Jump to content United States-English
HP.com Home Products and Services Support and Drivers Solutions How to Buy
» Contact HP

HP Labs home

Intelligent Information Management Lab

» 

HP Labs

» Research
» News and events
» Technical reports
» About HP Labs
» Careers @ HP Labs
» People
» Worldwide sites
» Downloads

Goal

Enable near real-time business intelligence with robust, scalable data management, data-intensive analytics and fusion of structured and unstructured information.

 

Impact

Information is a critical enterprise asset displaying exponential growth. Information can be analyzed for decisions and new insights, and turned into knowledge delivered into user contexts.  It powers modern businesses and scientific research and is the lifeblood of enterprise business processes.

Research in intelligent information management builds on today’s business intelligence  and related information-management technology foundation, and dramatically expands its horizon to achieve near real-time data capture and integration, adaptive and closed-loop operational business intelligence supporting a wide user base, data-intensive parallel analytics, and fusion of business intelligence with search and information extraction over structured and unstructured information from a wide variety of internal and external sources and data feeds.

The research draws requirements from emerging applications ranging from consumer industries delivering better customized products and services, supply chains synchronizing with real-time demand signals and scientists improving their ability to access and analyze massive amounts of data.

 

Research

  • real-time continuous feed of business events into business intelligence systems and stream data processing to reduce the latency between the time an event occurs and the time quality data is available for decision-making
  • workload management techniques to enable self-managing, adaptive tuning for complex, mixed workloads, and robust query processing, storage and access methods that adapt gracefully to changing workload
  • algorithms for scalable data-intensive analytics, including real-time adaptation of models and extraction of information from unstructured data
  • fusion of search, extraction, query and analytics to enable integration of structured and unstructured information in solution contexts
  • use of parallel architecture on multi-core clusters and emerging memory hierarchy and storage devices in large-scale data management, and use of distributed infrastructure to further scale across geographical and organizational boundaries
  • emerging, innovative vertical applications enabled by the next generation information platform

Director: Meichun Hsu  


Printable version
Privacy statement Using this site means you accept its terms Feedback to HP Labs
© 2009 Hewlett-Packard Development Company, L.P.