Aggregation in traditional database systems is performed in batch mode: a query is submitted, the system processes a large volume of data over a long period of time, and, eventually, the final answer is returned. This archaic approach is frustrating to users and has been abandoned in most other areas of computing. In this paper we propose a new online aggregation interface that permits users to both observe the progress of their aggregation queries and control execution on the fly. After outlining usability and performance requirements for a system supporting online aggregation, we present a suite of techniques that extend a database system to meet these requirements. These include methods for returning the output in random order, for providing control over the relative rate at which different aggregates are computed, and for computing running confidence intervals. Finally, we report on an initial implementation of online aggregation in POSTGRES.
Aggregation in traditional datab=e systems is performed in batch mode: a query is submitted, the system processes a large volume of data over a long period of time, and, eventually, the final answer is returned. This archaic approach is frustrating to users and has been abandoned in most other areas of computing. In this paper we propose a new online aggregation interface that permits users to both observe the progress of their aggregation queries and control execution on the fly. After outlining usability and performance requirements for a system supporting online aggregation, we present a suite of techniques that extend a database system to meet these requirements, These include methods for returning the output in random order, for providing control over the relative rate at which different aggregates are computed, and for computing running confidence intervals. Finally, we report on an initial implementation of online aggregation in POSTGRES.
Access is the killer app" 3 is the vision of the Daedalus project at U.C. Berkeley. Being able to be connected seamlessly anytime anywhere to the best network still remains an unful lled goal. Often, even determining the best" network is a challenging task because of the widespread deployment of overlapping wireless networks. In this report, we describe a policy-enabled hando system that allows users to express policies on what is the best" wireless system at any moment, and make tradeo s among network characteristics and dynamics such as cost, performance and power consumption. We designed a performance reporting scheme estimating current network conditions, which serves as input to the policy speci cation. A primary goal of this work is to make it possible to balance the bandwidth load across networks with comparable performance. We identi ed the problem of hando instability that may becaused by hando synchronization, i.e., the scenario of many mobile hosts making the same hando decision at essentially the same time. We use randomization to break such synchronizations. Given the current best" network, our system also determines whether the hando is worthwhile based on the hando overhead and potential network usage duration.
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