Automatic Indexer Workflow and Optimization Techniques

An automatic indexer workflow can be optimized by implementing a tiered indexing strategy, where less critical data is indexed first, freeing up resources for more critical data. This approach can significantly improve indexing speed and efficiency.

By leveraging cloud-based indexing, organizations can scale their indexing capacity to meet changing demands, reducing the need for costly hardware upgrades. Cloud-based indexing also provides greater flexibility and accessibility.

A well-designed indexing workflow should include regular maintenance tasks, such as re-indexing and data validation, to ensure data accuracy and consistency. This helps to prevent data corruption and ensures that the indexer remains efficient over time.

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Automatic indexer workflow is a crucial process that simplifies the indexing of data. Indexers can automate some tasks related to data ingestion, but creating an index is generally not one of them.

To set up an indexer, you must have a predefined index that contains corresponding target fields for any source fields in your external data source. Fields need to match by name and data type, and if not, you can define field mappings to establish the association.

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Indexers require a data source object that provides a connection string and possibly credentials. Data sources are independent objects, and multiple indexers can use the same data source object to load more than one index at a time.

Here's a breakdown of the auto-submit index feature:

SEO Utils will submit for indexing all sites that enable the Auto Index feature every 10 minutes.

An indexer requires a data source object that provides a connection string and possibly credentials.

This data source object is an independent object, and multiple indexers can use the same data source object to load more than one index at a time.

Indexers will automate some tasks related to data ingestion, but creating an index is generally not one of them.

You must have a predefined index that contains corresponding target fields for any source fields in your external data source.

Fields need to match by name and data type, or you can define field mappings to establish the association.

Here's a breakdown of the common steps that all indexers share:

By following these steps, you can set up a basic indexer workflow that meets your needs.

The subheading prediction CNN model was evaluated for all manually indexed main headings in the test set, achieving a standalone F1 score of 0.664.

The subheading prediction model performed significantly better than the main heading prediction model, with an F1 score that's about 3% higher.

The main heading prediction model, on the other hand, had a standalone F1 score of 0.638, which is slightly lower than the subheading prediction model.

This discrepancy in performance is attributed to error propagation from main heading prediction, which negatively impacts the overall end-to-end MeSH pair prediction performance.

The chained method's main and subheading prediction models are shown to have standalone F1 scores of 0.638 and 0.664 respectively, indicating their individual strengths and weaknesses.

Data preparation is a crucial step in the automatic indexing process. It involves cleaning, transforming, and formatting the data to make it usable for indexing.

Removing duplicates and handling missing values are essential tasks in data preparation. This can be done using techniques such as data deduplication and imputation.

The quality of the data directly affects the accuracy of the index. Poor data quality can lead to incorrect or irrelevant results, which can be frustrating for users.

The dataset used in this study is comprised of citation data for manually indexed articles published from 2004 onwards.

The dataset contains about 10 million articles, which is a massive amount of data to work with.

20,000 articles published in 2018 were randomly selected for the validation set, and 38,903 articles indexed between 16th November 2017 and 31 January 2018 were randomly selected for the test set.

All citation data was downloaded from the MEDLINE/PubMed 2019 baseline, which provides a comprehensive source of medical literature.

We have a record of MTI's original recommendations for the test set articles, which is important because MTI uses nearest neighbor algorithms and its performance will improve after articles have been indexed.

FacetWP's automatic indexer is a game-changer for data management. It performs a single re-index when an individual item is edited or saved, keeping the index up to date without putting a heavy burden on your site.

This means you don't have to re-index after adding or editing/saving posts or terms, with a few exceptions.

When working with data, it's essential to know what sources you can connect to. Indexers in Azure can crawl data stores both within and outside of Azure.

You can connect to various Azure services, including Azure Blob Storage, Azure Cosmos DB, and Azure Data Lake Storage Gen2.

Indexers also support connecting to on-premises SQL Server instances, such as SQL Server on Azure Virtual Machines.

Azure Files and Azure MySQL are also supported, but they're currently in preview.

Here's a list of supported data sources:

Note that Azure Cosmos DB for Cassandra is not supported.

Field mappings are an essential part of data preparation and processing, and they're used to associate the content of source fields with destination fields in a search index.

Field mappings occur after document cracking, but before transformations, when the indexer is reading from the source documents. This means that the value of the source field is sent as-is to the destination field with no modifications.

You can specify field mappings by entering the source and destination fields in the indexer definition. This helps to map fields with different names or types in the output.

Field mappings are considered optional, which means you can choose to use them or not. However, if you're working with transformed content, you'll need to use output field mappings, which are required for any transformed content that should be in the index.

Output field mappings are used to associate the content of an internal enriched document (skill outputs) with destination fields in the index. This is different from field mappings, which build associations from source fields.

A sample indexer debug session representation shows the different stages of the indexer, including document cracking, field mappings, skillset execution, and output field mappings.

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A structured approach to database indexing is crucial for efficient query performance. This involves creating the right indexes to help the database quickly locate specific data.

Indexes are essentially a variant of a cache, designed to make queries faster by providing an additional data structure within the database. They help answer queries directly or optimize where to look in the actual table.

The tradeoff is that each added index slows down writes to the database, as the database must maintain the index data structure. To measure this tradeoff, we can use two metrics: Cost improvement and Index Write Overhead.

Cost improvement measures how much a potential index would improve query performance, based on the Postgres cost model. Index Write Overhead measures the additional bytes written to indexes compared to the bytes written to the table itself.

To decide which indexes to create, we need to analyze queries and identify potential index candidates. We can start by looking at the queries we're running, but this becomes harder with complex queries.

By breaking down queries into smaller parts called "scans", we can make it easier to identify potential index candidates. Scans are always on a single table and can be represented in an EXPLAIN plan.

The pganalyze Indexing Engine uses a generic query plan to determine whether a JOIN clause is likely to be indexable. This helps us identify potential index element candidates, which are essentially the columns or expressions being indexed.

We can use the Postgres planner to determine which operator classes are applicable for indexing a given clause. For example, equality operators are indexable by B-Tree indexes, while operators like @> are only indexable with a GIN index.

In data preparation and processing, skillset execution is an optional step that invokes built-in or custom AI processing.

This step is where enrichment occurs, allowing you to add value to your data through various transformations. Skillsets can add optical character recognition (OCR) or other forms of image analysis if the content is binary.

You can also use skillsets to add natural language processing, such as text translation or key phrase extraction. This can be especially useful when working with data that contains text or images.

Skillsets can be thought of as a "pipeline within the pipeline", allowing you to create a more complex and efficient data processing workflow.

The Scan Matrix is a powerful tool used in the Indexing Engine to visualize the results of the "What If?" analysis for a set of existing indexes and potential new indexes. It's essentially a table that maps each potential index to a scan, showing the cost improvement over the current baseline.

Each cell in the matrix represents the cost improvement over the current baseline, with "XXX" indicating the best overall index and "(x)" indicating existing indexes that match the scan. Some columns have no cost improvements, typically when the existing indexes are covering the scan sufficiently.

The Scan Matrix is a crucial step in the Indexing Engine's process, as it helps identify the most effective indexes to use for each scan. By analyzing the matrix, you can quickly see which indexes are the most beneficial and make informed decisions about which ones to implement.

To give you a better idea of what the Scan Matrix looks like, here's a breakdown of the different types of cells:

Here's an example of what the Scan Matrix might look like for a particular set of indexes and scans:

By analyzing the Scan Matrix, you can quickly identify the most effective indexes to use for each scan and make informed decisions about which ones to implement.

The Set Covering Algorithm is a key component of the pganalyze Indexing Engine. It's a greedy algorithm that starts with the potential index that matches the most scans and continues until all scans are covered.

The algorithm chooses the index with the lower Index Write Overhead if two indexes match the same number of scans. This helps to minimize the overhead of maintaining the indexes.

The algorithm produces a deterministic result, meaning that when run again with the same inputs, it would produce the same outcome. However, human review is still encouraged to ensure that the selected indexes meet the specific needs of the application.

Here's an example of how the algorithm works, using the input matrix from the article:

The algorithm picks the following indexes:

1. I11 (covers S2, S5, S10, S11, S15)

2. I9 (covers S6, S12, S14)

3. I6 (covers S1)

4. I2 (covers S8)

This result is deterministic, but human review is still encouraged to ensure that the selected indexes meet the specific needs of the application.

Pulling URLs from sitemaps is a daily task that can be automated. SEO Utils will pull URLs from all sitemaps at 5:00 AM, your local time.

This process helps get new URLs and check if existing URLs have updated content. The Last Mod field is used to determine if a URL has been updated.

If a URL has updated content, SEO Utils will resubmit it for indexing, regardless of the submission status. This ensures that updated content is crawled and indexed by search engines.

In Table 2, we see the main heading/subheading pair prediction performance for critical subheadings. The Method MTI has a Precision of 0.416, which is lower than the chained method's Precision of 0.453.

The Recall of MTI is 0.162, whereas the chained method's Recall is 0.438, showing a significant improvement. The F1 Score for MTI is 0.234, while the chained method's F1 Score is 0.445, demonstrating its better performance.

The chained method's performance is close to the end-to-end method's performance for all subheadings, with the largest absolute difference in F1 Score being 2.8% for the "diagnostic imaging" subheading.

Here's a summary of the performance metrics for the three methods:

A Convolutional Neural Network (CNN) is used for fine-grained automatic MeSH indexing because it's computationally efficient and effective for text classification problems with large numbers of labels.

The CNN architecture is chosen for its ability to process text inputs and generate a fixed length representation of the input text that is invariant to the position of the detected features.

The network learns a set of convolutional filters that are convolved along the length of the input text to produce an activation map, and this is followed by a max pooling operation that keeps only the maximum activation of each filter.

The three different models share five common inputs: the article title, abstract, journal, publication year, and indexing year, and the standalone subheading prediction model requires an additional input for the main heading that subheadings are being predicted for.

Each input is processed separately using the same word embeddings and convolutional filter weights, with the title and abstract inputs using standard and dynamic max pooling respectively.

The journal is treated as a categorical input, represented by a fixed length vector, and the two year inputs are represented using a special encoding scheme previously described in Rae et al..

The first phase of the Indexing Engine runs at least once for each query, making sense of both simple and complex queries by structuring them into a simpler format for processing.

This phase is responsible for introspecting the actual inputs to the index recommendation logic, making it easier to understand how the database will perform with different index combinations.

The second phase of the Indexing Engine relies on the first phase to provide a clear and concise format for processing, allowing it to focus on optimizing index selection.

The "What If?" analysis is a crucial aspect of the Indexing Engine, where the algorithm asks the database to simulate the performance of different index combinations.

This approach is more efficient than traditional methods, which can be slowed down by expensive cost estimation calls to the what-if optimizer.

HypoPG, a popular extension for Postgres, allows creating hypothetical indexes on the production system, but our solution runs solely within the pganalyze system, requiring no extensions on the production database.

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Our "What if?" analysis runs directly within the pganalyze app, separate from the database, and adds extra instrumentation to compare all possible indexes for a given scan.

The result of the "What if?" analysis is a set of cost improvement numbers, one number per scan and index combination, providing a clear picture of the potential benefits of different index configurations.

Post-processing and evaluation are crucial steps in the development of an automatic indexer. The precision, recall, and F1 score metrics used in the study have limitations, particularly in their treatment of false positives.

False positives can be either acceptable alternative indexing, acceptable imperfect indexing, or unacceptable indexing that needs to be removed. The problem with the current metrics is that they penalize all false positives equally, making it difficult to judge their usefulness in practice.

To better understand the strengths and weaknesses of the proposed neural network approach, an indexer evaluation was conducted. The evaluation focused on particularly problematic false positive main heading/subheading pair predictions, and indexers were asked to decide whether each false positive pair is acceptable or unacceptable.

The evaluation set consisted of 180 articles from the test set, and two senior indexers participated in the study. Each indexer evaluated 90 articles using the Microsoft Access form, which displays the article title, abstract, and existing manual indexing.

Here is a summary of the evaluation metrics used:

The evaluation results showed that on average 70% of false positives were considered to be acceptable, with the highest acceptable fraction being 0.83 for the critical subheading "toxicity".

FacetWP will let you know when you need to re-index by displaying a message in the top right corner of the admin screen after you save facet settings.

Re-indexing is only necessary when facets are added, which is a crucial step in setting up your faceted search. This is because FacetWP needs to know about the new facets to accurately index your content.

Here are some specific situations where re-indexing is required:

These situations all require re-indexing to ensure that your faceted search is accurate and up-to-date.

As we move into the post-processing and evaluation stage, we need to eliminate the worst candidates for indexing. The algorithm eliminates all indexes from consideration that are not within 50% of the best possible result.

This early reduction is crucial because it enables later stages of the algorithm to view the problem as a greedy set covering problem. Any index that remains after this initial elimination would be acceptable and "good enough".

In other words, the algorithm is saying that if an index is not performing at least half as well as the best index, it's not worth considering. This makes sense, as it would be a waste of resources to pursue an index that's not even close to being the best option.

The algorithm is looking for indexes that are within 50% of the best possible result, which is a reasonable threshold. It's not expecting perfection, just a good enough performance to be worth considering further.

Checking is an essential step in the post-processing and evaluation process. You can trigger a process to bulk-check the index for all URLs on your site by clicking the Check Index action.

SEO Utils offers two methods for checking index: using the "site:url" operator or the Search Console URL Inspection API. The latter method has some pros, including not requiring proxies, being faster, and being easier to set up.

Here are the quota limits for the Search Console URL Inspection API method:

If you reach the limit, you might want to switch to the "site:url" operator method. This method doesn't have a quota limit, but you may want to consider using Proxies to prevent Google from blocking your IP when checking thousands of URLs.

SEO Utils will search Google using the "site:url" operator to see if the URL is on the SERP, providing extra information like Indexing State, Coverage State, and Crawled Date.

In the evaluation process, precision, recall, and F1 score metrics are used to measure performance, but they have limitations. They treat all false positives equally, making it difficult to judge the usefulness of MeSH term recommendations in practice.

The evaluation focused on particularly problematic false positive main heading/subheading pair predictions, and indexers were asked to decide whether each false positive pair is acceptable or unacceptable. There are three types of false positives of interest: incorrect main heading and subheading, correct main heading and incorrect subheading, and incorrect main heading and correct subheading.

Two senior indexers participated in the study, each evaluating 90 articles using a Microsoft Access form. The form displays the article title, abstract, and existing manual indexing, and the indexers select "Acceptable" or "Not Acceptable" for each false positive prediction.

The evaluation set consisted of 180 articles from the test set, with a good balance of false positive types and critical subheadings. An additional set of 12 articles were evaluated by both indexers to understand the level of inter-indexer agreement.

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The evaluation metric for the indexer evaluation was the fraction of acceptable false positive predictions. The chained method's false positive predictions were evaluated, and the results are shown in Figure 5.

Here are the indexer evaluation results:

The shared set of 12 articles contained 57 false positives, and the two indexers agreed on 95% of their determinations. This high level of agreement may be an overestimate, as the indexers discussed the shared set false positive examples to calibrate their determinations.

The Journal Statistics Baseline is an interesting approach to evaluating subheading prediction performance.

It's based on the indexing statistics of training set articles published in 2015 or later. The probability of the 17 critical subheadings being indexed was recorded for each journal and main heading.

At test time, critical subheadings were randomly sampled according to these statistics. This method doesn't consider the article text.

This baseline is useful because it provides a neutral, data-driven approach to evaluating subheading prediction.

Automatic indexers can be fine-tuned to optimize their performance on specific types of content.

This is achieved through the use of indexing rules that define how certain elements within the content should be indexed.

For example, an automatic indexer can be configured to prioritize indexing certain keywords or phrases over others, which can significantly improve its relevance and accuracy.

Indexing rules can also be used to exclude certain elements, such as images or videos, from being indexed, which can help to reduce the overall size of the index and improve performance.

As you start to think about customizing and optimizing your indexing strategy, it's essential to consider the different scenarios and use cases.

You can use an indexer as the sole means for data ingestion, or in combination with other techniques.

In the simplest scenario, one data source is the sole content provider for a search index, and most supported data sources provide some form of change detection.

This pattern is known as a "single data source" scenario, where one data source is the sole content provider for a search index.

You can also have multiple data sources, where each indexer run brings new content from a different data provider.

Each source can contribute its share of full documents, or populate selected fields in each document.

Here's a summary of the main scenarios:

You should plan on creating one indexer for every target index and data source combination, and you can have multiple indexers writing into the same index.

Improving the speed of your website is crucial for a good user experience. If your website has a lot of content, indexing can take a long time.

One simple way to speed up the indexing process is to use the facetwp_indexer_query_args hook to limit indexing to the post types you are actually using with FacetWP. This can be a game-changer for large websites.

To check which post types FacetWP currently indexes, click the “Show indexable post types” button in the settings.

Customize what is being indexed by using FacetWP's indexing hooks, which let you modify how FacetWP interacts with the facetwp_index table during the indexing process.

The facetwp_indexer_query_args hook is a powerful tool that allows you to customize which posts and post types get indexed.

You can also use the facetwp_index_row hook to modify individual facet values before they are saved into FacetWP's index table.

One application of the facetwp_index_row hook is indexing serialized data, which can be a game-changer for certain use cases.

FacetWP's indexing hooks give you the flexibility to tailor the indexing process to your specific needs, making it easier to get the most out of your search functionality.

You can automate the indexing process with the Auto Index feature, which is available in SEO Utils. This feature allows you to enable auto-indexing for specific sites or sitemaps, and it will automatically check and submit URLs for indexing.

To use the Auto Index feature, you need to toggle the Auto Index field on for the site or sitemap. You can also enable it for specific sitemaps by visiting the Sitemaps list page. If a URL is enabled for auto-indexing, it will turn yellow.

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The Auto Index feature will check the index of all sites that have it enabled at 6:00 AM local time. It will skip URLs that belong to a sitemap with the Auto-Index setting turned off. If you have many URLs to check index, consider using Proxies.

Here are some key points to keep in mind when using the Auto Index feature:

The Web Search API is a powerful tool that allows you to access Google's vast index of web pages.

To use the Web Search API, you'll need to create a Google Service Account, which is a special type of account that allows you to access Google APIs without sharing your personal account credentials.

The Web Search API has a daily quota of 100 requests per project, so be mindful of how often you're querying the API to avoid hitting this limit.

Google Service Accounts are managed through the Google Cloud Console, where you can create, manage, and delete your service accounts as needed.

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To authenticate your API requests, you'll need to generate a private key for your service account, which you can then use to sign your API requests.

The private key is a JSON key file that contains your service account's credentials, and it's stored securely on your server or in a secure location.

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The Auto Feature and URL Management of an automatic indexer can be a game-changer for your website's SEO.

SEO Utils provides an auto-pilot mode that lets you skip manually pulling URLs from sitemaps and checking them for indexing.

You can enable the Auto Index feature for a site by toggling the Auto Index field on, and also for specific sitemaps on the Sitemaps list page.

A URL is enabled for auto-indexing if it's yellow, so keep an eye out for that color cue.

If a URL is disabled for auto-indexing, you can still manually check and submit it for indexing using the "Check Index" and "Submit Index" actions.

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SEO Utils will pull URLs from all sitemaps daily at 5:00 AM your local time to get new URLs and check if existing URLs have updated content.

If a URL has updated content, SEO Utils will resubmit it for indexing, regardless of the submission status.

You'll need to keep SEO Utils running on your machine or use a VPS so you don't need to keep your computer on constantly to use the Auto Index feature.

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The automatic indexer is a powerful tool, but sometimes you might need to tweak its settings to get the results you want. You can do this by overriding its default behavior.

The indexer's default settings can be overridden for specific types of content, such as PDFs and Word documents. This is useful for files that don't follow standard formatting rules.

You can also adjust the indexer's advanced settings to fine-tune its performance. For example, you can change the indexing frequency to suit your needs.

The indexer's advanced settings also include options for handling duplicate content and specifying custom metadata. These features help ensure that your indexed content is accurate and up-to-date.

By overriding the indexer's default settings and adjusting its advanced settings, you can get the most out of your automatic indexer.

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The evaluation results of the automatic indexer are quite impressive. The chained method has a 3.7% absolute improvement in precision and a 27.6% absolute improvement in recall compared to the MTI baseline. This is a significant boost in performance.

The chained method has 2.7 times the recall of MTI, which means it can identify a much larger portion of the relevant indexing information. This is crucial in the field of medical indexing, where accuracy is paramount.

In the indexer evaluation, the two senior indexers evaluated 180 articles and found that 70% of false positives were considered acceptable. This is a key finding, as it highlights the importance of context in evaluating indexing performance.

The indexers also agreed on 95% of their determinations for the shared set of 12 articles, which suggests a high level of inter-indexer agreement. This is a promising sign for the reliability of the automatic indexer.

Here's a summary of the main heading/subheading pair prediction performance for critical subheadings:

The chained method outperforms MTI in all three metrics, and the end-to-end and chained methods have very similar performance. This suggests that the chained method is a reliable and accurate choice for automatic indexing.

The two neural network methods for fine-grained automatic MeSH indexing significantly outperformed the existing MTI implementation, offering a small improvement in precision and close to three times the recall of the current system.

This is a significant improvement, especially when considering the MeSH pair assignment, where the two methods exceeded manual indexing consistency for MeSH pair assignment using F1 score.

The F1 score is an important metric, and exceeding manual indexing consistency suggests that future improvements in performance may be more limited and difficult to achieve.

The evaluation results of particularly problematic false positive predictions were encouraging, with 70% of false positives considered to be either acceptable alternative indexing or imperfect indexing that doesn't need to be removed.

This gives us confidence that the updated MeSH term recommendations will be well received by NLM indexers.

Despite the end-to-end approach being a popular method for many tasks, the performance of end-to-end and multi-step methods in this paper were found to be similar.

The multi-step chained method achieved a marginally better F1 score, even when trained on less data.