Technology advances in communications, computation, and storage result in huge collections of data, capturing information of value to business, science, government, and society. Data volumes are currently growing faster than Moore's law. Looking forward, the exponential growth is not likely to stop. The huge size of data is imposing big challenges on infrastructure for data storage which can achieve economical scaling to even more than Petabyte, massively parallel query execution, and facilities for analytical processing. Meanwhile, the rise of large data centers and cluster computers has created a new business model, cloud-based computing, where businesses and individuals can rent storage and computing capacity, rather than making the large capital investments needed to construct and provision large-scale computer installations. Cloud-based data storage and management is a rapidly expanding business. We design Cloud-based Database System, which is a data management solution built to support the next generation of information management and large-scale analytics processing. This project aims at researching new database system which can handle the next generation big data application and applied various areas, like medicine/healthcare, mobile communications etc.
TaijiDB is a native cloud-based database system, developed under Renmin University of China. TaijiDB takes HBase and Cassandra as the storage base and combines the Master/Slave structure and P2P structure to support efficient management and query on massive data. This just accords with the meaning of Taiji, so we name our system "TaijiDB".
Online aggregation is a promising solution to achieving fast early responses for interactive ad-hoc queries that compute aggregates on massive data. To process large datasets on large-scale computing clusters, MapReduce has been introduced as a popular paradigm into many data analysis applications. However, typical MapReduce implementations are not well-suited to analytic tasks, since they are geared towards batch processing. With the increasing popularity of ad-hoc analytic query processing over enormous datasets, processing aggregate queries using MapReduce in an online fashion is therefore an emerging important application need. We present a MapReduce-based online aggregation system called COLA, which provides progressive approximate aggregate answers for both single table and multiple joined tables. COLA provides an online aggregation execution engine with novel sampling techniques to support incremental and continuous computing of aggregation, and minimize the waiting time before an acceptably precise estimate is available. In addition, user-friendly SQL queries are supported in COLA. Furthermore, COLA can implicitly convert non-OLA jobs into online version so that users don't have to write any special-purpose code to make estimates.
- X. Zhang, Z. Wang, J. Ai, J. Lu, X. Meng: An Efficient Multi-Dimensional Index for Cloud Data Management. Accepted for publication in the proceedings of the CIKM Workshop on Cloud Data Management(CloudDB2009), November 2, 2009, Hong Kong, China. (full paper)
- Y. Shi, X. Meng, J. Zhao, X. Hu, B. Liu, H. Wang: Benchmarking Cloud-based Data Management Systems. In Proceedings of the CIKM Workshop on Cloud Data Management(CloudDB2010): 47-54, October 30, 2010, Toronto, Canada.
- X. Hu, J. Zhao, X. Meng, Z. Wang, Y. Shi, B. Liu, H. Wang: TaijiDB: A Dual-Core Cloud-based Database System. Journal of Computer Research and Development, Vol.47（Suppl）:433-437, Oct.2010(NDBC2010, Beijing)(the Best Demo Show Award)
- H.Wang, X. Meng, Y.Chai : Efﬁcient Data Distribution Strategy for Join Query Processing in the Cloud. In Proceedings of the CIKM2011 Workshop on Cloud Data Management (CloudDB2011):15-22, October 28, 2011, Glasgow, Scotland, UK.
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- Y. Gan, Y. Shi, X. Meng: COLA: A Cloud-based On-Line Aggregation System (Demonstration). Journal of Computer Research and Development. Vol.49(suppl.): 398-402, 2012, 10. (NDBC2012, Hefei)(the Best Demo Show Award)
- Y. Shi, X. Meng, F. Wang, Y. Gan : HEDC: A Histogram Estimator for Data in the Cloud. In Proceedings of the Fourth International Workshop on Cloud Data Management(CloudDB2012), pages: 51-58, Oct.29, 2012, Maui, USA.
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- Y. Shi, X. Meng, F. Wang, Y. Gan : You can stop early with COLA: Online processing of aggregate queries in the cloud. In Proceedings of the 21st ACM Conference on Information and Knowledge Management(CIKM2012), pages: 1223-1232, Oct.29 - Nov.2, 2012, Maui, USA.(Full paper)
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- Y. Shi, X. Meng, B. Liu: Halt or Continue: Estimating Progress of Queries in the Cloud. In Proceedings of the 17th International Conference of Database Systems for Advanced Applications (DASFAA 2012), pages: 169-184, April 15-19, 2012, Busan, South Korea.
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- Y. Gan, X. Meng, Y. Shi: COLA: A Cloud-based System for Online Aggregation (Demonstration). Accepted for publication in the 29th International Conference on Data Engineering(ICDE2013). April 8-12, 2013, Brisbane, Australia.
- H.Wang, X.Ci, X.Meng:Fast multi-fields query processing in bigtable based cloud systems[C]. WAIM2013. June 2013：142-154.
- Y. Ma, Y. Zhang, Xiaofeng Meng: ST-HBase: A Scalable Data Management System for Massive Geo-tagged Objects. WAIM 2013: 155-166
- Y. Ma, X. Meng, D. Jiang: Mobile Application Integration:Framework, Techniques and Challenges. Chinese Journal of Computers.Vol.26 No.7 July 2013
- Y. Gan, X. Meng, Y. Shi: COLA: Processing Online Aggregation on Skewed Data in MapReduce. Accepted for publication in the 5th International Workshop on Cloud Data Management(CloudDB2013). October 27-November 2, 2013, San Francisco, USA.
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