There are many search engines, some are open source and some are paid. The trend in search technology shows an affinity towards cognitive and artificial intelligence now.
The common Big data used with searches are:
Keyword Search -> Semantic -> Contextual -> Cognitive -> (Human Brain)
Enterprise search now uses natural language processing and machine learning which dramatically improve the results relevancy and completeness.
Here cognitive search is based on Artificial Intelligence.
Which search is the best?
I always suggest the one that meets your needs in your environment and requirements.
Process of identifying the needs
Understand any data
Search solutions must connect to and ingest data from a wide variety of source. For e.g.: Data types ranging from images, video, audio, and machine data such as from internet-of-things (IoT) devices.
Scale to handle big
Now the data is in peta bytes which resides in distributed architectures.
The migration process
Provide transparent, solid experience to the end users, Educate the users about new system.
Install base and revenue history
A proven stream of revenue generated by customer adoption of its solution and installation market presence.
Cross-domain standalone solution, Employ AI technologies
The solution is self-sufficient, general-purpose cross domain one now. Understand and organize data, predict, improve relevancy, and automatically tune the relevancy of results over time using AI Techniques.
Allow developers to customize search applications
Currently search vendors provide SDKs, Apis, and in some cases visual design tools to customize search to a maximum extend.
List out your semi finalists then finalists. Then rematch the requirement with finalists and decide the winner.
Read More
Steps to implement any search technology
AEM Dispatcher, why it is needed?
AEM Desktop App
Figure out the best search technology or tool
Steps to implement search in Solr
Quality of Search - fine tuning search implementation
- Solr
- Elasticsearch
- Google Search Appliance (GSA)
- Oracle Endeca
- Microsoft FAST
- Attivio
- Sinequa
- Coveo
- IBM Watson
- Amazon CloudSearch
- SharePoint Search
- HP Autonomy
The common Big data used with searches are:
- Apache Hadoop
- Cloudera
- Hortonworks
Keyword Search -> Semantic -> Contextual -> Cognitive -> (Human Brain)
Enterprise search now uses natural language processing and machine learning which dramatically improve the results relevancy and completeness.
Here cognitive search is based on Artificial Intelligence.
Which search is the best?
I always suggest the one that meets your needs in your environment and requirements.
Process of identifying the needs
Auditing the current system
Understand the current system and new requirements.
OS & Systems
Think of operating system to be hosted. On-Premise Vs Cloud Vs Hybrid
Dev tools
Think on the tools to develop while search is getting revamped
Repositories
Think about content repository and ensure product upgrade history is clean.
Security
Are there any new security levels to be added?
Content
Think on the content when search system upgrades are in place
Users
External Vs internal? The behavior & type of users.
Understand the current system and new requirements.
OS & Systems
Think of operating system to be hosted. On-Premise Vs Cloud Vs Hybrid
Dev tools
Think on the tools to develop while search is getting revamped
Repositories
Think about content repository and ensure product upgrade history is clean.
Security
Are there any new security levels to be added?
Content
Think on the content when search system upgrades are in place
Users
External Vs internal? The behavior & type of users.
Understand any data
Search solutions must connect to and ingest data from a wide variety of source. For e.g.: Data types ranging from images, video, audio, and machine data such as from internet-of-things (IoT) devices.
Scale to handle big
Now the data is in peta bytes which resides in distributed architectures.
The migration process
Provide transparent, solid experience to the end users, Educate the users about new system.
Install base and revenue history
A proven stream of revenue generated by customer adoption of its solution and installation market presence.
Cross-domain standalone solution, Employ AI technologies
The solution is self-sufficient, general-purpose cross domain one now. Understand and organize data, predict, improve relevancy, and automatically tune the relevancy of results over time using AI Techniques.
Allow developers to customize search applications
Currently search vendors provide SDKs, Apis, and in some cases visual design tools to customize search to a maximum extend.
List out your semi finalists then finalists. Then rematch the requirement with finalists and decide the winner.
Read More
Steps to implement any search technology
AEM Dispatcher, why it is needed?
AEM Desktop App
Figure out the best search technology or tool
Steps to implement search in Solr
Quality of Search - fine tuning search implementation
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