We realize this is a tough time of year for most students so instead of boring you with a post here are some headlines.

As more Dads do the Christmas shopping, stores decide it’s time for a makeover. (Hint, less pink)

Remember back during the campaign when Republicans criticized the President for rewarding his fundraisers with political appointments? Well guess what . . .

Light vehicle sales hit their highest level since 2007.

Text messaging turns 20. One more year and we can dip our phones in beer. 

GOP offers the President a counter proposal in the fiscal cliff talks.

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The online journal Aeon has published an excerpt from associate professor of history Kate Brown’s forthcoming book Plutopia, which is due out in 2013.

“For the past seven years, I have spent a great deal of time in the radiated traces of the world’s first plutonium plants — the Hanford factory in eastern Washington State and the Mayak plant in the southern Russian Urals. As countries from the Middle East to the Baltics gear up for a new generation of nuclear power reactors, it is worth taking another look at how scientists laid claim to the ‘truth’ to dismiss the testimony of local farmers such as Tom Bailie, and how farmers fought back to cast doubt on the experts,” writes Brown.

Plutopia is about two communities—Richland, Washington and its Soviet parallel, Cheliabinsk-40—created to house operators of the world’s first two plutonium plants. It is the first to narrate in tandem the plutonium disasters in the US and USSR, and Brown argues that it no longer makes sense to tell the two histories separately. She shows that plutonium was a collaborative, transnational project that bound together the lives of working people across the cold war divide.

The excerpt, “A People’s Truth,” appeared online on December 3 and can be read here.

Center for Hybrid Multicore Productivity Research (CHMPR)
Distinguished Computational Science Lecture Series

Parallel Real-Time OLAP on Multi-Core Processors

Frank Dehne
Chancellor's Professor of Computer Science
Carleton University, Ottawa, Canada
http://www.dehne.net

3:00 p.m. Thursday, 6 December 2012, ITE 456, UMBC

One of the most powerful and prominent technologies for knowledge discovery in Decision Support systems is On-line Analytical Processing (OLAP). Most of the traditional OLAP research, and most of the commercial systems, follow the static data cube approach proposed by Gray etal. and materialize all or a subset of the cuboids of the data cube in order to ensure adequate query performance. Practitioners have called for some time for a real-time OLAP approach where the OLAP system gets updated instantaneously as new data arrives and always provides an up-to-date data warehouse for the decision support process. However, major problems for real-time OLAP are significant performance issues with large scale data warehouses. The aim of our research is to address these problems through the use of efficient parallel computing methods. We present a parallel real-time OLAP system for multi-core processors. To our knowledge, this is the first real-time OLAP system that has been parallelized and optimized for contemporary multi-core processors, providing the opportunity for real-time OLAP on large scale data warehouses. Our system allows for multiple insert and multiple query operations (transactions) to be executed in parallel and in real-time. We evaluated our method for a multitude of scenarios (different ratios of insert and query transactions, query transactions with different sizes of results, different system loads, etc.), using the TPC-DS “Decision Support” benchmark data set. The tests demonstrate that our parallel system achieves a significant speedup in transaction response time and a significant increase in transaction throughput. Since hardware performance improvements are currently achieved not by faster processors but by increasing the number of processor cores, our new parallel real-time OLAP method has the potential to enable OLAP systems that are real-time and efficient/feasible for large databases.