DB2 NoSQL JSON enables developers to write applications using a popular JSON-oriented query language created by MongoDB to interact with data stored in IBM DB2 for Linux, UNIX, and Windows. This driver-based solution embraces the flexibility of the JSON data representation within the context of a RDBMS, which provides established enterprise features and quality of service.
he LUPOSDATE SPARQL system supports various approaches to manage RDF data and process SPARQL queries: Index, RDF3X, Stream, Jena and Sesame. Jena [21] and Sesame [3] refer to third-party SPARQL engines. Index is our in-memory Engine presented in [6]. Stream is our stream-based implementation (see [10]). RDF3X is a re-implementation of [14], but is further enhanced with additional optimization strategies.
Query log data for ad targeting
A WWW2006 paper out of Microsoft Research, "Finding Advertising Keywords on Web Pages" (PDF), claims that query log data is particularly useful for ad targeting.
Specifically, the researchers extracted from MSN query logs the keywords some people used to find a given page. They tested using that as one of many features for ad targeting. In their results, it was one of the most effective features.
Very interesting. It has always been harder to target ads to content than to search results because intent is much less clear.
By using the query log data in this way, the researchers were effectively using the intent of the searchers that arrived at the page as a proxy for the intent of everyone who arrived at the page.
Query log data for ad targeting
A WWW2006 paper out of Microsoft Research, "Finding Advertising Keywords on Web Pages" (PDF), claims that query log data is particularly useful for ad targeting.
Specifically, the researchers extracted from MSN query logs the keywords some people used to find a given page. They tested using that as one of many features for ad targeting. In their results, it was one of the most effective features.
Very interesting. It has always been harder to target ads to content than to search results because intent is much less clear.
By using the query log data in this way, the researchers were effectively using the intent of the searchers that arrived at the page as a proxy for the intent of everyone who arrived at the page.
M. Carman, M. Baillie, R. Gwadera, и F. Crestani. SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, стр. 123--130. New York, NY, USA, ACM, (2009)
M. Carman, M. Baillie, R. Gwadera, и F. Crestani. SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, стр. 123--130. New York, NY, USA, ACM, (2009)
M. Carman, M. Baillie, R. Gwadera, и F. Crestani. SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, стр. 123--130. New York, NY, USA, ACM, (2009)
R. Navigli, и P. Velardi. Proceedings of the 14th European Conference on Machine Learning, Workshop on Adaptive Text Extraction and Mining, Cavtat-Dubrovnik, Croatia, стр. 42--49. (2003)