HP Labs Israel

HP Laboratories Israel (HPL-I) is located on the campus of Technion - Israel Institute of Technology, which sits on Mount Carmel, overlooking the city of Haifa.

HP Labs Israel is an excellence center in imaging science, learning, and automation technologies. Along with innovative research in these areas, HP Labs Israel is focused on bridging disruptive technology gaps on the roadmaps of HP's businesses. The two primary focus areas of HP Labs Israel are workflow automation for commercial printing and machine learning and knowledge extraction for IT service automation.

The commercial printing workflow project is focused on our core competencies in digital imaging and printing. We are working to provide technology and software applications to deliver digital printing solutions that are more reliable, provide higher print quality and offer better level of automation by incorporating image processing and analysis technologies, machine learning methods, color science and systems software.

The expected impact is:

• Reducing the cost of ownership and operation of HP's commercial printing presses
• Increasing digital printing reliability and reducing waste using automated print inspection, just-in-time defect detection and problem diagnosis as an integral part of the printing workflow
• Facilitating faster, more efficient creation of complex digital content to drive higher volume of digitally printed pages
• Reducing the cost of managing the IT infrastructure for the print shop provider (PSP) environment

Research topics:

• Automatic image processing, analysis and understanding
• Pattern analysis and computer graphics technology
• Perceptual color measures based on simple sensors
• Commercial printing software applications
• Data mining and event clustering
• Structural and contextual print job understanding

The IT informatics project is focused on our core competencies in machine learning and optimization. We are working to provide technology and algorithms to extract and aggregate business software interaction patterns representing typical business processes. The goal is to achieve better automation throughout the lifecycle of business applications from the requirement specification, development, testing, deployment, problem isolation and resolution, and back to specification. We employ novel sparse optimization and robust metrics to improve data analysis, and use image analysis and computer vision to mine and understand end-user interactions.

The expected impact is:

• Improved execution through automation of recurring or repetitive IT tasks
• Quicker and more accurate IT problem resolution using knowledge created from unstructured or untapped data scattered throughout the organization
• Better operational agility using shareable business process encapsulation modules
• Better informed management decisions from accurate assessments of actions and usage modes on business services

• Text and temporal data mining
• Machine learning for event sequence clustering and classification
• GUI image analysis and understanding
• Layout analysis and planar graph parsing
• User and domain modeling
• Optimization technologies
• Visualization

Director: Doron Shaked