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Data Management

Department of Data Management

NEC Laboratories America, Inc., Cupertino Campus

     In today's world the amount of data is increasing at a staggering pace. Organizations are in critical need to manage data very efficiently and effectively to stay competitive, increase efficiency, and quickly respond to market opportunities and threats. On top of the ever increasing data amounts, emerging data types, such as RFID data, click streams, application execution logs etc., create additional challenges in data management. Although traditional relational database systems have matured after decades of research and development, they only manage small percentage of current enterprise data. As a result, enterprises underutilize their data because of lack of effective data management capabilities. The Data Management Department focuses on conducting world-class research to create cutting edge technologies to address the data management challenges we are facing in today's world.
Projects
 
CloudDB: A Data Store for All Sizes in the Cloud
 

We envision a new architecture of databases that dramatically reduces administration costs while managing a large scale data reliably. The key is to provide data management capability as a service. This vision, called CloudDB, is along with the current trend of "Cloud Computing" that allows users to access IT-related services in the cloud without the knowledge of the underlying technology infrastructure. Cloud Computing is a fundamentally different computing model and traditional data management systems are not designed to work in this new environment. Consequently, data management in cloud computing raises particularly challenging problems, which call for pioneering technology development and innovative solutions.

CloudDB is a comprehensive data management platform in the cloud. The envisioned system would provide data management capabilities as a service to transparently and efficiently support heterogeneous application workloads with identifiable SLA guarantees and end-to-end system management functions. The system will be able to employ heterogonous underlying storage models to effectively meet applications' query and scalability requirements. We propose the achievement of data independence for all underlying specific storage models as the key guiding principle for the system. If the system is able to achieve data independence, the application logic is decoupled from the data processing logic and allows applications to enjoy the benefits of individual storage models, which are optimized for particular purposes, without worrying about the specifics of data processing.

Recent Publications

2013 ICDE

Jennie Duggan, Yun Chi, Hakan Hacigumus, Shenghou Zhu, Ugur Cetintemel:

Packing Light: Portable Workload Performance Prediction for the Cloud. Data Management in the Cloud

2013 ICDE

Wentao Wu, Yun Chi, Shenghuo Zhu, Junichi Tatemura, Hakan Hacigumus, Jeffrey F. Naughton:

Predicting query execution time: are optimizer cost models really unusable?

2013 EDBT

Samer Al-Kiswany, Hakan Hacigumus, Ziyang Liu, Jagan Jankaranarayanan:

Cost Exploration of Data Sharings in the Cloud.


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