In this final installment, we have summarized the scenario, requirements, and features covered in the series and provided a glimpse into the future of IBM's ESB technologies.
在这最后的一部分中，我们概述了本系列所介绍的场景、需求和功能，并且简单地展望了IBM的ESB技术的未来。
Future articles in this series will take you deeper into the use of new V5.1.0.1 APIs.
本系列的后续文章将带您更深入地了解新V 5.1.0.1API的使用。
Future articles in this series will go into more detail about some of the other features in the list.
在本系列专题中，以后的文章将深入研究列表中的其它功能。
In future articles in this series, we'll look into the details of how to implement a CallbackHandler in detail.
在此系列文章的后续文章中，我们将详细探讨如何实现CallbackHandler。
In future versions of OmniFind, beyond the 8.2.1 version that was used to develop the examples for this article series, the functionality of the Data Listener API was incorporated into the SIAPI.
在未来的OmniFind版本中，除了用于开发本系列文章中的例子的8.2.1版以外，DataListenerAPI的功能均被合并进SIAPI中。
Future articles in this series go in depth into programming the Cell BE and extracting every ounce of speed that you can from the SPEs.
本系列的后续文章将深入介绍CellBE编程以及如何充分利用可从SPE获得的每一点速度。
Prominent among the various techniques that can help to extrapolate past date into future trends are the following:time series, least squares method, exponential smoothing, regression and correlation.
由历史数据推测未来趋势的众多方法中较突出的有：时间序列法、最小平方法、指数平滑法、回归分析和相关分析。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
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