By Andreas L. Symeonidis
AGENT INTELLIGENCE via information MINING deals a self-contained review of a comparatively younger yet very important region of analysis: the intersection of agent expertise and information mining. This intersection ends up in enormous developments within the quarter of knowledge applied sciences, drawing the expanding consciousness of either study and business groups. it will probably take varieties: a) the extra mundane use of clever brokers for better info mining and; b) using information mining for smarter, extra effective brokers. the second one strategy is the main target of this volume.
Knowledge, generally created and maintained by means of today’s functions, is hidden in voluminous facts repositories that may be extracted via facts mining. the next move is to rework this stumbled on wisdom into the inference mechanisms or just the habit of brokers and multi-agent platforms. AGENT INTELLIGENCE via info MINING addresses this factor, in addition to the controversial problem of producing intelligence from information whereas moving it to a separate, very likely independent, software program entity. Following a short overview of knowledge mining and agent expertise fields, this e-book offers a strategy for constructing multi-agent platforms, describes on hand open-source instruments to aid this method, and demonstrates the appliance of the method on 3 various cases.
AGENT INTELLIGENCE via facts MINING is designed for a certified viewers composed of researchers and practitioners in undefined. This quantity is usually appropriate for graduate-level scholars in computing device science.
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Extra info for Agent Intelligence Through Data Mining
It allows Jaql to operate with a variety of different data representations for both input and output, including delimited text files, JSON files, binary files, Hadoop’s SequenceFiles, relational databases, key-value stores or XML documents. Functions are first-class values in Jaql. They can be assigned to a variable and are high-order in that they can be passed as parameters or used as a return value. Functions are the key ingredient for reusability as any Jaql expression can be encapsulated in a function, and a function can be parameterized in powerful ways.
Sakr and A. Liu Fig. 9 Example file co-location in CoHadoop  of Hadoop to co-locate all copies of those related files in the same server. These changes are designed in a way that retains the benefits of Hadoop, including load balancing and fault tolerance. In principle, CoHadoop provides a generic mechanism that allows applications to control data placement at the file-system level. In particular, a new file-level property, called a locator, is introduced and the Hadoop’s data placement policy is modified so that it makes use of this property.
1 The Family of Map-Reduce 33 and data partitioning properties. The Data Loader component is responsible for globally repartitioning data on a given partition key upon loading and breaking apart single node data into multiple smaller partitions or chunks. The SMS planner extends the HiveQL translator  and transforms SQL into MapReduce jobs that connect to tables stored as files in HDFS. Abouzeid et al.  have demonstrated HadoopDB in action running two different application types: (1) a semantic web application that provides biological data analysis of protein sequences, and (2) a classical business data warehouse.
Agent Intelligence Through Data Mining by Andreas L. Symeonidis