Co-Author: Saunnie Bell, Technical Manager, Oracle Consulting Background This blog is part of a series covering topics on how to utilize Oracle Analytics / ETL...
Co-Author: Saunnie Bell, Technical Manager, Oracle Consulting Background This blog is part of a series covering topics on how to utilize Oracle Analytics / ETL / Data Integration tools to extract data from various data sources. Future blog topics will cover extracting from: Oracle SaaS Applications, Oracle on-premise applications (e.g. EBS), and other Oracle applications such as Responsys, Eloqua and third party data sources like Salesforce or Workday. The first blog on...
Co-Author: Saunnie Bell, Technical Manager, Oracle Consulting Background This blog is part of a series covering topics on how to utilize Oracle Analytics / ETL / Data Integration tools to extract...
Learn use cases for Natural Language Processing and practical steps for how to deploy an NLP environment for your own machine learning.
Required reading time: < 15 minutes Introduction In continuation of my blog on Fusion applications cloud instance strategy and Global Single Instance...
Required reading time: < 15 minutes Introduction In continuation of my blog on Fusion applications cloud instance strategy and Global Single Instance guidelines, I wanted to share my experience on Fusion Applications multi-pillar implementation. As Oracle's Fusion SaaS applications cloud is built on a single unified data model with a consistent user and developer experience that connects end-to-end business processes across modules (Human Capital Management (HCM), Enterprise...
Required reading time: < 15 minutes Introduction In continuation of my blog on Fusion applications cloud instance strategy and Global Single Instance guidelines, I wanted to share my experience on...
Managing product descriptions with Oracle Enterprise Data Quality Many organisations purchase the same products from multiple suppliers and commonly ask the...
Managing product descriptions with Oracle Enterprise Data Quality Many organisations purchase the same products from multiple suppliers and commonly ask the suppliers to provide data as to the manufacturer, description, part number etc. Unfortunately each supplier usually has their own format for the data they supply and no way to convert the content into a format specified or required by the buyer. Oracle Enterprise Data Quality provides a range of tools and techniques for...
Managing product descriptions with Oracle Enterprise Data Quality Many organisations purchase the same products from multiple suppliers and commonly ask the suppliers to provide data as to...
While exploring natural language processing (NLP) and various ways to classify text data, I wanted a way to test multiple classification algorithms and chains...
While exploring natural language processing (NLP) and various ways to classify text data, I wanted a way to test multiple classification algorithms and chains of data processing, and perform hyperparameter tuning on them, all at the same time. I ended up using Apache Spark with the CrossValidator and pipeline models. This article will detail the approach. Import statements. Nothing will be detailed here. These are just provided to make running this yourself simple. from...
While exploring natural language processing (NLP) and various ways to classify text data, I wanted a way to test multiple classification algorithms and chains of data processing, and perform...
Last validation: September 30, 2020 with 12.2.1.4.0 Introduction This post details the steps required to create connections in Oracle Data Integrator (ODI)...
Last validation: September 30, 2020 with 12.2.1.4.0 Introduction This post details the steps required to create connections in Oracle Data Integrator (ODI) Marketplace to Autonomous Databases on Dedicated Infrastructure (ADB-D) e.g. Autonomous Data Warehouse (ADW-D). This post uses ADW-D, but the method is also applicable to Autonomous Transaction Processing (ATP-D) databases. For additional details about ADW-D, visit Using Oracle Autonomous Data Warehouse on...
Last validation: September 30, 2020 with 12.2.1.4.0 Introduction This post details the steps required to create connections in Oracle Data Integrator (ODI) Marketplace to Autonomous Databases on...
Introduction This post will compare vectorizing word data using term frequency-inverse document frequency (TF-IDF) in several python implementations. TF-IDF is...
Introduction This post will compare vectorizing word data using term frequency-inverse document frequency (TF-IDF) in several python implementations. TF-IDF is used in the natural language processing (NLP) area of artificial intelligence to determine the importance of words in a document and collection of documents, A.K.A. corpus. Various implementations of TF-IDF were tested in python to gauge how they would perform against a large set of data. Tested were sklearn, gensim...
Introduction This post will compare vectorizing word data using term frequency-inverse document frequency (TF-IDF) in several python implementations. TF-IDF is used in the natural language processing...
Last Validation: October 23, 2020 with OAC 5.8 Introduction This post details the steps required to use an advanced JDBC driver in Remote Data Gateway (RDG)...
Last Validation: October 23, 2020 with OAC 5.8 Introduction This post details the steps required to use an advanced JDBC driver in Remote Data Gateway (RDG) to support private Autonomous Database (ADB) data connections in an Oracle Analytics Cloud (OAC) RPD. ADB refers to both Autonomous Data Warehouse and Autonomous Transaction Processing databases. It supplements the post on Connecting an Oracle Analytics Cloud RPD to Private Autonomous Databases For additional details on...
Last Validation: October 23, 2020 with OAC 5.8 Introduction This post details the steps required to use an advanced JDBC driver in Remote Data Gateway (RDG) to support private Autonomous Database...
Introduction In the course of prepping some data for a machine learning activity using natural language processing (NLP), several methods were used to compare...
Introduction In the course of prepping some data for a machine learning activity using natural language processing (NLP), several methods were used to compare the performance and volume of data that could be efficiently processed. This post will show the performance of cleaning a small set, and a larger set of data. All examples are in python, and compare the use of Pandas dataframes, Dask dataframes, and Apache Spark (pyspark). Test environments The small dataset was...
Introduction In the course of prepping some data for a machine learning activity using natural language processing (NLP), several methods were used to compare the performance and volume of data that...
