Events

I attended four papers (you can check the abstracts of Friday papers here): paper 304, 323, 538, and 434. On of the paper has nobody to present it. There were quite a bunch of interesting ideas. I just want to mention one of them. Two papers presented several methodologies to model SOA. Another interesting approach was the claim (papers 304 and 434) that SOA can be used for modeling business processes, and have little to do with information technologies. They claimed that such a connection to information technologies should be done once the SOA business processes are well defined. This may sound quite estrange if you are coming from a technical background. However, it is not far fetch. It just sound like a reincarnation of the late nineties hype on business reengineering via functional designs—which SAP rided so successful. Interestingly Jorge Cardoso also hinted this direction during the morning panel. ...

The hotel finally got the wireless system rebooted, so I am finally back up. I found quite unusual to open the ICEIS 2008 conference with a panel form by the four invited speakers: Moira Norrie (Global Information System Group, ETH), Jorge Cardoso (SAP), Jean-Marie Favre (UFR, IMA), Ricardo Baeza-Yates (Yahoo! Research Barcelona). Each of them did a short introduction of their invited talks, and just three questions were asked. Since the conference started at 11am, after the panel, lunch break :D ...

It has been a greet meeting. Lots of interesting ideas and a lot to explore from now on. Just what I like :D. I summarized below the list of post I make related to the meeting. Introductory post [Data-Intensive Scalable Computing. Randy Bryant, CMU](/posts/data-intensive-scalable-computing-randy-bryant.md" >}}) Text Information Management: Challenges and Opportunities. ChengXiang Zhai, UIUC Clouds and ManyCore: The Revolution. Dan Reed, MSR Computational Paradigms for Genomic Medicine. Jill Mesirov, Broad Institute of MIT and Harvard Simplicity and Complexity in Data Systems (Garth Gibson) Handling Large Datasets at Google: Current Systems and Future Directions. Jeff Dean, Google Algorithmic Perspectives on Large-Scale Social Network Data. Jon Kleinberg, Cornell Mining the Web Graph. Marc Najork, MSR “What” Goes Around. Joe Hellerstein, Berkeley Sherpa: Hosted Data Serving. Raghu Ramakrishnan, Yahoo! Scientific Applications of Large Databases. Alex Szalay, JHU Data-Rich Computing: Where It’s At. Phil Gibbons, Intel NSF Plans for Supporting Data Intensive Computing: Jeannette Wing, NSF. The Google/IBM data center: Christophe Bisciglia, Google

I was lucky to attend the Big Data Computing Study Group 2008. The line of speaker is impressive. The event was held at Yahoo! Sunnyvale, and Thomas Kwan (UIUC alumni know at Yahoo!) helped organize it. I blogged about it on my DITA blog where you can find links to all the related posts.

NSF listens at you academics. Jeannete opens the floor with this claim. Questions: What are the limitations of this modeling paradigm (data-intensive one)? What are meaningful metrics of performance here? What about security processes and data on a shared resource? How can we reduce power consumption? Can this parading problem not possible otherwise, or simplify them, or open the door to new applications? NSF rolling out cluster exploratory program, also going to roll out a new solicitation for Data-Intensive Computing. Also emphasizing from data to knowledge, since scientist are throwing it away. This is a great opportunity for collaborative efforts between CS and scientist. NSF goal: provide access to cluster resource and access to massive data sets. Google and IBM rolling out the cluster (for academics). NSF will roll out a cluster exploratory will be the solicitation program announced yesterday to distribute access to the cluster and research grants. Review of Christophe experience on teaching a class about clustering, and he realized that providing away computer cycles is more valuable than plain grant money. It runs on Hadoop. The cluster will be allocate by rack weeks, 5 Terabytes and priority on 80 processes (but still people there and lower priority and large data sets). And since the reviewing was not Google expertise they reach to NSF to use it. Googler to start collaborations and IBM will also help providing support for it. Jeannette claiming this is a new model, but NSF is open for new model and other partners. ...