, and analyze revenues by geography to guide business growth.
The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.
Lecture 04 - Granularity in the Data Warehousephanleson
This chapter discusses the importance of determining the proper level of granularity, or level of detail, for data in a data warehouse. It notes that granularity affects all dependent systems and should begin with estimates of data volumes. Feedback from users is also important for refining granularity over time. The chapter provides examples of different levels of granularity needed in various banking data and how the data warehouse must support the lowest level required by any dependent data marts. Proper granularity design is vital for success of the overall architecture.
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
Basic Introduction of Data Warehousing from Adiva Consultingadivasoft
This document provides an overview of Hyperion Essbase & Planning Training. It discusses key concepts like raw data transformation into information, online transaction processing (OLTP) systems, challenges with current data management, the purpose of data warehousing and data marts. It also covers dimensional modeling best practices, types of fact and dimension tables, and how Essbase is tuned for analysis and provides advantages over traditional databases for analytics.
This document discusses the basics of data integration. It covers concepts like ETL (extract, transform, load), data mapping, data staging, data extraction, transformation, and loading. It also discusses metadata and its types, data quality, and data profiling concepts. The key objectives are to understand data integration approaches, metadata, data quality, and perform data cleaning/profiling. The document is from a chapter about data integration in the textbook "Fundamentals of Business Analytics".
The document discusses building a data warehouse in SQL Server. It provides an agenda that covers topics like an overview of data warehousing, data warehouse design, dimension and fact tables, and physical design. It also discusses components of a data warehousing solution like the data warehouse database, ETL processes, and security considerations.
Data warehouse-dimensional-modeling-and-designSarita Kataria
This document provides an overview of data warehousing, dimensional modeling, and online analytical processing (OLAP). It defines key concepts in data warehousing like the data mart, metadata, cube, extraction transformation and loading (ETL), and data mining. Dimensional modeling is presented as an important technique for data warehouse design that uses facts, dimensions, and star or snowflake schemas. Finally, the document discusses OLAP features like multidimensional views and time intelligence, and different OLAP system types including multidimensional, relational, and hybrid OLAP.
1. The document discusses methodological approaches for data warehousing projects, including conceptual design using the dimensional fact model and logical design using star schemas.
2. It compares top-down and bottom-up approaches, noting that bottom-up incrementally builds data marts and is lower cost but may provide only a partial view, while top-down provides a complete picture but is higher cost with long implementations.
3. The document also discusses supply-driven and demand-driven design methodologies, noting the pros and cons of each approach depending on the availability of data sources and user requirements.
This document provides a project report on data warehousing. It includes an abstract describing data warehousing and how it transforms operational databases into informational warehouses for analysis. It also describes the introduction, background, architecture, advantages, and conclusion of data warehousing. The report is submitted by Sana Alvi and includes references.
This document introduces an online course on data warehousing from Edureka. It provides an overview of key topics that will be covered in the course, including what a data warehouse is, its architecture, the ETL process, and modeling dimensions and facts. It also shows examples of using PostgreSQL to create tables and Talend to populate them as part of a hands-on project in the course. The course modules will cover data warehousing introduction, dimensions and facts, normalization, modeling, ETL concepts, and a project building a data warehouse using Talend.
The document defines and describes key concepts related to data warehousing. It provides definitions of data warehousing, data warehouse features including being subject-oriented, integrated, and time-variant. It discusses why data warehousing is needed, using scenarios of companies wanting consolidated sales reports. The 3-tier architecture of extraction/transformation, data warehouse storage, and retrieval is covered. Data marts are defined as subsets of the data warehouse. Finally, the document contrasts databases with data warehouses and describes OLAP operations.
This document provides an overview of data mining and data warehousing. It discusses the history and evolution of databases from the 1960s to today. Data mining is defined as using automated tools to extract hidden patterns from large databases to address the problem of data explosion. Descriptive and predictive models are used in data mining. Data warehousing involves integrating data from multiple sources into a centralized database to support analysis and decision making.
This document provides an overview of big data adoption and analytics technologies. It discusses prerequisites for organizations adopting big data such as data governance frameworks and skillsets. It also outlines the typical big data analytics lifecycle including stages like data identification, analysis, and utilization of results. Finally, it describes various enterprise technologies that support big data analytics like extract-transform-load (ETL) processes, data warehouses, online transaction processing (OLTP), and online analytical processing (OLAP).
The document provides an introduction to data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-varying, and non-volatile collection of data used for organizational decision making. It describes key characteristics of a data warehouse such as maintaining historical data, facilitating analysis to improve understanding, and enabling better decision making. It also discusses dimensions, facts, ETL processes, and common data warehouse architectures like star schemas.
Gulabs Ppt On Data Warehousing And Mininggulab sharma
The document provides an overview of data warehousing, decision support, and OLAP. It discusses how a data warehouse can integrate data from various operational sources to provide a single point of access for analysis. It also compares the differences between operational databases designed for transactions versus data warehouses designed for analytics and decision making. Key points covered include data extraction, transformation and loading into the warehouse, as well as refresh strategies to propagate changes from source systems.
This document provides an introduction to data warehousing. It discusses why data warehouses are used, as they allow organizations to store historical data and perform complex analytics across multiple data sources. The document outlines common use cases and decisions in building a data warehouse, such as normalization, dimension modeling, and handling changes over time. It also notes some potential issues like performance bottlenecks and discusses strategies for addressing them, such as indexing and considering alternative data storage options.
(1) The document discusses data warehousing, business intelligence, and their relationship to addressing challenges from multiple data sources.
(2) A layered scalable architecture is presented as a reference architecture for data warehouses to provide reliable, consistent, and understandable data from different source systems.
(3) Big data is also discussed in relation to data warehousing, noting differences in schema and consistency needs between traditional warehouses and big data systems handling high volumes and varieties of data.
This document discusses multidimensional databases and provides comparisons to relational databases. It describes how multidimensional databases are optimized for data warehousing and online analytical processing (OLAP) applications. Key aspects covered include dimensional modeling using star and snowflake schemas, data storage in cubes with dimensions and members, and performance benefits of multidimensional databases for interactive analysis of large datasets to support decision making.
This document discusses data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used for analysis and decision making. A key aspect of data warehousing is the multidimensional data model which organizes data into cubes with facts and dimensions for analysis. Common schemas include star schemas with dimensions connected to a central fact table and snowflake schemas which normalize dimensional hierarchies.
The document describes a proposed data warehouse for a collection agency. It would help the agency identify profitable clients and account types, measure employee performance, and make better strategic decisions. The data warehouse would collect, categorize, and analyze data on accounts, clients, collection methods, employees, and more. Key performance indicators would measure costs and revenues by category, compare employee collections to targets, and analyze revenues by geography to guide business growth.
The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.
Lecture 04 - Granularity in the Data Warehousephanleson
This chapter discusses the importance of determining the proper level of granularity, or level of detail, for data in a data warehouse. It notes that granularity affects all dependent systems and should begin with estimates of data volumes. Feedback from users is also important for refining granularity over time. The chapter provides examples of different levels of granularity needed in various banking data and how the data warehouse must support the lowest level required by any dependent data marts. Proper granularity design is vital for success of the overall architecture.
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
Basic Introduction of Data Warehousing from Adiva Consultingadivasoft
This document provides an overview of Hyperion Essbase & Planning Training. It discusses key concepts like raw data transformation into information, online transaction processing (OLTP) systems, challenges with current data management, the purpose of data warehousing and data marts. It also covers dimensional modeling best practices, types of fact and dimension tables, and how Essbase is tuned for analysis and provides advantages over traditional databases for analytics.
This document discusses the basics of data integration. It covers concepts like ETL (extract, transform, load), data mapping, data staging, data extraction, transformation, and loading. It also discusses metadata and its types, data quality, and data profiling concepts. The key objectives are to understand data integration approaches, metadata, data quality, and perform data cleaning/profiling. The document is from a chapter about data integration in the textbook "Fundamentals of Business Analytics".
The document discusses building a data warehouse in SQL Server. It provides an agenda that covers topics like an overview of data warehousing, data warehouse design, dimension and fact tables, and physical design. It also discusses components of a data warehousing solution like the data warehouse database, ETL processes, and security considerations.
Big Data Warehousing Meetup: Dimensional Modeling Still Matters!!!Caserta
Joe Caserta went over the details inside the big data ecosystem and the Caserta Concepts Data Pyramid, which includes Data Ingestion, Data Lake/Data Science Workbench and the Big Data Warehouse. He then dove into the foundation of dimensional data modeling, which is as important as ever in the top tier of the Data Pyramid. Topics covered:
- The 3 grains of Fact Tables
- Modeling the different types of Slowly Changing Dimensions
- Advanced Modeling techniques like Ragged Hierarchies, Bridge Tables, etc.
- ETL Architecture.
He also talked about ModelStorming, a technique used to quickly convert business requirements into an Event Matrix and Dimensional Data Model.
This was a jam-packed abbreviated version of 4 days of rigorous training of these techniques being taught in September by Joe Caserta (Co-Author, with Ralph Kimball, The Data Warehouse ETL Toolkit) and Lawrence Corr (Author, Agile Data Warehouse Design).
For more information, visit http://casertaconcepts.com/.
The document discusses an overview of enterprise data governance. It describes the goals of data governance as making data usable, consistent, open, available and reliable across an organization. It outlines the roles and responsibilities involved in data governance including an oversight committee, data stewards, data custodians and various initiatives around master data management, data quality, naming conventions, metadata management and more. The document also discusses why organizations implement data governance and how to effectively implement a data governance program.
Data warehousing is an architectural model that gathers data from various sources into a single unified data model for analysis purposes. It consists of extracting data from operational systems, transforming it, and loading it into a database optimized for querying and analysis. This allows organizations to integrate data from different sources, provide historical views of data, and perform flexible analysis without impacting transaction systems. While implementation and maintenance of a data warehouse requires significant costs, the benefits include a single access point for all organizational data and optimized systems for analysis and decision making.
Spatial Network Inc. Data Management and Transformation with FMESafe Software
Spatial Networks geospatial data assets fit the gambit of Human Geography domains and Geospatial Intelligence, with a contextually relevant and intimate understanding from local professional experts collecting data on the ground. We are transforming this data into insightful analytics and rich, robust data sets of 21 unique data types to help our clients solve challenging geospatial problems. FME has helped us to streamline our workflows and automate several processes along our data pipeline. We look to scale our operations significantly in 2018 and FME will ease many of our challenges as we move forward.
This document provides an overview of data warehousing. It defines a data warehouse as a subject-oriented, integrated collection of data used to support management decision making. The benefits of data warehousing include high returns on investment and increased productivity. A data warehouse differs from an OLTP system in its design for analytics rather than transactions. The typical architecture includes data sources, an operational data store, warehouse manager, query manager and end user tools. Key components are extracting, cleaning, transforming and loading data, and managing metadata. Data flows include inflows from sources and upflows of summarized data to users.
- Data warehousing aims to help knowledge workers make better decisions by integrating data from multiple sources and providing historical and aggregated data views. It separates analytical processing from operational processing for improved performance.
- A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data to support analysis. It is maintained separately from operational databases. Common schemas include star schemas and snowflake schemas.
- Online analytical processing (OLAP) supports ad-hoc querying of data warehouses for analysis. It uses multidimensional views of aggregated measures and dimensions. Relational and multidimensional OLAP are common architectures. Measures are metrics like sales, and dimensions provide context like products and time periods.
Various Applications of Data Warehouse.pptRafiulHasan19
The document discusses various applications of data warehousing. It begins by describing problems with traditional transactional systems and how data warehouses address these issues. It then defines key components of a data warehouse including the extraction, transformation, and loading of data from various sources. The document outlines how online analytical processing (OLAP) tools, metadata repositories, and data mining techniques analyze and explore the collected data. Finally, it weighs the benefits of a data warehouse against the costs of implementation and maintenance.
A data warehouse is a collection of integrated data from multiple sources organized to support management decision making. It contains subject-oriented, integrated, time-variant and non-volatile data stored in a way that is optimized for query and analysis. There are different types of data warehouses including data marts, operational data stores and enterprise data warehouses. Key components of a data warehouse include data sources, extraction, loading, a comprehensive database, metadata and middleware tools.
The document discusses database management and data resource management. It covers logical data elements, database structures, types of databases, and the advantages of database management over traditional file processing. Database management software helps businesses by allowing data to be accessed and maintained in an integrated way. It also discusses database development, interrogation, maintenance, and application development functions performed with database management systems.
A data warehouse is a collection of data integrated from multiple sources to support decision making. It contains subject-oriented, integrated, time-variant, and non-volatile data stored in a way that makes it readily available for analysis. Data marts can be dependent on the warehouse or independent subsets designed for specific departments. Successful implementation requires identifying data sources and governance, planning data quality and modeling, selecting ETL and database tools, and supporting end users. Key challenges include unrealistic expectations, technical issues, and ensuring ongoing value.
Ray Scott - Agile Solutions – Leading with Test Data Management - EuroSTAR 2012TEST Huddle
Ray Scott discusses test data management in agile environments. He notes that while development may be agile, supporting test data often cannot keep up with frequent changes. Traditional test data generation methods take weeks but agile needs data in hours. He advocates treating test data management as a development project and service. Testers should own the data by determining usage, mapping test conditions to data conditions, and ensuring versioning. With solid data provisioning focusing on business rules and repeatability, testing can add value in agile projects.
Data it's big, so, grab it, store it, analyse it, make it accessible...mine, warehouse and visualise...use the pictures in your mind and others will see it your way!
The document discusses ETL (extraction, transformation, and loading) processes which are used to update data warehouses. It describes two common data warehousing strategies, the enterprise-wide and data mart approaches. The document also discusses recent developments in ETL, including more frequent updates, handling of clickstream data, challenges with dirty or inconsistent source data, and the importance of metadata.
This presentation is about the following points,
1. Introduction to ETL testing,
2. What is use of testing
3. What is quality & standards
4. Responsibilities of a ETL Tester
This document discusses using machine learning techniques like clustering and decision trees to analyze crime data from Chicago between 2014-2016. It aims to identify crime hot spots and patterns to help police allocate resources more efficiently. The document applies k-means clustering to crime data grouped by location and type, identifying a "vice" cluster with crimes like prostitution and drugs in two adjacent wards. It suggests police could use temporal and hourly crime patterns from the analysis to optimize staff scheduling and deployment. The document also discusses using decision trees and k-nearest neighbors algorithms on the crime data supplemented with temperature and unemployment data to further explore crime patterns.
The Prepared Executive: A Linguistic ExplorationTom Donoghue
This document provides an abstract for a research project that analyzes executive answers during the question and answer section of corporate earnings calls. It aims to explore linguistic features in executive answers to see if they can indicate the executive's level of preparedness. The research will examine features of uncertainty, avoidance, and repetition in a sample of earnings call transcripts from the drinks industry. A domain expert will provide labels for a subset of executive answers to use as a baseline for comparison. Models using word lists and document similarity techniques will be developed and evaluated to see if they can accurately detect these linguistic features and determine an executive's preparedness. The results will help uncover new aspects of "executive speak" and company communication strategies.
Crime Analysis using Regression and ANOVATom Donoghue
A statistical analysis of damage to property using a predictive regression model. Also an investigation to ascertain possible differences in reported divisional burglary rates using ANOVA.
Exploration of Call Transcripts with MapReduce and Zipf’s LawTom Donoghue
This study implements a proof of concept
pipeline to capture web based call transcripts and produces
a word frequency dataset ready for textual analysis
This document summarizes challenges in processing data from the growing Internet of Things (IoT). It discusses how the large volume and uneven frequency of data from heterogeneous IoT devices can overwhelm cloud infrastructure. It reviews literature on using distributed computing approaches like fog computing to help address these issues by bringing computation and storage closer to where data is generated at the network edge. Fog computing could help with data locality, initial processing, and partitioning data to relieve strain on centralized cloud systems as more IoT devices generate data.
This paper describes the concept of a data lake and how it compares to a data warehouse. A review recent research and discussion of the definition of both repositories, what types of data are catered for? Does ingesting data make it available for forging information and beyond
into knowledge? What types of people, process and tools need to be involved to realise the
benefits of using a data lake?
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
2. Data Warehouse
• Business user friendly stories about past events (including near time)
• Designed to support decision making
• Serves a digest of answers in grouped and aggregated ways
• More meaningful and therefore more important to the business
• Ingests data from disparate sources which need to be merged to
enable business friendly queries
3. Data Warehouse Definition
• A consolidating bolt-on to existing operational systems
• Structured data associated with a specific user base and a specific set of
predefined business queries
• The data schema is predefined and structured to facilitate regular and ad-
hoc queries
• Populating the data warehouse requires multiple ETL processes designed in
advance
• Halts the proliferation of reports
O'Leary (2014)
4. Data Warehouse Basic Architecture
ETL Staging Area
Source
Data
Data
Warehouse
Business Users
Source
Data
Source
Data
Operational Data Soures Data Preparation Business Queries
5. Data Warehouse Requirements
• Organisational Data is easy to access
• Information is presented consistently
• Adaptive and resilient to change
• Secure
• Serves as a base for improved decision making
• Accepted by the business community
(Kimball, 2002)
6. Machine Learning
• A Data warehouse provides historic information for decision making
• Machine Learning uses algorithms to process features in the data to
learns patterns, make predictions and solution outcomes
• Image recognition, Classification, Forecasting, Anomaly detection
• Learning is Supervised (labelled with the desired outcome) or
Unsupervised (unlabelled, the model learns unaided)
7. Machine Learning - Supervised
• A predictive model is trained using a labelled training data set and the
outcome evaluated on its performance
• The model is tweaked to improve performance
• The model is then run against a test data set which is unlabelled and
evaluated on its performance in identifying the correct label
• Examples:
• k-Nearest Neighbours
• Linear and Logistical Regression
• Decision Trees
• Support Vector Machines
(Lantz, 2015)
8. Machine Learning - Unsupervised
• The training data set is unlabelled
• The descriptive model is trained and evaluated on its performance
• Examples:
• Clustering - k-Means
• Association Rules
• Natural Language Processing
(Lantz, 2015)
9. Machine Learning an Extension to Data
Warehousing
• Much of the hard work to cleanse and transform data has been
accomplished
• Ask the Business Question – what is the objective? Is it descriptive or
predictive?
• Does the data contain the desired features?
• Is further data transformation required
• Which ML algorithm is optimal for answering the question?
• Iterative approach assessing and evaluating model(s) performance
• Present the Solution
10. References
• Kimball, R., Ross, M., Thornthwaite, W., Mundy. J and Becker, B. (2008) The data warehouse lifecycle toolkit.
2nd ed. Indianapolis: Wiley Publishing, Inc.
• Lantz, B. (2015). Machine Learning with R, 2nd edn, Birmingham: Packt.
• O'Leary, D. E. (2014), ‘Embedding AI and Crowdsourcing in the Big Data Lake’, IEEE Intelligent Systems,
Volume 29, Issue 5, pp. 70-73.