Jilll is reviewing what is going with data and biology. There has been an explosion on the numbers they are generating data (from volumes to throughput). Simulations has also been common practices, robot operations, etc. more and more data. Some numbers, now their center use 4.8K processors and 1440+ Terabytes of storage. The challenge give the proper tools to biologist (not CS people). The two key topics of the talk: computation paradigms and computation foundations. They heavily rely on genome expression arrays (row patients, column genes, value expression values). A simple example: classify leukemias (example of how can be distinguished using expression arrays). Patient samples, extract messenger RNA and then create the expression signatures (high dimensionality low training sample set). They repeated the same problem for predicted outcome on prognosis on brain cancer, but for this program there was no strong signal to get them accurate enough. Genes work on regulatory networks (sets of genes), and they tried to do the analysis this way—acting as adding background knowledge to the problem—boosting the results and making the treatment possible. But, the problem is that there should be and infrastructure that could be easy to use and able to replicate experiments. Infrastructure should integrate and interact to components. Should be able to support techs and illiterates equally. Two interfaces (visual and programatic). Access to a library of functions, write pipes, language agnostic, and build on web services (scalable from the laptop to clusters). The name GenePattern. They are collaborating with Microsoft working on a tool (word document) to link to pipelines and the data in the data (can run with other version) and append results to the document too.

Filed by Xavier at 1:37 pm under Events
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Dan Reed (former NCSA director now at Microsoft Research) continues with the meeting presentations. His elevator pitch: the infrastructure need to take into account applications and the user experience. Current trend is that monolithic data consolidation is crumbling under dispersion, changing the traditional picture. The flavors of big data can be explored along two dimensions: (regular/irregular) versus (structured/unstructured). He emphasizes on focusing more on the user experience with big data, and how you can manage resource at any given point. Cloud computing can help organically orchestrate this resources on demand. He also show some examples of Dryad (the Microsoft take on map-reduce architectures) and DryadLINQ. Another interesting comment:

Building simple things ain’t easy.

I definitely agree with this one :D. Finally he mention his initiative to bring academics, business, and users together under the big data problem (PCAST NITRD review).

Filed by Xavier at 12:32 pm under Events
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UIUC CS professor Zhai reviews texts information management. ChenXiang start reviewing the importance of text as a natural way to encode human knowledge. His main focus is how he can provide support for different usages of text information, and how they interact to models, applications, systems and algorithms. This allowed him to motivate future research directions on information retrieval. Some of his interesting words:

Future research directions require improvements on IR and NLP (shallow: POS, partial parsing, fragmental semantic analysis), but it is fragile and domain oriented. Machine learning algorithms are still no scalable and not enough training data to satisfy the algorithm requirements. Data mining has lots of algorithms, but only for salient patterns.

ChengXiang says there is a triangle involving: (1) Keyword queries (search history, complete user models), (2) bags of words (entity-relations, nwoledge representation), (3) search (access, mining, and task support). That leads to personalized search, large-scale semantic analysis, full fledged text information management. On the road there is for sure scalability (he demoed the UCAIR project as a leap toward new search engines). On the large-scale semantics he emphasize the importance of graph representation for the analysis and how you can use graph analysis techniques. And changing gears to a third topic is how you can create multi-resolution topic map for navigation. The basic idea is zoom in and zoom out strategy to drill in and aggregate for the navigation.

Filed by Xavier at 12:05 pm under Events
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Randy opens fire reviewing models of parallelisms and how Google’s Mpa-Reduce model (the core of Yahoo’s Hadoop) is changing the picture. He is emphasizing how data is and integral part of the computational process (which has been greatly unregarded). Map-Reduce model can greatly help because of it fault tolerant capabilities. Now he is reviewing the two traditional parallel programming models (shared model and message-passing model) and how this differ from map-reduce (and how this increases the IO). Initiatives like Hadoop allow to cut-down cost for accessing large scale computing.

Filed by Xavier at 11:31 am under Events
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I am lucky to attend the Big Data Computing Study Group 2008. The line of speaker is impressive. The event is held at Yahoo! Sunnyvale, and Thomas Kwan (UIUC alumni know at Yahoo!) is helping organize it. I will keep blogging about it the rest of the day.

Filed by Xavier at 11:07 am under Events
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