Accelerated Discovery Lab

Collaborative innovation to scale big data discovery

With much of today's discovery rooted in massive amounts of data distributed across a variety of channels, businesses and scientists struggle to locate and share necessary resources and skills to produce effective business results.The IBM Research Accelerated Discovery Lab allows IBM researchers and partners to work together to tackle the most difficult big data analytics problems across a variety of industries and domains. The technology and resources in the lab enable business analysts and scientists to make new discoveries in their fields more easily, and at a more rapid pace, giving IBM researchers the chance to constantly improve the technology used for discovery. Read the press release

The challenge of extracting insights from Big Data

Big Data analytics is not easy – the path from raw data to insight, or better yet, predictive capability, is still long, error-prone, and expensive. To get there, companies must deal with the four V’s of Big Data – volume, variety, velocity, and veracity.

Data must be acquired and enhanced in type-specific ways to improve quality, and key entities must be identified, and matched across datasets. To understand the data, models must be created, and analytics developed, tested, and then deployed. With the increasing volume and velocity of data, these analytics must run efficiently, typically on highly parallel systems, and the results must be interpreted, often requiring further analysis or visualization.

These various tasks are all part of the discovery process and they require a broad set of skills, most of which are not core competencies of the scientists and businesses that want to gain the insight. Significant collaboration across disciplines, departments, and in many cases, institutions, may be needed as a result.

What the lab offers

The Accelerated Discover Lab provides a complete environment for data analytics facilitating the discovery process and enabling experts in various domains to focus on their investigations. Working alongside researchers with expertise in all aspects of data and analytics, partners can access public and licensed data sets, generous storage facilities, a rich software stack, and a library of analytics, models and analytics tools and frameworks.

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Developing big data analytics solutions

Highlighted projects

Our partners


As one of the world's leading food companies, Mars, Incorporated is committed to helping solve the critical challenges it faces, shared by industry and society. Mars has a strong focus on addressing these "grand challenges" through collaborative scientific research coupled with innovation and entrepreneurship in the food, agriculture, and health sectors. Scientists from IBM Research and Mars, Incorporated established the Consortium for Sequencing the Food Supply Chain, a collaborative food safety platform that will leverage advances in genomics to further our understanding of what makes food safe. As a first step, the consortium will conduct the largest-ever metagenomics study to categorize and understand micro-organisms and the factors that influence their activity in a normal, safe factory environment. This work could be extended into the larger context of the food supply chain, from farm to fork.

The first data samples will be gathered at Mars-owned production facilities, while IBM’s genomics, healthcare and analytics experts will utilize IBM’s Accelerated Discovery THINKLab, a unique collaborative research environment, for the large-scale computational and data requirements of this initiative. Beyond the research, data and findings will be presented in a systematic way to enable affordable and widespread use of these testing techniques.

Thiess, headquartered in Brisbane, Australia, is one of the world's largest contract miners, operating one of the world’s largest mining fleets with more than $3 billion in fixed assets. Thiess partnered with the Accelerated Discovery Lab to increase its production rate, reduce operational down time and cut maintenance costs. Thiess provided domain experience and historical data regarding maintenance, inspection and repair, as well as fluids analysis, environmental data and real-time data from equipment sensors.

Working with IBM researchers at the Accelerated Discovery Lab, Thiess produced economic impact models for each type of equipment failure, accelerated the development of analytics and mathematical modeling approaches, and created a system to drive major reductions in down time and maintenance costs. Thiess is now implementing data-driven solutions in its operations and will document savings over the coming months. Because predictive data analytics are not yet common practice in the natural resources industry, Thiess stands to benefit from a big data solution as yet untapped by its competitors.

Baylor College of Medicine (BCM) seeks to advance human health by integrating patient care and community service with education and fundamental research. To accelerate the discovery of new drugs to treat and cure human diseases such as cancer, Alzheimer's and ALS (Lou Gehrig's Disease), BCM domain experts in computational biomedicine and biology have teamed with IBM experts in analytics and natural language processing. Together, using IBM's Accelerated Discovery Lab as a springboard, this multi-disciplinary team is creating a system to search for overlooked relationships and hidden correlations across the scientific literature, in order to isolate novel testable hypotheses that are clinically relevant. The collaborative use of the Accelerated Discovery Lab's text analytics, domain related knowledge, and rich BCM/IBM Big Data analytics environment allows Baylor and IBM researchers to focus their time on developing new techniques to understand complex biological processes with the hope to find new ways to treat diseases.

Waterfund, a pioneer in global water risk management, set out to create a benchmark cost for water that captures the true cost of water production and motivates capital markets and private equity to underwrite large water infrastructure investment projects – typically funded by municipal bonds or development banks.

IBM researchers in the Accelerated Discovery Lab and Waterfund’s financial engineers developed the Rickards Real Cost Water Index™, a standardized measure of true water cost that serves as a benchmark for helping measure critical water projects on a like-for-like basis. The "calculation agent" developed by IBM Research computes the index through advanced text analytics over publicly reported financial data to identify direct costs such as operating expenses, and hidden costs including interest subsidies and infrastructure grants - scalable statistical analysis is used to estimate missing cost variables.

The index values reflect estimated water production costs for a variety of major global water infrastructure projects ranging from retail to wholesale water utilities. Waterfund publishes the Rickards Real Cost Water Index™ for several regions globally and is working with governments such as Uganda on their water infrastructure needs.

Esri, a Redlands, California-based supplier of geographic information system (GIS) software, and the IBM Research Accelerated Discovery Lab have collaborated on a demonstration that combines social media analytics with geo-spatial analytics and contextual data to enable new insights into a target customer base. IBM's social media analytics analyzes terabytes of social media data to create real time as well as historical personality profiles, The profiles are created using psycho-linguist techniques to model personality traits, values and fundamental needs. These profiles are then enriched and aggregated by geographical location using ESRI's geo-spatial mapping and contextual data capabilities, enabling a never-before-seen view of self-described customer wants and needs - right down to the city block level.

Juniper Networks, an IBM Global Alliance partner, is providing the IBM Research Accelerated Discovery Lab with the Juniper QFabric™ System, SRX Series Services Gateways and MX Series routers, to deliver massive scale and high-performance network connectivity. The Juniper solutions provide a solid networking foundation that supports IBM Research's initiative to accelerate the insight discovery process and research timeline for Big Data analytics projects.