Shared State for Client-Server Mining

For many organizations the explosive growth in data collection techniques and database technology has resulted in large and dynamically growing datasets. These organizations are increasingly turning to data mining, the process of extracting useful information from such datasets. These datasets are typically in a remote repository accessible via a local or inter-network. Despite advances in processing speed and networking technology remote data mining is difficult because of the conflicting requirements imposed by the size of the data involved and the interactive aspect of data mining. The size of the datasets prohibit transferring the entire data to the remote client(s). In addition, data mining is often an iterative process with the user tweaking the supplied parameters according to domain-specific knowledge. This compounds the problem of increased response times due to network and server delays. We have shown [32] that often these applications can be structured so that subsequent requests can operate on relatively small summary data structures. Once the summary structure is computed and communicated to the client, interactions can take place on the client without further communication with the server. The summary is based on the snapshot of the actual data at any point in time. If the data is dynamically being modified, the summary is likely to change. In this scenario, the client’s copy of the summary structure must be kept up-to-date. Traditional realizations of this communication employ some form of message passing or remote procedure call (RPC) in order to keep data coherent, are rather cumbersome, and can be inefficient. Programming ease concerns suggest the need for an abstraction of shared state that is similar in spirit to distributed shared memory (DSM) semantics. However, even the most relaxed DSM coherence model (release consistency [17]) can result in a prohibitively large amount of communication for the type of environment in which data mining may typically be performed. These ∗This work is supported in part by NSF grants EIA–9972881, CCR–9702466, CCR–9705594, and CCR-9988361; and an external research grant from Compaq. †CIS Department, Ohio-State University. Email: srini@cis.ohio-state.edu ‡CS Department, University of Rochester. Email: sandhya@cs.rochester.edu

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