Technology Assets to Support Search and Unstructured Data Analytics Projects
FILLING THE TECHNOLOGY GAP
Over the years, and through the experience of hundreds of search and analytics projects, we have seen a number of technology challenges that available commercial or open source platforms cannot solve. To address these issues, we have developed a collection of search engine-independent technology assets to fill in the gaps in clients' search and big data analytics projects.
These assets were created by our engineers during live implementations and have evolved through numerous subsequent projects where they were used and optimized, with substantial input from our consulting clients. By eliminating the need to reinvent the wheel for some repeatable use cases, like enterprise search, these assets can accelerate project timelines and increase speed-to-value.
As part of Accenture, we are continuing to build end-to-end, flexible solutions that help power the most intelligent businesses with search and analytics.
- Create an end-to-end analytics and search system in days, not weeks, and bring immediate business value
- Deployable in the cloud or on-premise
- Provide a risk-free approach to switching between search platforms, such as replacing the Google Search Appliance, migrating from FAST ESP to SharePoint, moving from Solr to Elasticsearch, etc.
- Fill in the gaps in open source and commercial search engine platforms using search engine independent, complementary technology assets
Our technology assets work with a range of search engines, including SharePoint Search, Google Cloud Search, Microsoft Search, Amazon Kendra, Solr, Elasticsearch, and others. Each component can be deployed individually or together to complement and optimize your organization's search architecture.
- Aspire Content Processing: a content processing framework that is now integrated into the Accenture Insights Platform and designed specifically for unstructured and semi-structured data. Aspire Content Processing is commonly used in our consulting engagements; it provides optimal functionality, a wide range of ready-made processing components, a Hadoop implementation, and distributed processing capabilities.
- Content Connectors: search engine independent connectors with built-in early-binding security and metadata capture capabilities.
- Saga Natural Language Understanding (NLU): enables non-data scientists to create and maintain powerful, flexible, tested, and scalable enterprise language models for user interaction and document understanding. It incorporates many language modeling techniques and machine learning into a single, user-friendly semantic framework to handle a wide variety of natural language use cases.
- Search Application UI: an end-user search interface with full source code and can be customized for specific requirements. The use of the API Server and QPL allows the Search UI to be search engine agnostic.
- Query Processing Language (QPL): a query parsing and business rules engine, enabling sophisticated query-side processing to be set up and maintained efficiently. QPL is often deployed with the Search API Server, but also available separately in some implementations, depending on the customer's requirements.
- Search API Server: allows new endpoints to be configured in seconds. These endpoints are backed by scripts that can simply pass the incoming queries to a search engine, perform query manipulation using QPL to increase relevancy, or perform other actions such as database lookups or updates. Results are then amalgamated into a single response returned to the Search UI.
- Admin UI: pluggable admin interface for installation, administration, server management, and health check.
- Staging Repository: an intermediate repository where content can be placed after it has been extracted from a source. This staging repository allows for more efficient content reprocessing without having to reach back to the content source for every processing iteration.
As we have expanded the number of our technology assets over the years, we have also developed an optimized reference architecture for creating scalable and customizable search and big data analytics applications. Built around our technology assets, open source search engines, and other complementary technologies, this proven reference architecture has enabled our clients to create working search systems more quickly and gain business value sooner.
The diagram below shows the reference architecture for a browser-based search application.
- In this example architecture, the search application needs to access a number of disparate content sources (e.g. content management systems, text documents, e-mails, image repositories, social media sites, etc.).
- Connectors acquire data from external sources. Aspire Content Processing (or some other content publishing engines) can then do the heavy lifting to prepare the content for indexing by the search engine.
- Search Application UI provides basic or custom templates for most search use cases, including e-commerce, corporate-wide search, data warehouse analytics, media & publishing, recruiting, and many others. To enable sophisticated query-side processing, execute scripts in "sequential parallel” fashion, and serve relevant results to the Search Application UI, the Search API Server and QPL can be deployed. In some cases, QPL is available separately based on the customer's implementation needs.
- Saga NLU provides automated pipeline construction, state-of-the-art handling of language ambiguity, integrated machine learning, and business-friendly user interfaces for creating and maintaining NLU models.
- In addition, Admin UI provides a centralized, customizable dashboard that allows system admins to holistically configure, manage, and monitor all system components. In some deployments, the Staging Repository can be used as a buffer between front-end load and back-end publishing from the data sources.
- When application requirements call for text mining, machine learning, semantic analysis, or quality metrics, it may be necessary to deploy Aspire in a big data array as part of a Big Data Framework (Hadoop). What may not seem like a big data job (millions of documents) can quickly become one when advanced text mining is required (billions of words, phrases, and semantic relationships).
ACCELERATE RESULTS WITH ACCENTURE'S AIP+
While our technology assets will be available standalone, they are also available as options on AIP+
AIP+ is a comprehensive and scalable solution that allows organizations to get actionable insights and business outcomes, quickly, with a competitive flexible commercial model. Custom solutions can also be created to meet your organization’s needs. Each of AIP+ flexible and agile analytics apps manages the complete end-to-end process, providing organizations with immediate access to the tools needed to make data-driven and intelligent decisions. Learn more about AIP+. Together, AIP+ and Aspire bring a powerful technology stack that modernizes the acquisition, enrichment, analysis, and visualization of unstructured and structured data.
When appropriate to the solution architecture, our technology assets facilitate the efficient delivery and support of custom search solutions, and our clients benefit from increased reliability. We propose using these assets only where there are clear and specific benefits to the project, and any decision to use them is made by our technical team in full consultation with the customer.
Some of our technologies can also be licensed by non-consulting clients and OEMs. In these cases, we offer support and maintenance services for solutions that deploy our technology assets. Support for other solution components (commercial and open source) is also available from us and our partners.
FOR MORE INFORMATION
Contact us for more information about our technology assets and how they can help optimize your search and big data analytics applications.