FAST ESP
core platform has been designed to
allow for modification and customization by content owners and rapid integration
with external data sources. At a high-level, FAST ESP does following tasks:
- Retrieves or accepts content from
different content sources - Transforms all content into an
internal document representation - Analyzes and processes these documents
to allow for enhanced relevancy - Indexes the documents and makes
them searchable - Processes search queries against
these documents - Applies algorithms or business
rule-based ranking to the results - Presents the results along with
navigation options
Lot of people asking about when do we need to consider FAST ESP Search – I would really like to break it up into two structures as follows
Strategic
- Monetizing Search – Visionary Implementation
- Information Assets are core to
your business
Tactical
- Indexing beyond 25M documents or
Content Items - Query Performance > 5 QPS
20+ QPS possible with guaranteed sub second response times - Indexing Extensions from other
content sources - Advanced Linguistic Requirements
- Highly granualr control of indexing
and query processing
Architecture of FAST ESP Versus MOSS 2007
Comparison between FAST ESP and MOSS 2007 Search
|
Capability |
FAST ESP |
MOSS 2007 |
| Query Rate Scalability |
Will deliver a model which can handle up to a thousands QPS (Queries per second). FAST ESP is a product on the market which differs from its competitors because it expressses its query rate in QPS in stead of QPM (Queries per minute). |
QPS is still unknown. |
| High availability | Can be deployed in a distributed manner so thtat there is no single point of failure. | It only uses one index server and has a single point of failure. |
| Taxonomy | Provides contextual search insight information over arbitrary XML content. | Not Available |
| Faceted Navigation on Metadata |
One of the biggest advantages of FAST is that OOTB Entity extraction capability is over 30 different entities (e.g. companies, products, persons, and more). It also supports Deep navigation technology. |
It supports only a shallow faceted search solution based on its results by best bets. |
| Advanced federation |
Supports advanced federation including sending queries to various web search APIs, mixing results, and shallow navigation. |
It only supports federation without mixing of results from different sources and navigation components, but showing them seperatly. |
| Linguistics |
Supports word flexion for 32 languages with high quality of lemmatization. Also spell check like "Did you mean" is available for these languages. There is an ability to tune spelling dictionairess and algorithms and indexing support up to more then 80 languages. |
It only uses stemming algorithms and basis spell checking with not much configuration abilities |
| Relevance tuning |
Has an open architecture and is fully configurable for relevancy. There is a management GUI for business rules. |
It is not easy to configure for tuning relevancy. |
|
Enrichment of indexing and handling search |
Has a document processing framework which provides the customer with 11 pre-configured pipelines for handling various types of content (e.g. HTML, unstructured data like PDFs and Office documents). Each of those pipelines can be customized based on over 100 stages. You are able to define your own pipelines and stages. Stages are developed in Python. |
Not Available |
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