By Andreas L. Symeonidis
AGENT INTELLIGENCE via facts MINING deals a self-contained evaluation of a comparatively younger yet vital zone of analysis: the intersection of agent expertise and knowledge mining. This intersection ends up in substantial developments within the zone of knowledge applied sciences, drawing the expanding recognition of either examine and commercial groups. it might probably take types: a) the extra mundane use of clever brokers for greater facts mining and; b) using facts mining for smarter, extra effective brokers. the second one process is the focus of this volume.
Knowledge, frequently created and maintained by way of today’s functions, is hidden in voluminous information repositories that may be extracted by way of information 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 debatable problem of producing intelligence from information whereas moving it to a separate, probably self sufficient, software program entity. Following a short evaluate of knowledge mining and agent expertise fields, this publication offers a technique for constructing multi-agent structures, describes on hand open-source instruments to help this procedure, and demonstrates the appliance of the technique on 3 diverse cases.
AGENT INTELLIGENCE via facts MINING is designed for a qualified viewers composed of researchers and practitioners in undefined. This quantity can also be appropriate for graduate-level scholars in desktop science.
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The first step in solving this problem is specifying a point just after the 12th digit of a 16-digit number. Example 3-6 shows how to use XPointer for this purpose. Note that the syntax of any final XPointer Recommendation may differ somewhat from that used here. Example 3-6 xpointer (//Payment/Card/Number /child::text()/point()[position()=12]) The first part of this XPointer expression uses XPath syntax to narrow the scope of the match to the "Number" element of the "Card" element of any "Payment" element within the document.
Resolving a Conflict with Namespaces 38 39 When the Accounting application processes the document, it wants to use "Status" as a keyword to signify billing status. When the Fulfillment application processes the document, it wants to use "Status" as a keyword to signify shipping status. Moreover, the same problem extends to other keywords. Example 3-1 shows how to solve this problem using XML Namespaces. com/names/ful-REV10">
The vertical domain standards provide formats for describing information specific to a particular industry but used by many types of applications. Examples include interoperability formats for finance, health care, and telecommunications. They generally facilitate compatibility among systems used within the same industry. Their applicability depends on whether your organization works with information from a particular industry. Table 3-1. XML Feature Standards Standard XML Namespaces XML Path Language XML Pointer Language XML Query Language XSL Transformations Extensible Stylesheet Language XML Linking Language AbbreviationPurpose Namespaces Prevent overlap of names used by different software XPath Address data within a document XPointer Specify locations within a document XQuery Search for data within a set of documents XSLT Transform data from one format to another XSL Present data in a paginated format XLink 37 38 Link data from different documents XML Schema N/A Specify more extensive format definition rules than DTDs Table 3-1 lists the most important feature standards.
Agent Intelligence Through Data Mining by Andreas L. Symeonidis