This demo presents AstroShelf, our on-going effort to enable astrophysicists to collaboratively investigate celestial objects using data originating from multiple sky surveys, hosted at different sites. The AstroShelf platform combines database and data stream, workflow and visualization technologies to provide a means for querying and displaying telescope images (in a Google Sky manner), visualizations of spectrum data, and for managing annotations. In addition to the user interface, AstroShelf supports a programmatic interface (available as a web service), which allows astrophysicists to incorporate functionality from AstroShelf in their own programs. A key feature is Live Annotations which is the detection and delivery of events or annotations to users in real-time, based on their profiles. We demonstrate the capabilities of AstroShelf through real end-user exploration scenarios (with participation from "stargazers" in the audience), in the presence of simulated annotation workloads executed through web services.

Data streams have become pervasive and data production rates are increasing exponentially, driven by advances in technology, for example the proliferation of sensors, smart phones, and their applications. This fact effectuates an unprecedented opportunity to build real-time monitoring and analytics

Complex event detection over data streams has become ubiquitous through the widespread use of sensors, wireless connectivity and the wide variety of end-user mobile devices. Typically, such event detection is carried out by a data stream management system executing continuous queries (CQs), registered by the users. In this paper, we consider the situation where the results of the CQs, which are in the form of individual data streams, are disseminated to the users' hand-held, battery-operated devices over a shared broadcast medium. In order to reduce the overall energy consumption of the mobile devices, we propose Bose*, a power-aware query operator placement algorithm that determines which part of a CQ plan should be executed at the data stream management system and which part should be executed at the mobile device. Bose*'s effectiveness in reducing energy consumption, as well as response time under specific conditions, is evaluated using simulation, driven by parameters measured on real mobile devices.

Complex event detection over data streams has become ubiquitous through the widespread use of sensors, wireless connectivity and the wide variety of end-user mobile devices. Typically, event detection is carried out by a central server executing continuous queries. In this demonstration, we focus on the case where users with mobile devices submit continuous queries (for event detection) to a data stream management server which disseminates the results to the users over a shared broadcast medium. In order to minimize the overall energy consumption of the mobile devices (clients), we have proposed operator placement algorithms that split the processing of each continuous query between the centralized server and the requesting mobile clients, thus trading off energy consumption for communication energy consumption for computation. Specifically, in this demonstration, we present an interactive graphical interface to the inner workings of our three proposed operator placement algorithms, whereby attendees are able to investigate various query plans and the decisions that the algorithms make, as well as visualize the results of these algorithms in terms of client power consumption and response time. Besides being able to step through an algorithm's execution as it considers various operator placement decisions, attendees are able to experiment with different scenarios by customizing the parameters of the query workloads (e.g., changing the selectivities and projectivities of the operators) or the client's profile (e.g., power consumed per unit of time of processing) and examine the impact.

Traditional workflow enactment systems view a workflow as a one-time interaction with various data sources, executing a series of steps once, whenever the workflow results are requested. The fundamental underlying assumption has been that data sources are passive and all interactions are structured along the request/reply (query) model. Hence, traditional Workflow Management Systems cannot effectively support business or scientific reactive applications that require the processing of continuous data streams.

Amazon, Google, and IBM now sell cloud computing services.We consider the setting of a for-profit business selling data stream monitoring/management services and we investigate auction-based mechanisms for admission control of continuous queries. When submitting a query, each user also submits a bid of how much she is willing to pay for that query to run. The admission control auction mechanism then determines which queries to admit, and how much to charge each user in a way that maximizes system revenue while being strategyproof and sybil immune, incentivizing users to use the system honestly. Specifically, we require that each user maximizes her payoff by bidding her true value of having her query run. We design several payment mechanisms and experimentally evaluate them. We describe the provable game theoretic characteristics of each mechanism alongside its performance with respect to maximizing profit and total user payoff.

Net-dbx-G is a tool that utilizes Java and other World Wide Web tools as an interface to Grid services to help Grid application developers debug their MPI programs from anywhere in the Internet. Net-dbx-G is a source level debugger with the full power of gdb (the GNU Debugger), providing the user with full Grid functionality. The portability of the tool is of great importance as well because it enables the tool to be used on heterogeneous nodes that participate in an MPI enabled, Grid environment. The users of our system simply point their browser to the Net-dbx-G webpage and authenticate to the virtual organization that they are part of. Having at their disposal the shared resources participating with that VO, they start debugging by interacting with the provided GUI environment of the tool. The users can dynamically select which MPI processes to view/debug.