New PDF release: Agent Intelligence Through Data Mining

By Andreas L. Symeonidis

ISBN-10: 0387243526

ISBN-13: 9780387243528

ISBN-10: 0387257578

ISBN-13: 9780387257570

AGENT INTELLIGENCE via info MINING deals a self-contained evaluate of a comparatively younger yet vital zone of study: the intersection of agent know-how and information mining. This intersection results in huge developments within the sector of data applied sciences, drawing the expanding awareness of either examine and business groups. it could actually take varieties: a) the extra mundane use of clever brokers for greater info mining and; b) using info mining for smarter, extra effective brokers. the second one process is the main target of this volume.

Knowledge, generally created and maintained by means of today’s functions, is hidden in voluminous information repositories that may be extracted by means of information mining. your next step is to rework this came upon wisdom into the inference mechanisms or just the habit of brokers and multi-agent platforms. AGENT INTELLIGENCE via facts MINING addresses this factor, in addition to the controversial problem of producing intelligence from facts whereas shifting it to a separate, most likely independent, software program entity. Following a short assessment of knowledge mining and agent expertise fields, this e-book provides a technique for constructing multi-agent structures, describes on hand open-source instruments to help this approach, and demonstrates the applying of the method on 3 diverse cases.

AGENT INTELLIGENCE via info MINING is designed for a qualified viewers composed of researchers and practitioners in undefined. This quantity is additionally compatible for graduate-level scholars in computing device science.

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If using the fixed-size chunk approach, the combination is more delicate. For this example, data for a particular year will typically be split into several chunks, each processed independently. We’ll end up with the maximum temperature for each chunk, so the final step is to look for the highest of these maximums, for each year. Third, you are still limited by the processing capacity of a single machine. If the best time you can achieve is 20 minutes with the number of processors you have, then that’s it.

Grid Computing The High Performance Computing (HPC) and Grid Computing communities have been doing large-scale data processing for years, using such APIs as Message Passing Interface (MPI). Broadly, the approach in HPC is to distribute the work across a cluster of machines, which access a shared filesystem, hosted by a SAN. This works well for predominantly compute-intensive jobs, but becomes a problem when nodes need to access larger data volumes (hundreds of gigabytes, the point at which MapReduce really starts to shine), since the network bandwidth is the bottleneck and compute nodes become idle.

When converting your Mapper and Reducer classes to the new API, don’t forget to change the signature of the map() and reduce() methods to the new form. Just changing your class to extend the new Mapper or Reducer classes will not produce a compilation error or warning, since these classes provide an identity form of the map() or reduce() method (respectively). Your mapper or reducer code, however, will not be invoked, which can lead to some hard-to-diagnose errors. Example 2-6. waitForCompletion(true) ?

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Agent Intelligence Through Data Mining by Andreas L. Symeonidis


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