Kamani Hill and Metadata discoveryFor other uses, see Kamani (disambiguation).Kamani Helekunihi Hill (born December 28, 1985 in Berkeley, California) is an American professional soccer player who currently plays for Colorado Rapids in Major League Soccer.Contents 1 Early years 2 Professional career 3 International career 4 References 5 External linksEarly yearsHill was born in Berkeley, California to a Trinidadian father and an American mother of Hawaiian origin. He grew up playing in various Bay Area youth leagues and attended Berkeley High School, where he was a star forward on their soccer team. He then attended UCLA, where he played in 40 games (32 starts), scored nine goals, and assisted on 13 more in his two seasons with the team. During his college years he also played with Orange County Blue Star and San Fernando Valley Quakes in the USL Premier Development League. Professional careerAt the beginning of October 2006, Hill went to Germany for a trial with Bundesliga team VfL Wolfsburg, and in November he signed a two-and-a-half year contract with the team. He made his first-team debut on January 27, 2007 as a substitute in a 2–1 defeat away at Hertha Berlin. However, after a promising start, the arrival of coach Felix Magath at the club saw Hill relegated to the second team, where he spent most of his time at Wolfsburg. During November 2008, he trained with Norwegian club FK Bodø/Glimt. However, he was not offered a contract. In April 2009, he began a ten-day trial with Vitoria Guimaraes upon the recommendation of his Wolfsburg teammate Alex, and on May 8 the club announced his signing to a three-year contract.Hill was released by Vitória Guimarães in July 2010 and returned to the United States to train with Major League Soccer club San Jose Earthquakes.Hill signed with Major League Soccer club Colorado Rapids on March 28, 2012. International careerHill played for the United States U-20 men's national soccer team in the Suwon Youth Tournament in South Korea in 2005, where he scored a game-winning goal against Argentina.He later made his debut for the senior United States men's national soccer team on June 2, 2007 as a substitute in a 4–1 friendly match victory over China in San Jose, California.
Metadata discovery and Kamani HillIn metadata, metadata discovery is the process of using automated tools to discover the semantics of a data element in data sets. This process usually ends with a set of mappings between the data source elements and a centralized metadata registry. Metadata discovery is also known as metadata scanning.Contents 1 Data source formats for metadata discovery 2 A taxonomy of metadata matching algorithms 2.1 Lexical Matching 2.2 Semantic Matching 2.3 Statistical Matching 3 Vendors 4 Research 5 See also 6 ReferencesData source formats for metadata discoveryData sets may be in a variety of different forms including: Relational databases Spreadsheets XML files Web services Software source code such as Fortran, Jovial, COBOL, Assembler, RPG, PL/1, EasyTrieve, Java, C# or C++ classes, and thousands of other software languages Unstructured text documents such as Microsoft Word or PDF files A taxonomy of metadata matching algorithmsThere are distinct categories of automated metadata discovery: Lexical Matching Exact match - where data element linkages are made based on the exact name of a column in a database, the name of an XML element or a label on a screen. For example if a database column has the name "PersonBirthDate" and a data element in a metadata registry also has the name "PersonBirthDate", automated tools can infer that the column of a database has the same semantics (meaning) as the data element in the metadata registry. Synonym match - where the discovery tool is not just given a single name but a set of synonym. Pattern match - in this case the tools is given a set of lexical patterns that it can match. For example the tools may search for "*gender*" or "*sex*" Semantic MatchingSemantic matching attempts to use semantics to associate target data with registered data elements. Semantic Similarity - In this algorithm that relies on a database of word conceptual nearness is used. For example the WordNet system can rank how close words are conceptually to each other. For example the terms "Person", "Individual" and "Human" may be highly similar concepts. Statistical MatchingStatistical matching uses statistics about data sources data itself to derive similarities with registered data elements. Distinct Value Analysis - By analyzing all the distinct values in a column the similarity to a registered data element may be made. For example if a column only has two distinct values of 'male' and 'female' this could be mapped to 'PersonGenderCode'. Data distribution analysis - By analyzing the distribution of values within a single column and comparing this distribution with known data elements a semantic linkage could be inferred. VendorsThe following vendors (listed in alphabetical order) provide metadata discovery and metadata mapping software and solutions Esquire Innovations (see ) IBM InfoLibrarian Corporation (see ) Masai Technologies (see ) MindHARBOR Metadata Database application (see ) Revelytix (see ) Sliver Creek Systems (see ) Sypherlink: Harvester (see ) Unicorn Systems (see ) Research INDUS project at the Iowa State University (see ) Mercury - A Distributed Metadata Management and Data Discovery System developed at the Oak Ridge National Laboratory DAAC (see ) See also metadata data mapping data warehouse semantic web Defense Discovery Metadata Specification
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