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A System and Method to Support User-Defined Prefetching. Disclosure Number: IPCOM000201544D
Publication Date: 2010-Nov-15

Publishing Venue

The Prior Art Database


Introduction/Abstract Disclosed is a System and Method to Support User-Defined Prefetching. A long-standing trend in enterprise information systems is the increased componentization for greater modularity, reusability, and better time-to-market. The components could be SOA services, widgets-based components (iWidget, yahoo! pipes, etc.), Object-oriented components, or proprietary components such as COM/DCOM. These components are often deployed in a distributed manner. Hence, the communication efficiency among these components varies with the network proximity of the involved components.

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A System and Method to Support User -Defined Prefetching.

The following describes one possible way of implementing the ideas proposed earlier. The prediction method and the pattern identification modules below are quite simplified and are meant to provide a proof of concept only. A mature implementation would make use of the state-of-the-art prediction techniques and employ the latest research on identifying patterns in sequences of data.

An alternative way to implement the customised prefetching would be to let the system pass the parameters generated by the modules to the onPrefetch function at the runtime. The parameters would be received by the onPrefetch function and then it would prefetch the data according to the rules and logic defined by the component designer in the onPrefetch module. The structure of the overloaded onPrefetch would take the following form :



Event, Probability, Event



. . .




Event, Time


Slice, Time




. . .


Hence the proposed system and method is capable of handling customized prefetching both at the design time ( Using APIs) and at the runtime by passing the values of probability and its distribution as and when the state of the system changes.



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The proposed system and method consists of an Event Inference Manager (EIM) having the following modules:

1. Probability generator - Monitors data requests being triggered in the system by the various components and ased on the historical patterns repository, generates the probability of all future data requests. The predictions are updated at a regular time interval, e.g., one second.

2. Time-Varying probability distribution generator - For each predicted data requests from 1, generates the future time point at which the data request has


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the highest probability of occurrence. In addition, generates a discrete probability distribution around such a time point of the occurrence of the data request.

3. Historical patterns repository - Identifies patterns in the raw sequences of data requests and stores them in a repository, called Event Sequences and their occurrence frequencies (ESOF) and also the time of occurrence (TOC) which is a pointer to the time distribution table entry of that Event Sequence.

4. Prefetching Module - Integrates all the information provided by the other modules of EIM and notifies relevant components of the prediction parameters regularly. Also, implements the APIs for prefetching including prefetching proportional to the time varying probability of occurrence.

The Strucutre of Historical Patterns Repository

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1. The Probability Generator is a module that takes the current Event Sequence


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and searches in...