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Ugur Çetintemel, David Laidlaw and Stan Zdonik are part of the newly launched Intel Science and Technology Center (ISTC) in Big Data. The center is based at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The center is primarily affiliated with the Intel Parallel Computing Lab.

The goal of the ISTC is to produce new data management systems and architectures that can help users process “big data” - data collections that are too big, growing too fast, or are too complex for existing information technology systems to handle. The center will also demonstrate the effectiveness of these solutions on real applications in science, engineering, and medicine.

In addition to Cetintemel, Laidlaw, and Zdonik, the center includes leading researchers from MIT, Portland State, Stanford, Tennessee, UCSB, and Washington. Their research spans data-intensive scalable computing, machine learning, computer architecture and domain sciences (genomics, medicine, oceanography, imaging, and remote sensing).

The center is focused on five major research themes:

1. Databases and Analytics: new software platforms for processing massive amounts of data and applying analytics beyond what conventional relational systems can do.

2. Math and Algorithms: algorithms for linear algebra, signal processing, search, and machine learning that scale to tens or hundreds of machines and petabytes of data.

3. Visualization: visualizations and interfaces that allow users to interact with massive data sets, on displays ranging from phones to video walls.

4. Architecture: data processing systems that leverage next generation hardware innovations, such as many-core chips, non-volatile random-access memories, and reconfigurable hardware.

5. Streaming: data processing systems and algorithms that facilitate rapid processing and ingest of data streams.

Zdonik observes, “This project should have a profound impact on the way science and complex analytics are done in the future.” Cetintemel says, “We invite the broader data science community to collaborate with us in this important effort.” Laidlaw adds, “This will be a great opportunity to bring together visualization and big data.”