IBM Big Data Hub

MAY 1, 2023

I love the game of chess and was shocked when IBM’s Deep Blue chess-playing machine defeated the world chess champion in 1997. That ground-shaking event divided the world with excitement and trepidation about a future with thinking machines. In the late 18 th century, The Turk, a chess playing machine captured the attention of the world.

Insurance 83

Insurance Predictive Modeling Cost-Benefit Statistics 83

IBM Big Data Hub

APRIL 28, 2022

Data agility, the ability to store and access your data from wherever makes the most sense, has become a priority for enterprises in an increasingly distributed and complex environment. That’s where the data fabric comes in. Consequently, a data fabric self-manages and automates data discovery, governance and consumption, which enables.

Metadata 85

Metadata Data Warehouse Forecasting Predictive Modeling 85

Sisense

JANUARY 21, 2020

However, to further explore the data, you decide to break down the distribution of likes between men and women: Liked ginger Liked sugar cookie Men 12/37 = 32.4% In addition, we can see that the distribution of men and women surveyed is unbalanced. Here are the results: Liked the ginger Liked the sugar cookie 62/100 = 62% 54/100 = 54%.

Testing 104

Testing Risk Data-driven Statistics 104

Cloudera

APRIL 12, 2021

Enterprise-level IT transformation provides uniformity and saving as well as enabling new capabilities for the entire workforce. Enterprise-level IT transformation provides uniformity and saving as well as enabling new capabilities for the entire workforce. It dramatically increased economies of scale and boosted buying power.

Enterprise 52

Enterprise IT Digital Transformation Data Warehouse 52

BizAcuity

MARCH 29, 2019

To get an in-depth knowledge of the practices mentioned above please refer to the blog on Oracle’s webpage. Data can be stored as-is, without first structuring it, and different types of analytics can be run on it, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to improve decision making.

Data Lake 102

Data Lake Unstructured Data Cost-Benefit Data Quality 102

Ontotext

NOVEMBER 16, 2023

A mesh approach helps distribute data ownership and reduce dependencies between services, creating an environment that promotes data-driven thinking. Data mesh is still in its infancy, and data personas and organizations are craving clarity and specificity. The debate on what constitutes a data mesh rages on.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Darkhorse

MARCH 15, 2014

So, after some python scraping and excel manipulating we find ourselves with a paradox: uniform distributions that aren't uniform distributions. So, after some python scraping and excel manipulating we find ourselves with a paradox: uniform distributions that aren't uniform distributions.

IT 40

IT Visualization 40

The Unofficial Google Data Science Blog

JANUARY 16, 2018

by SANGHO YOON In this article, we discuss an approach to the design of experiments in a network. In particular, we describe a method to prevent potential contamination (or inconsistent treatment exposure) of samples due to network effects. We first describe the unique challenges in designing experiments on developers working on GCP.

Testing 58

Testing Experimentation Measurement Modeling 58

The Unofficial Google Data Science Blog

AUGUST 27, 2019

But given the low probability of violation and wanting to use our rater capacity wisely, this is not an adequate solution — we typically have too few positive labels in uniform samples to achieve an accurate estimate of the prevalence, especially for those sensitive policy verticals.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

IBM Big Data Hub

APRIL 5, 2024

Part 2 of this four-part series discusses the complex tasks energy utility companies face as they shift to holistic grid asset management to manage through the energy transition. The first post of this series addressed the challenges of the energy transition with holistic grid asset management. Asset data is the basis for the network.

Management 71

Management Risk Optimization Predictive Modeling 71

The Unofficial Google Data Science Blog

JUNE 30, 2016

The second tries to balance the distribution of predictors evenly between the treatment and control groups. The second tries to balance the distribution of predictors evenly between the treatment and control groups. In observational studies treatment is assigned by nature, therefore its mechanism is unknown and needs to be estimated.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

JULY 22, 2020

However, if one changes assignment weights when there are time-based confounders, then ignoring this complexity can lead to biased inference in an OCE. In the case of MABs, ignoring this complexity can also lead to poor total reward, making it counterproductive towards its intended purpose.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

JULY 17, 2018

In this blog post we describe one of these instances — Google search deciding when to check if web pages have changed. by BILL RICHOUX Critical decisions are being made continuously within large software systems. Often such decisions are the responsibility of a separate machine learning (ML) system.

Data Science 85

Data Science Snapshot Data Processing Optimization 85

Sisense

JUNE 26, 2020

As datasets become bigger and more complex, only AI, materialized views, and more sophisticated coding languages will be able to glean insights from them. In Next-Level Moves , we dig into the ways advanced analytics are paving the way for the next wave of innovation. Visual processing and responses both occur more quickly compared to other stimuli.

Visualization 97

Visualization Unstructured Data Measurement Data-driven 97

Cloudera

AUGUST 7, 2023

Introduction The snapshots feature of the Apache Hadoop Distributed Filesystem ( HDFS) enables you to capture point-in-time copies of the file system and protect your important data against corruption, user-, or application errors. Using snapshots to protect data is efficient for a few reasons. on that file/directory. on that file/directory.

Snapshot 78

Snapshot IT 78

Domino Data Lab

APRIL 21, 2021

In this blog post we will show an implementation of an ML-based anomaly detection based on XGBoost. In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. has made this approach unfeasible.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Data Visualisation Catalogue

JANUARY 1, 2019

In the example below, the stacked bars have been fixed to have a uniform total width as a way of displaying part-to-a-whole relationship at each node. When combining an Arc Diagram with another form of data visualization, the additional graph markers can be placed on or aligned with the connecting nodes of an Arc Diagram. Bar Chart.

Visualization 45

Visualization Marketing IT 45

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. An Amazon S3 Lifecycle configuration is a set of rules that define actions that Amazon S3 applies to a group of objects. With the s3.delete.tags

Data Lake 81

Data Lake Snapshot Metadata Optimization 81

The Data Visualisation Catalogue

MARCH 25, 2024

Rather than all bars being of uniform width, as in conventional bar charts, the width of the bars in a Variable Width Bar Chart is determined by an additional dimension within the dataset. In essence, each bar in the chart can represent a specific category, such as different product lines or demographic groups.

Snapshot 79

Snapshot Visualization Sales IT 79

Cloudera

AUGUST 4, 2022

In a previous blog post , we demonstrated the power of Parquet page indexes, which can greatly improve the performance of selective queries. A table stored in a distributed file system typically has partition columns and data columns. This article reveals how Z-ordering works and how one can use it with Apache Impala.

IoT 103

IoT Measurement Statistics Cost-Benefit 103

Jedox

MARCH 11, 2021

In this blog post, we’ll look at data silos, how they emerge and the problems they can cause within an organization. We’ll also discuss some approaches to resolving the silos, and most importantly, why it’s vital to success in the long run. They are contrary to the approach of a data warehouse.

Sales 80

Sales Optimization Digital Transformation Data Warehouse 80

CIO Business Intelligence

OCTOBER 23, 2023

In this blog, we’ll highlight the five key principles of our Data Center of the Future initiative, and look at how each supports the goals of making our operations more sustainable and unlocking business value for our customers. The theme for Earth Day 2022 was “Invest in Our Planet.”

Testing 59

Testing Reporting Software Technology 59

The Data Visualisation Catalogue

OCTOBER 18, 2021

For ages, the naming between Sankey Diagrams , Parallel Sets , and Alluvial Diagrams have been used interchangeably. Are these visualisations that different from one another and is it a bad thing that some misnaming is taking place? So to return to our three charts in question, why are they often confused with one another? Sankey Diagrams.

Visualization 89

Visualization IT 89

Cloudera

AUGUST 14, 2020

Although we have designed this fault injection framework for Ozone, it is generic enough to be used for validating any other distributed and scalable system. . Although we have designed this fault injection framework for Ozone, it is generic enough to be used for validating any other distributed and scalable system. .

Testing 96

Testing Metadata Data Processing IT 96

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But doing this can be hard, for reasons not typically encountered in problems with smaller or less critical regression systems. In this post, we describe the challenges posed by one problem — how to get approximate posteriors — and an approach that we have found useful. All these problems can be phrased as large-scale regressions.

KDD 40

KDD Testing Machine Learning Modeling 40

Cloudera

NOVEMBER 13, 2020

This is part of our series of blog posts on recent enhancements to Impala. The entire collection is available here. Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? What if our queries are very selective? Impala Optimizations for Small Queries. Query Planner Design.

Optimization 142

Optimization Metadata Statistics Cost-Benefit 142

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment. The interval [-a, a] is called a 90% confidence interval.

Statistics 27

Statistics Uncertainty Risk Marketing 27

Ontotext

APRIL 25, 2019

In this article you will read why and how SPARQL queries make for a better search and are of immense help when it comes to accessing all the independently designed and maintained datasets across an organization (and outside it) in an integrated way. Data, Databases and Deeds: A SPARQL Query to the Rescue. In a world of 2.5

Cost-Benefit 75

Cost-Benefit Enterprise Structured Data Data Architecture 75

Domino Data Lab

AUGUST 26, 2019

The project also covers building a pipeline for automating ML workflow and stay tuned for additional hyperparamater content on the Domino Data Science blog. This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Introduction. In [1]: # setup.

Testing 79

Testing Modeling Machine Learning Metrics 79

Darkhorse

NOVEMBER 8, 2016

Many charting taxonomies include distributions, but they only present a few options. These charts are useful in conveying the volume of observations as well as the distribution, but can quickly become messy and hard to read as the size of the data set increases. Let’s remedy that with a post on the many. Barcode Chart or Rug Plot.

Visualization 72

Visualization Experimentation Measurement Statistics 72

Darkhorse

JULY 6, 2017

You see, 80% of your working hours will be spent wrangling the data. That’s on average. On some projects, you will spend more than 100% of your “working” hours with your lasso. I hope you enjoy that sort of thing. So what is data wrangling? Let’s imagine the process of building a data lake. Don’t limit yourself to just the ERP or internal systems.

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Insight

MARCH 25, 2019

In this blog post, I discuss the design and implementation of kubeCDN , a tool designed to simplify geo-replication of Kubernetes clusters in order to deploy services with high availability on a global scale. This setup improves the UX for geographically distributed users as they see reduced latencies and packet loss.

Data Processing 106

Data Processing Cost-Benefit Optimization Metrics 106

Ontotext

APRIL 25, 2019

In a world of 2.5 quintillion bytes of data created each day, the bar for enterprise knowledge and information systems, and especially for their search functions and capabilities, is raised high. One such way towards better search (and better informed actions) is the SPARQL query. Think of all the databases an organization is operating with.

Cost-Benefit 40

Cost-Benefit Enterprise Structured Data Data Architecture 40

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. What is Named Entity Recognition? mentioned in unstructured text.

Modeling 111

Modeling Statistics Testing Metrics 111

The Unofficial Google Data Science Blog

MARCH 22, 2018

What if their uptake rate is not uniform? This blog post provides details for how we can make inferences without waiting for complete uptake. by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps. But what if users don't immediately uptake the new experimental version?

Experimentation 62

Experimentation Measurement Modeling Statistics 62

The Unofficial Google Data Science Blog

APRIL 16, 2019

In fact, this blog has published posts on this very topic. This blog post is about experimentation in this regime. by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. With the right experiment methodology, a product can make continuous improvements, as Google and others have done.

Experimentation 68

Experimentation Sales Metrics Measurement 68

Ontotext

AUGUST 4, 2023

As data is generated, stored, and used across data centers, edge, and cloud providers, managing a distributed storage environment is complex with no map to guide technology professionals. It enables data federation and virtualization as well as seamless access and sharing in a distributed data environment. Data Fabric: Who and What?

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52