How OpenSearch Can Improve Search for Complex Museum Collections

Museum collections contain some of the richest and most complex data on the web. An individual object record may include artists or makers, dates, classifications, materials, dimensions, geographic information, images, provenance, controlled vocabularies, and other cataloging data.

Making all of that information available online is one challenge. Making it easy to search and explore is another.

For museums, archives, libraries, and other cultural organizations with large digital collections, standard website or CMS search tools may not provide enough flexibility to create the kind of discovery experience their collections require.

Solving that challenge often requires looking beyond search itself: at the collection data, source systems, digital assets, integrations, CMS architecture, and the experience being created for the people exploring the collection. OpenSearch can be an important part of that architecture, but its value depends on how those pieces work together.

Why Is Museum Collection Search So Complex?

A typical website search may need to find relevant pages based primarily on titles, body copy, keywords, and other relatively straightforward content fields.

Collection search works differently.

Users may want to search for an artist, object type, date range, material, culture, classification, or any combination of those criteria. Researchers may approach the collection differently from casual visitors. Curators and internal teams may have different needs again.

The underlying data can also be complicated. Collection records often come from collection management systems or other specialized sources, with detailed cataloging structures that were not designed primarily for public web search.

As collections grow, organizations need more control over questions such as:

• Which collection fields should be searchable?
• Which fields should become filters or facets?
• How should results be sorted and ranked?
• How should different types of records appear in results?
• How should users move between searching and browsing?
• How should search behave as the collection continues to grow?

Trying to solve all of this with a site's standard CMS search can become limiting.

What Is OpenSearch?

OpenSearch is an open-source search and analytics engine originally derived from Elasticsearch. For our museum collection implementations, we use Amazon OpenSearch Service, AWS's managed implementation of OpenSearch.

In practical terms, OpenSearch sits between the systems that hold collection data and the public-facing website. Collection records are transformed into a purpose-built search index containing the fields needed for discovery, such as artist or maker, object type, date, classification, material, geography, and other institution-specific metadata.

When someone searches or applies filters on the website, the application queries that index rather than asking the CMS or collection management system to search the underlying records directly. This separation is important: the collection's source systems can remain optimized for cataloging and content management, while OpenSearch is optimized for fast retrieval, filtering, faceting, and relevance.

Amazon OpenSearch Service manages much of the underlying search infrastructure within AWS, allowing institutions to use a dedicated search engine without having to operate the OpenSearch cluster themselves.

In our experience, it has also proven to be extremely fast while keeping infrastructure costs relatively modest, even for substantial collection datasets.

What Can OpenSearch Enable for Museum Collections?

Depending on the collection and project requirements, an OpenSearch implementation can support:

• Fast search across large collection datasets
• Advanced filters and faceted browsing
• Custom sorting, ranking, and result presentation
• Search across object records and collection-specific fields
• Interfaces designed around specific audience needs
• More flexible collection discovery as data and requirements evolve

Just as importantly, the search architecture can reflect the way an institution actually catalogs and organizes its collection rather than forcing that data into a generic website-search model.

A Search Index Is Only Half the Architecture

One of the lessons we've learned from implementing OpenSearch for museum collections is that querying the index is only part of the problem. You also need a reliable way to build that index and keep it synchronized with the collection.

In a decoupled architecture, we typically separate these responsibilities.

The public-facing application doesn't need to communicate directly with OpenSearch. Instead, a search API accepts the visitor's keyword query, advanced search criteria, and selected facets, translates those parameters into an OpenSearch query, and returns the appropriate results to the interface.

That abstraction has practical benefits. The front end doesn't need to know the details of the underlying search implementation, and the search service can evolve without requiring the entire collection interface to be rebuilt around it.

A separate indexing process handles the other direction. It retrieves collection data from the source system, transforms it into the structure needed for public search, and updates the OpenSearch index.

There isn't one correct indexing strategy. For LACMA Collections, for example, retrieving the source data is fast enough that rebuilding the search index can be simpler and more reliable than determining and updating individual records. Other collections may benefit from more targeted incremental indexing.

The right approach depends on the size of the collection, how frequently records change, the source systems involved, and how expensive the underlying data is to retrieve.

For statically generated or heavily cached collection sites, the indexing process can also identify which objects have changed and selectively revalidate those pages. This keeps individual object records current without requiring a full site rebuild every time collection data changes.

Collection Metadata Is Only Part of the Picture

Search also sits within a larger collections publishing architecture.

Object metadata may originate in a collection management system, while high-resolution images and other media come from a digital asset management system (DAM). Those assets may need their own transformation and publishing process before they are ready for the web.

We have built these kinds of parallel data and image pipelines as part of collections projects, including architectures that use Amazon S3 for web-oriented image storage. But these pipelines solve a different problem from search.

The important architectural point is that OpenSearch does not need to become the system of record for the collection. Instead, it provides a search-optimized representation of the information needed by the public experience, while authoritative metadata and media can remain in the systems best suited to manage them.

Separating these concerns gives museums more flexibility to improve the public collection experience without requiring their internal cataloging and asset-management systems to serve every need of the website.

The Technology Is Only Part of the Solution

OpenSearch provides the search infrastructure, but installing a search platform does not automatically produce a good collection experience.

The larger challenge is deciding how the collection data, technology, and user experience should work together.

Our collection search engagements typically start by understanding the institution's collection data, source systems, audiences, and current search limitations. From there, the team can determine which fields should be indexed, which filters and facets are most meaningful, how results should be displayed, and how visitors are likely to explore the collection.

The work can include:

• Reviewing collection data and cataloging structures
• Understanding how data moves from collection management systems and other source systems
• Defining searchable fields, filters, facets, ranking, and sorting
• Designing the indexing and synchronization process
• Building and configuring the OpenSearch index
• Designing and implementing the front-end search experience
• Testing and tuning search behavior before and after launch

This is why we view OpenSearch collection search as a repeatable architecture rather than a plug-and-play product. The underlying approach can be reused, but every institution's data, audiences, source systems, and discovery goals are different.

What Does This Look Like in Practice?

Urban Insight has used this approach for live collection-search experiences at the Los Angeles County Museum of Art (LACMA) and Winterthur Museum, Garden & Library.

Both institutions have substantial collections with detailed object records and collection-specific metadata, but the implementations are not identical.

LACMA's collection search uses a more highly tuned and sophisticated query implementation, while its indexing process can remain comparatively simple. Winterthur's search needs are somewhat different, and more of the complexity lives in the process that prepares and indexes its collection data.

That difference is important.

The goal isn't to reproduce the same OpenSearch implementation for every museum. It is to reuse an architecture and lessons that have proven successful, then adapt the indexing, querying, and user experience to the needs of the individual collection.

The value isn't simply choosing OpenSearch as a technology. It comes from combining a search platform designed for complex data with thoughtful decisions about collection structure, indexing, user experience, front-end implementation, and how people actually want to discover objects.

Is OpenSearch the Right Fit for Your Collection?

Not every website needs a dedicated search platform.

A dedicated search architecture using OpenSearch becomes particularly worth considering when an organization has:

• A large digital collection or archive
• Complex object records or cataloging data
• Search that feels slow or difficult to refine
• A need for advanced filters or faceted browsing
• Collection data coming from specialized systems
• Multiple systems contributing metadata or digital assets to the public experience
• Audiences that need deeper ways to explore the collection

The right approach still depends on the data, systems, audiences, and goals behind the project.

But the larger lesson from our collection work is that cataloging, publishing, search, and presentation do not all need to be solved by the same system.

When collection discovery has outgrown basic website search, treating search as part of a larger collections architecture can open up much more useful ways for audiences to search, browse, and explore a collection.