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.
With this Web page, we are opening some aspects of hakia R&D to the view of our users. We undertook highly specific research tasks solely dedicated to the advancement of the core-competency in Web search. The main challenge is to make science work in a co
The Doctrine Project is the home to several PHP libraries primarily focused on database storage and object mapping. The core projects are a Object Relational Mapper (ORM) and the Database Abstraction Layer (DBAL) it is built upon.
Criteria queries allow for multiple root level objects. Caution should be used when doing this, as it can result in Cartesian products of the two table. The where clause should ensure the two objects are joined in some way.
// Select the employees and the mailing addresses that have the same address.
CriteriaBuilder criteriaBuilder = entityManager.getCriteriaBuilder();
CriteriaQuery criteriaQuery = criteriaBuilder.createQuery();
Root employee = criteriaQuery.from(Employee.class);
Root address = criteriaQuery.from(MailingAddress.class);
criteriaQuery.multiselect(employee, address);
criteriaQuery.where(criteriaBuilder.equal(employee.get("address"), address.get("address"));
Query query = entityManager.createQuery(criteriaQuery);
List<Object[]> result = query.getResultList();
QueryBuilder can be used on advanced search engine pages, administration backends, etc. to build complex queries or filters. It is highly customisable and can be used with many jQuery widgets like autocompleters and sliders.
It outputs a structured JSON of rules which can be easily parsed to create SQL/NoSQL/whatever queries.
MRQL (the Map-Reduce Query Language) is an SQL-like query language for map-reduce computations. It is implemented on top of Apache's Hadoop. MRQL is powerful enough to express most common data analysis tasks over many different kinds of raw data, including hierarchical data and nested collections, such as XML data. It is more powerful than other current languages, such as Hive and Pig Latin, since it can operate on more complex data and supports more powerful query constructs, thus eliminating the need for using explicit map-reduce code.
select min(seq) seq,state,count(*) numb_ops, -> round(sum(duration),5) sum_dur, round(avg(duration),5) avg_dur, -> round(sum(cpu_user),5) sum_cpu, round(avg(cpu_user),5) avg_cpu -> from information_schema.profiling -> where query_id = 7 -> group by state -> order by seq;
"Nesting performs a join across two buckets. But instead of producing a cross-product of the left and right hand inputs, a single result is produced for each left hand input, while the corresponding right hand inputs are collected into an array and nested as a single array-valued field in the result object."
Couchbase Query Language, known as N1QL and pronounced "Nickel", is a query language for finding data in Couchbase Server. N1QL is designed to be human readable and writable. It is a language designed for ad-hoc querying. The query language is a standard semantic used to build querying ability in other databases.
HTSQL was created in 2005 to provide an XPath-like HTTP interface to PostgreSQL for client-side XSLT screens and reports. HTSQL found its audience when analysts and researchers bypassed the user interface and started to use URLs directly. The language has evolved since then.
Tuning your PostgreSQL database is somewhat of a black art. While documentation does exist on the topic, many people still find it hard to get all the power out of their system. This article aims to help demystify PostgreSQL database performance tuning.
SELECT * FROM pg_stat_activity WHERE state = 'active';
So you can identify the PID of the hanging query you want to terminate, run this:
SELECT pg_cancel_backend(PID);
The Query Representation and Understanding (QRU) data set contains a set of similar queries that can be used in web research such as query transformation and relevance ranking. QRU contains similar queries that are related to existing benchmark data sets, such as TREC query sets. The QRU data set was created by extracting 100 TREC queries, training a query-generation model and a commercial search engine, generating similar queries from TREC queries with the model, and removal of mistakenly generated queries.
R. Jones, R. Kumar, B. Pang, and A. Tomkins. CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, page 909--914. New York, NY, USA, ACM, (2007)
P. Haase, J. Broekstra, A. Eberhart, and R. Volz. The Semantic Web - ISWC 2004. Proceedings of the Third
International Semantic Web Conference, volume 3298 of Lecture Notes in Computer Science, Hiroshima, Japan, Springer-Verlag, (2004)
P. Haase, J. Broekstra, A. Eberhart, and R. Volz. Proceedings of the Third International Semantic Web Conference, Hiroshima, Japan, 2004, 3298, page 502-517. Springer Berlin / Heidelberg, (November 2004)
P. Haase, J. Broekstra, A. Eberhart, and R. Volz. International Semantic Web Conference, volume 3298 of Lecture Notes in Computer Science, page 502-517. Springer, (2004)
B. Krause, A. Hotho, and G. Stumme. Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, 4956, page 101-113. Springer, (2008)
B. Krause, A. Hotho, and G. Stumme. Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, 4956, page 101-113. Springer, (2008)
B. Krause, A. Hotho, and G. Stumme. Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, 4956, page 101-113. Springer, (2008)
M. Carey, D. DeWitt, and S. Vandenberg. Proceedings of the 13th Annual ACM Conference on
the Management of Data, page 413--423. Chicago, Illinois, (June 1988)
K. Cheung, H. Frost, M. Marshall, E. Prud'hommeaux, M. Samwald, J. Zhao, and A. Paschke. BMC Bioinformatics, (2009)"We have explored a tool called "FeDeRate", which enables a global SPARQL query to be decomposed into subqueries against the remote databases offering either SPARQL or SQL query interfaces.".
S. Kanmani, K. Vinupriya, M. Yamuna, and R. Deepika. International Journal on Recent and Innovation Trends in Computing and Communication, 3 (3):
1425--1427(March 2015)
S. Sun, S. Prasher, and X. Zhou. Conceptual Modeling for Advanced Application Domains, volume 3289 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2004)