Trending December 2023 # Google Updates Employer Aggregate Rating Structured Data Requirements # Suggested January 2024 # Top 13 Popular

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Google updated their developer page for job posting structured data for employer aggregate ratings. The change adds an additional required property to help qualify for rich results.

Job Posting Related Structured Data Keeps Changing

The structured data requirements for job posting seems as if it has been in a state of evolution. So it’s good to keep up with large and incremental changes to structured data requirements.

Failure to keep up with changes can result in a loss of featured snippets or other rich results.

For example, the job posting structured data used to rely on the review snippet structured data in order to qualify for rich results.

But Google later encouraged publishers to update to EmployerAggregateRating structured data and away from review structured data..

The new change is similar but narrower in scope.

What Changed in Employer Aggregate Rating Structured Data

Google updated the developer page in order to reflect changes in the Rich Results Test tool.

Google has introduced an additional required property called, reviewCount.

The reviewCount property provides Google with a count of how many aggregated reviews are on a page. This data is independent of how many ratings were provided.

So it’s possible to have twenty reviews for a product and fifteen ratings.

Here’s an example of the reviewCount structured data in a JSON-LD structured data script:

Screenshot Shows Code With reviewCount Property is Eligible for Rich Results

Mixed Rating Count and Review Ratings

The example below is for a situation where there are 561 ratings out of 10,561 total reviews, meaning there were 10,000 reviews and only 561 ratings.

Screenshot Shows that Mixed Rating and Review Count Code Qualifies for Rich Snippets

Only Required to Use at Least One Property

This does not mean you have to use both properties. You can use one or the other property. It’s up to you.

You can use the “reviewCount” property instead of the ratingCount property if you want. It still validates for Rich Results.

What’s important to understand is that publishers are not required to use both. One is enough unless the number of reviews is different than the number of ratings.

See the updated structured data requirements here:

Employer Aggregate Rating

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Google Updates Video Seo Best Practices

Google’s video SEO best practices document has been updated with a section on how to optimize mature content for SafeSearch.

SafeSearch is a setting that Google users can apply to their account which specifies whether to show or block explicit images, videos, and websites in search results.

Google requests that site owners assist the search engine with understanding the nature of their site’s content. This ensures SafeSearch settings can be applied when appropriate.

Here are the guidelines that have been added to Google’s document.

Group Adult-Only Videos in Common Location Add Metadata to Adult Pages

One of the strongest signals Google uses to determine whether a page contains a content that should be filtered by SafeSearch is metadata.

Adult videos that have been marked up with the correct metadata can help Google understand whether a whole page or a select video should be filtered when SafeSearch is turned on.

In the absence of metadata, Google looks for signals generated using machine learning, as well as simpler signals such as where the video was used previously and the context in which the video was used.

Rather than relying on Google to figure things out on its own, it recommends site owners use metadata which looks like:

Google adds:

“Many users prefer not to have adult content included in their search results (especially if kids use the same device). When you provide one of these meta tags, it helps to provide a better user experience because users don’t see results which they don’t want to or expect to see.”

Credit for the discovery of these changes to Google’s best practices document goes to Brodie Clark:

Google just added a new section to their ‘video best practices’ doc. Not an industry I work with, but maybe of interest to some.

Optimising for SafeSearch:

— Brodie Clark (@brodieseo) June 5, 2023

Google Ads Rolling Out Multiple Updates To App Campaigns

App campaigns in Google Ads can now be optimized specifically for driving as many app installs within as short a timeframe as possible.

An update coming later this year will simplify the campaign creation process with a new tool for optimizing images.

Here’s more about each of these updates to app campaigns.

Maximize Conversions Bidding in App Campaigns

Advertisers can now use maximize conversions bidding through app campaigns.

This allows app campaigns to be optimized for generating as many installs in as little time as possible.

Google strongly emphasizes the speed in which maximize conversions bidding is designed to work:

“Whether you’re releasing a new game, launching a seasonal campaign or promoting new content in your app, it’s important for you to be able to grow your audience quickly.”

Advertisers can simply enter their budget and Google’s machine learning technology will do the rest.

To get started with Maximize conversions bidding:

Select the “Bidding” section

Uncheck the box for “Set a target cost per install (optional)”

Save the changes on the campaign

Source: Google Ads Help

Simplified Image Requirements for App Campaigns

App campaigns currently accept over 30 different sizes and dimensions for image assets.

That can get complicated, so Google Ads is shifting from size-based to ratio-based image specifications.

Google says this will make the creative process simpler and more efficient.

The three aspect ratios are:

1:1 (Square): Minimum size of 200 x 200, recommended size of 1200 x 1200

1.91:1 (Landscape): Minimum size of 600 x 314, maximum size of 1200 x 628

4:5 (Portrait): Minimum size of 320 x 400, Recommended size of 1200 x 1500

Google is also increasing the image file size limit from 150KB to 5MB so assets can be uploaded in higher quality.

The approved file format for images now only include .jpg and .png. Google is disabling GIFs as an option.

All app campaigns will be migrated to this set of requirements starting early next year.

As this update gets closer to rolling out, Google Ads plans to introduce a tool for cropping images into either of the three supported aspect ratios.

The cropping tool will also be available when images are uploaded for newly created campaigns.

This tool should be available in the next few months. Here’s a mockup of what it might look like.

Source: Google Ads Help

Bu Updates Its Covid Data Dashboard, As Employee Vaccination Deadline Looms

BU Updates Its COVID Data Dashboard, as Employee Vaccination Deadline Looms

Triage nurse specialist Victoria Cunningham at the Moderna vaccine clinic at FitRec May 4. Photo by Cydney Scott

University News

BU Updates Its COVID Data Dashboard, as Employee Vaccination Deadline Looms Faculty, staff urged to get their shots and upload documentation by September 2

As Boston University transitions from summer to fall and the campus repopulates with thousands of students, faculty, and staff, BU’s COVID-19 Data Dashboard was updated Tuesday, August 17, with numbers showing a sharp increase in the community’s vaccination percentages.

The new numbers also reflect the progression over the past few months, from a time when the coronavirus pandemic had forced the vast majority of employees to work remotely to now, with more people returning to work on campus. (All employees have until September 2 to comply with BU’s vaccination mandate.)

The revised dashboard shows 92 percent of faculty and 84 percent of staff vaccinated as of August 16, up from the low 70 percent range for both groups previously.

Why did the percentages jump? Previously the dashboard reflected all faculty and staff, including those working remotely and who may not yet have been vaccinated or may have failed to upload proof of vaccination. But on Tuesday, the revised dashboard began to include only those who are coming to campus and “are a part of our testing protocol,” says Gloria Waters, BU vice president and associate provost for research.

Even though the new employee numbers are encouraging, Waters says, they are likely to bounce slightly up and down in the coming weeks for several reasons. Many employees who have worked remotely during the pandemic will return in person for the fall, and those high vaccination percentages could drop as “additional people come back to campus if they are not yet fully vaccinated, or have not yet uploaded their information or asked for an exemption,” she says.

“It is to be expected that the numbers will fluctuate over the next few weeks,” as the September 2 deadline bears down, Waters says.

As the campus repopulates for fall, watching BU’s COVID-19 Data Dashboard could put a crick in your neck, with vaccination rates expected to fluctuate with updates to the information.

Another complicating factor is that BU’s regular faculty aren’t the only employees who’ll need to be accounted for on the dashboard. “Visiting faculty and scholars are required to be compliant,” the associate provost says. “We need to be sure that departments update the data to exclude those who may have been here last year and left and those who are incoming.”

As more employees return to campus in the coming weeks and are placed into the testing Categories 1, 2, or 3 per the University’s COVID-19 safety protocols, she says, they will enter the population reported on the dashboard.

Meanwhile, with the reappearance of the giant yellow rolling bins used by incoming students as fall’s students start to arrive on campus this week, the dashboard’s student numbers also changed Tuesday. The new figures show 88 percent were vaccinated, up from 76 percent.

“Up until now, the dashboard has reflected the vaccination status for students who were in Summer Term II,” Waters says. “However, the vaccination requirement for students was not in effect until August 1, and so the summer data are not a reflection of the rates we will see in the fall. We now know which students will be on campus in the fall, and they have been required to be compliant with the vaccination requirement. The dashboard has been refreshed with the data for the students who will be on campus in the fall and so reflects a much higher rate.”

Waters adds that the old student data also included many groups that have left BU or are leaving campus—Class of 2023 graduates, for example.

The return to campus and updated numbers come as the United States grapples with COVID-19’s Delta variant, the most contagious strain so far and one that’s proven more likely to infect younger people in their 20s and 30s.

In coming weeks, Waters says, University leaders, department managers, and academic leaders will work to ensure that employees upload vaccination documentation at Healthway, comply with BU’s weekly testing requirement, and are included in the latest dashboard figures.

“As new staff are hired, they will be required to be a part of our testing program and compliant with the vaccination requirement,” Waters says. “Compliance with the vaccination requirement, as well as with weekly testing, will be monitored by managers, who will get weekly lists of people in their units who are not compliant.”

Judy Platt, director of Student Health Services and chair of the University’s Medical Advisory Group, says student numbers are expected to continue to rise as the start of the fall semester nears.

“With such a large population, we may have some students who are included in this mix, but will not actually be on campus,” Platt says. “BU was intentionally inclusive with our populations to ensure that we have consistent vaccination compliance.

Explore Related Topics:

How Search Queries Trigger Structured Information Cards (Knowledge Panels)

How might queries that trigger structured information cards change over time?

When does a search engine decide that it should show a knowledge panel in response to a query?

What words in a query will trigger that knowledge panel?

A knowledge panel is sometimes referred to as a structured information card by Google.

Other structured information cards contain information about things such as hotel reservations, flight arrivals, dinner reservations, movie tickets, and others.

If you’ve worked with knowledge panels, you’ve learned that different searches will trigger those to appear.

Often those include a mention of an entity, such as a business name, or a certain thing.

And queries that trigger structured information cards can change over time according to a recently granted patent.

Search queries can be used to return resources – such as web pages, images, text documents, electronic mail documents, multimedia content, etc. – relevant to a searcher’s needs and to present information about those resources in a way that is most useful to the searcher.

Sometimes the best result may be a structured information card.

A computer system may:

Receive a search query.

Process the search query.

Identify results that are relevant to the search query.

Return a set of search results in response to a searcher submitted query.

The patent this post is about is from the start of November 2023, and it tells us about a card trigger-term identification unit that could identify additional trigger-terms that show a structured information card.

The card trigger-term identification unit allows the grammar of one or more structured information cards to be tuned, over time, by evaluating candidate terms in queries for potential inclusion in the grammar of a structured information card.

For example, assume the grammar for a “Movie” structured information card includes terms such as “movie time,” “movie ticket confirmation,” and “ticket confirmation number.”

The card trigger-term identification unit may:

Analyze terms associated with the grammar of the “Movie” structured information card and one or candidate queries.

Identify an additional trigger-term for the “Movie” structured information card such as the trigger-term “movie ticket.”

Accordingly, follow-up queries that are received may include terms such as “movie time”, “movie ticket,” or both and will trigger the display of a “Movie” structured information card in response to such queries.

The subject matter of this patent may be used to identify additional trigger-terms that will show structured information cards.

The process behind the patent may include accessing data associated with a template for presenting structured information in response to a search query, wherein the accessed data references:

One or more label terms that, when included in the search query, triggers a structured information card to be presented according to the template.

For each of the one or more label terms, a value, obtaining a candidate label term that is not already associated with the template for presenting the structured information.

For each of the one or more label terms:

Identifying entities that are associated with the label term.

Identifying entities that are associated with the candidate label term.

For each of the entities associated with a candidate label term, a query may cause an association, with a candidate label term:

One or more of the label terms that are associated with the entity.

For each of the one or more of the label terms that are associated with the entity, the value associated with the label term, and after receiving a query that includes the candidate label term.

Using the one or more values associated with the candidate label term to determine whether to trigger the structured information to be presented according to the template.

These and other versions may optionally include one or more of the following features:

The label terms may correspond to parameters of a search query.

The value may be indicative of the number of times the query has been used to trigger the appearance of the structured information card.

Obtaining a candidate label term that is not already associated with the template for presenting the structured information card may involve identifying query terms from a query log.

Using the one or more values associated with the candidate label term to determine whether to trigger the structured information to be presented according to the template may include aggregating the one or more values that are associated with the candidate label term.

Determining whether the aggregated value satisfies a predetermined threshold, and in response to determining that the aggregated value satisfies the predetermined threshold.

Determining that the search query including the candidate label term will trigger the presentation of the structured information.

Triggering Structured Information Cards

Using the values associated with a candidate label term to trigger a structured information card to be presented according to the template may involve:

Aggregating the one or more values that are associated with the candidate label term.

Determining whether the aggregated value satisfies a predetermined threshold.

In response to determining that the aggregated value exceeds the predetermined threshold, determining that the search query including the candidate label term will not trigger the presentation of the structured information.

The method behind the patent may also include adjusting the values that are associated with candidate labels based on those candidate label’s similarity to the label terms.

This structured information cards patent can be found at:

Search and retrieval of structured information cards

Filed: October 26, 2023

Abstract

Methods, systems, apparatus, including computer programs encoded on a computer storage medium, to facilitate identification of additional trigger-terms for a structured information card. In one aspect, the method includes actions of accessing data associated with a template for presenting structured information, wherein the accessed data references:

(ii) a value.

Other actions may include obtaining a candidate label term, identifying one or more entities that are associated with the label term, identifying one or more of the entities that are associated with the candidate label term, and for each particular entity of the one or more entities that are associated with the candidate label term, associating, with the candidate label term, (i) a label term that is associated with the particular entity, and (ii) the value associated with the label term.

A System for Identifying Additional Trigger – Terms for a Structured Information Card

The patent shows off some examples of information that might be used to create structured information cards in a drawing:

It provides an example of a structured information card involving flight information.

Assume a searcher is standing at an airport ticket counter before his or her flight #437 to Denver, Colorado.

To check their bags, and obtain a boarding pass, they need to provide a flight confirmation number for their upcoming flight.

To respond to a request for a flight confirmation number from an airline employee, the searcher may look for a confirmation email from the airline that includes their flight confirmation number.

To obtain that confirmation email, the searcher may search for a query that includes the term “Flight Ticket” into a search box at the search engine.

After receiving a query, the search device may send the query to a server using a network.

The server may process the query, identify search results responsive to the query term “Flight Ticket,” and then return the search results to the search device.

The search results may be received by the search device and provided for display using the interface of the search device.

The search results may include references to email documents. The references may include a link that, when selected, provides an email document associated with the link to display on the search device.

Each respective email reference may include text such as the name of the email sender, the subject line of the email, the time the email was received, and the date the email was received.

The search results identified based on the query “Flight Ticket” may include emails from a variety of different senders.

The search results may refer to an email from a movie theater “ABC Theatre” related to the searcher’s purchase of a movie ticket to see “Pilot’s First Flight.”

The search results may also refer to an email from a restaurant reservation service “Closed Table” for a reservation at the “Wine Flight Bar.”

The search results may also refer to an email of an order confirmation from “DC Outfitters” for the purchase of a “Flight Jacket.”

The search results may refer to emails associated with airline ticket purchase confirmations from an airline such as “NE Airlines,” “SE Airlines,” or the like.

One particular reference is for a confirmation email that the searcher received after purchasing an airline ticket from “NE Airlines” for “Flight 437.”

However, given the number of search results provided in response to the search query, it could be time-consuming to obtain the searcher’s flight confirmation number.

This is because the searcher must read the sender and subject line of each reference to each respective email that is responsive to the search.

Then, the searcher using the search device must request the email document associated with the reference.

Finally, after the email document is selected, the email document with the flight confirmation number for the searcher’s upcoming Flight #437 is returned.

Though the searcher was able to obtain the user’s confirmation number, the process was inefficient.

In addition, the process may not go smoothly for reasons below and the like, such as:

Pressure on the user because of a long line of passengers at the ticket counter.

Slow cellular data signals due to poor reception in the airport terminal.

This system may work to improve the searcher’s retrieval of information from the searcher’s email inbox.

For instance, at stage A, the system provides multiple structured information cards stored in a structured information card storage unit.

Each structured information card includes a template and a grammar.

The template of the structured information card includes one or more predetermined fields that can be populated with information from an email document in response to a particular search query.

For example, the structured information storage unit may include a structured information card for “Flight” information.

The structured information card for “Flight” information may include:

A “To” field

A “From” field

A “Departs” field

A “Conf. #” field

A “Flight #” field

Trigger Terms Associated with Structured Information Cards

The grammar of structured information cards may include one or more trigger-terms that are associated with the card.

A trigger-term may include terms that, when detected by the server as being included in a search query, trigger the retrieval, population, and the display of the structured information card associated with the trigger-term.

So, in response to a query that includes the term “flight reservation,” the “Flight” structured information card may be:

Obtained.

Populated with data from the most recent email that is associated with an upcoming flight.

Provided for display via a searcher interface.

However, the “Flight” structured information card may not have been triggered in response to a query including the term “Flight Ticket” because the search term “Flight Ticket” may not have been included in the “Flight” structured information card’s grammar at an initial stage A.

The search server may use the card trigger-term identification unit to identify additional terms that trigger the “Flight” structured information card.

Those additional terms may be based on queries received from a search box associated with a user interface displayed by a search device such as a search box.

The card trigger-term identification unit could:

Obtain a query term from a log of received query terms.

Determine if the query term is related to one or more other terms in a structured information card’s grammar.

Add the query term to the structured information card’s grammar.

The card trigger-term identification unit may determine that the term “Flight Ticket” should be added to the grammar of the “Flight” structured information card.

Adding the term “Flight Ticket” to the grammar of the “Flight” structured information card results would be done in an updated structured information storage unit at stage B of the process.

At stage B, the searcher of the same search device can access a user interface at a later point in time.

The search interface may be the same searcher interface. The searcher may input a search query that includes the term “Flight Ticket” into the search box.

The search device may transmit the search query to a server.

The server may process the query, identify search results responsive to the search query “Flight Ticket,” and then return the search results to the search device.

The search results may be received by the search device and provided for display using the interface of the search device.

The search results may refer to email documents.

At stage B the search interface includes a structured information card.

That structured information card may include a display with fields that are populated with data extracted from a resource that is responsive to the search query.

The structured information card may be obtained, populated, and provided for display through the search interface because the grammar of the structured information card now includes the term “Flight Ticket.”

The term “Flight Ticket” may have been added to the grammar of the “Flight” structured information card based on:

The card trigger-term identification unit’s analysis of the existing terms included within the grammar of the structured information card.

Identified relationships between existing terms included within the grammar of the structured information card.

Identified relationships between the aforementioned grammar terms and one or more queries previously submitted via the search box.

The particular structured information card obtained and displayed may be based on query terms submitted through the search box.

For example, the server may select a particular structured information card for display via the user interface based on a determination that the search query term such as “Flight Ticket” matches one or more grammar terms associated with the particular structured information card.

The server may populate the obtained structured information card with the contents from the highest-ranked search result that includes information requested by the fields of the structured information card template.

Those highest-ranked search results might be from the most recent email document that includes information requested by the fields of the structured information card template.

Advantages of This Structured Information Card Approach

It can display relevant information related to the searcher’s upcoming flight without requiring the searcher to read the data associated with each reference returned as a search result by the server.

The structured information card displays:

The user’s flight destination (e.g., Denver, Co).

The user’s flight origin (e.g., Washington, D.C.).

The user’s flight departure time (e.g., 11:45 a.m. EST).

The user’s flight confirmation number (e.g., KP4EG).

The user’s flight number (e.g., 437).

The searcher does not need to open the email including information about the searcher’s upcoming flight because the necessary information associated with the user’s upcoming flight is within the structured information card.

Because of this, the searcher using the search device who is standing at the ticket counter can:

Quickly search their email.

Obtain their flight confirmation number from the structured information card.

Provide the flight confirmation number to the airline representative in an efficient manner.

The patent description provides an example of a system that uses a card trigger-term identification unit to identify additional terms that can be added to the grammar of a structured information card is directed towards a “Flight” structured information card.

However, the description of the patent tells us that it should not be so limited.

The card trigger-term identification unit can be used to identify additional grammar terms for any type of structured information card such as:

Movie Ticket structured information cards.

Dinner Reservation structured information cards.

Hotel Reservation structured information cards.

Vehicle Rental structured information cards.

Device Rental structured information cards.

The like.

The patent also tells us that any type of structured information card may be used where the structured information card can be uniquely identified using a set of one or more grammar terms.

Entities, Attributes, & Graph Structure Information Cards

Google identifies entities and associates labels and attributes with those entities, and described in the following flowchart drawing from the patent:

A card trigger-term identification unit analyzes existing terms associated with the grammar of one or more structured information cards.

The terms associated with the grammar of one or more structured information cards include terms that, when received in a query, trigger the display of a particular structured information card.

Analyzing existing terms associated with the grammar of one or more information cards may include the generation of a graph structure.

This graph structure may include query nodes each associated with a particular grammar term that triggers the selection, population, and display of a particular structured information card.

Each query node may be associated with a respective label term.

One query node may be associated with the label term “Flight Reservation” and another query node may be associated with the label term “Ticket.”

Label terms used to build the graph may be obtained from the structured information card storage unit, a query log, or the like.

The graph structure may also include one or more entity nodes.

The entity nodes may include an item of data that is indicative of a relationship between the respective label terms of one or more nodes.

The relationship may include a semantic relationship associated with the label terms.

By way of example, the card trigger-term identification unit may obtain the candidate query term “Flight Ticket” to evaluate the candidate query term “Flight Ticket” for potential inclusion in the grammar associated with a structured information card such the “Flight” structured information card.

The query term “Flight Ticket” may have been stored in a query log after the user of a user device such as user device submitted the query “Flight Ticket” to search one or more emails using an interface for an electronic mailbox such as interface before the inclusion of the term “Flight Ticket” in the grammar of the “Flight” structured information card.

A query node may be generated in the graph structure based on the candidate query term “Flight Ticket.”

The candidate query node is associated with a candidate label term “Flight Ticket.”

The information from the structured information card may be information that is in a graph structure.

For instance, when an information card is about flight information, it contains key/value pairs that provide information about related entities and attributes of those entities (making it structured information).

Referring to it as a card means that it is using a display format with a related template for that format.

For flight information, you would have a departure city, a destination city, a departure time, and an arrival time, a departure airport and an arrival airport, a confirmation number, a flight number, and so on.

These related entities and attributes for them can be found in a template that has labels for each of the fields of information that it covers, and those labels can be used in a query to show an information card about a flight ticket.

They can be used for identifying an additional trigger-term for a structured information card.

This drawing from the patent shows how labels might be connected to entities and attributes:

Generally, the process may include:

Accessing data associated with a template for presenting structured information.

Identifying the first set of one or more entities.

Associating one or more labels and one or more values with one or more entities in the first set of the identified entities.

Obtaining a candidate label term.

Identifying a subset of one or more entities from the first set of entities.

Associating one or more labels and one or more values with one or more candidate label terms

Receiving a search query.

Using values associated with each candidate label term to determine whether to trigger display of the structured information.

So these candidate labels may be chosen, when they appear in queries to display a structured information card.

For example, where an entity includes an email document, network address, URL, or the like, an entity may be associated with a candidate label term if the candidate label term would return the email document, network address, URL, or the like when a query that includes the candidate label term is executed.

The system may associate one or more labels and one or more values with each candidate label term.

For example, any label terms associated with a particular entity at stage may be associated with a candidate label term with which the entity is related.

Thus a label term that was propagated to a particular entity from a query node may be further propagated from the entity to a candidate label term with which the particular entity is related.

One or more values associated with an entity may similarly be associated with one or more candidate label terms with which the entity is related.

Accordingly, a value that was propagated to a particular entity from a query node may be further propagated to a candidate label term.

This system may analyze each of the one or more values that are associated with a candidate label term to determine whether the candidate label term should be added to the grammar associated with a structured information card.

Determining whether a label term should be associated with a structured information card may include aggregating the values associated with the candidate label term, and evaluating the aggregated value against a predetermined threshold.

If it is determined that the aggregated value satisfies a predetermined threshold, the label term may be added to the grammar of the structured information card.

If the aggregated value does not satisfy a predetermined threshold, the label term is not added to the grammar of the structured information card.

Triggering a Structured Information Card

The system may process the received search query, and use values associated with each identified candidate label term to determine whether to trigger the display of a structured information card.

Using the values associated with each candidate label term may include:

Aggregating the values associated with the candidate label term.

Evaluating the aggregated value against a predetermined threshold.

Determining that the aggregated value associated with the candidate label term satisfies the predetermined threshold, the system may let the search query including the candidate label term trigger a related structured information card.

Triggering Structured Information Cards Takeaways

A structured information card may appear in response to a query that is related to the grammar from the template for the different types of structured information cards.

It may be possible to anticipate which entities may be relevant for a structured information card, and which queries might trigger that card.

Structured information cards evolve in how they are triggered based on queries and the grammar of the information in the card.

More Resources:

Image Credits

All screenshots taken by author, December 2023

Basic Introduction To Google Bigquery And Data Studio Every Data Scientist Should Know!

This article was published as a part of the Data Science Blogathon.

Introduction

In today’s data-driven age, cloud platforms have been a boon in terms of reducing the reliance on physical IT systems and switching to a more seamless experience in terms of storage, efficiency, and scalability. As we all know, Google Cloud Platform (GCP) is one of such leading cloud providers offering a variety of services, and this article would focus on an introduction to its querying language platform BigQuery, and visual analytics tool Data Studio.

What is BigQuery?

Google BigQuery is a serverless data warehousing platform where you can query and process vast amounts of data. The best part about it is that one can run multiple queries in a matter of seconds even if the datasets are relatively large in size. If you’re familiar with SQL (Structured Query Language), it would be pretty easy to pick up. Let’s get started on basic BigQuery!

1. Open chúng tôi – the GCP window will open. You must ideally have a Google account for this. In the Search tab, enter BigQuery and this would redirect you to the BigQuery query editor window as shown below:

2. Let us begin first by using GCP’s existing repository of public datasets (yes! GCP has sample datasets to explore too!) Go to the left section of the window where you’ll find an ‘Add Data’ option – here, select ‘Explore public datasets’, and publicly available datasets will get listed as shown below (alternatively, one can also add their own data using ‘External data source’ option). Select the datasets you’d want to view and it will get added under the project name ‘bigquery-public-data’ in your main editor window. In our case, we have loaded the Covid-19 dataset.

Once created, it will reflect on the left-hand side of the main editor window. We now have a project ready, and next, we would have to create a dataset under this location to store the new table we want to create.

7. In the query editor, we will now create a table ‘myproject_covid_data’ in our newly-created location using SQL querying as follows:

We now have the number of confirmed, deceased, and recovered Covid cases by Country and Date in our dataset. We would now want to derive some insights from this data – this is where Data Studio comes into play.

What is the Data Studio?

Google Data Studio is a visualization platform whereby you can create quick dashboards and reports from your data. GCP offers a very useful option of exporting the data on BigQuery to Data Studio so that one can start working on the insights right away! Let’s explore this in the next section.

1. In our previous section, we had created the subsetted table ‘myproject_covid_data’. To visualize it in Data Studio, go to the ‘Export’ option on the query results pane below and select ‘Explore with Data Studio’. A new window would open up for visualization:

2. You would see a lot of chart/visual options on the right and the metrics to be represented in the visualization. Let us now create a visual which shows the number of confirmed versus recovered Covid cases by date and filter it by country to view results.

From the right, choose the combo chart (bar + line graph). On the bottom right, you would see 2 tabs for ‘Data’ and ‘Style’ – you can add the metrics required under the Data tab, and format the graphs visually under the Style tab.

3. Under the Data tab, add the ‘date’ column under Dimension, and ‘new_confirmed’ and ‘new_recovered’ under Metric. You would notice that an automatic sum aggregation is chosen for these columns, and that is what we want to look at.

4. Next, drag the ‘country_name’ and ‘date’ columns to the Filter pane above the chart. Select one of the countries, eg – India, and a date range – eg – 1st to 30th Sep’20 from the filter dropdowns. Your visual should appear as follows. Also, make sure to sort the X-axis dates using the sort option in the chart (in 2nd image):

5. As you can see, the chart plots the number of confirmed cases on the line graph and the number of recovered cases on the bar graph for India for the time period 1st to 30th Sep’20. Hovering over the data bars or lines would display the exact values for that data point.

6. We can further format this chart to look more visually appealing by using the Style tab on the bottom right. Our final visual then looks as follows, and voila! You have built insights by creating a dataset in BigQuery and visualizing it in Data Studio!

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