asp.net ean 13

asp.net ean 13

asp.net ean 13

asp.net ean 13

asp.net ean 13

asp.net ean 13,

asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,

asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,

asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,
asp.net ean 13,

False positives may occur because irrelevant documents get higher rankings than relevant documents This too depends on how many documents are examined One option is to measure precision as a function of number of documents fetched A better and more intuitive alternative for measuring precision is to measure it as a function of recall With this combined measure, both precision and recall can be computed as a function of number of documents, if required For instance, we can say that with a recall of 50 percent the precision was 75 percent, whereas at a recall of 75 percent the precision dropped to 60 percent In general, we can draw a graph relating precision to recall These measures can be computed for individual queries, then averaged out across a suite of queries in a query benchmark Yet another problem with measuring precision and recall lies in how to de ne which documents are really relevant and which are not In fact, it requires understanding of natural language, and understanding of the intent of the query, to decide if a document is relevant or not Researchers therefore have created collections of documents and queries, and have manually tagged documents as relevant or irrelevant to the queries Different ranking systems can be run on these collections to measure their average precision and recall across multiple queries

Account assignments: How are account assignment changes made under what conditions What sales credit rights do sales personnel have after accounts move in or out of territories When are accounts moved for temporary purposes How should temporary assignment of accounts affect sales credit and quotas What happens when external account factors affect account status such as when companies are moved, acquired, or shuttered Who approves account changes Quota management: How is quota allocation done How can quotas be changed What is the petition process for quota adjustments How will quota changes affect incentive compensation Who approves quota allocation assignments and quota assignment changes

.

ciscoasa(config)# class-map L3_ilsldap ciscoasa(config-cmap)# match port tcp eq 389 ciscoasa(config)# policy-map global-policy ciscoasa(config-pmap)# class L3_ilsldap ciscoasa(config-pmap-c)# inspect ils ciscoasa(config)# service-policy global-policy global

Web crawlers are programs that locate and gather information on the Web They recursively follow hyperlinks present in known documents to nd other documents A crawler retrieves the documents and adds information found in the documents to a combined index; the document is generally not stored, although some search engines do cache a copy of the document to give clients faster access to the documents Since the number of documents on the Web is very large, it is not possible to crawl the whole Web in a short period of time; and in fact, all search engines cover only some portions of the Web, not all of it, and their crawlers may take weeks or months to perform a single crawl of all the pages they cover There are usually many processes, running on multiple machines, involved in crawling A database stores a set of links (or sites) to be crawled; it assigns links from this set to each crawler process New links found during a crawl are added to the database, and may be crawled later if they are not crawled immediately Pages found during a crawl are also handed over to an indexing system, which may be running on a different machine Pages have to be refetched (that is, links recrawled) periodically to obtain updated information, and to discard sites that no longer exist, so that the information in the search index is kept reasonably up to date The indexing system itself runs on multiple machines in parallel It is not a good idea to add pages to the same index that is being used for queries, since doing so would require concurrency control on the index, and affect query and update performance Instead, one copy of the index is used to answer queries while another copy is updated with newly crawled pages At periodic intervals the copies switch over, with the old one being updated while the new copy is being used for queries.

Note that the class map for ILS/LDAP is unnecessary in this example, since the appliance expects ILS/LDAP connections to be using TCP 389 however, if this were different, you would need to use a class map to indicate the correct connection parameters

The McGraw Hill Companies, 2001