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Java Database Connectivity (JDBC) provides the J2EE application a standard interface to databases (usually relational databases) In principle, JDBC serves the same purpose as Open Database Connectivity (ODBC) JDBC provides a database-independent protocol for accessing relational databases from Java JDBC supports Data Manipulation Language (DML) statements such as insert, update, delete, and select It also includes Data Definition Language (DDL) statements such as Create Table, Alter Table, and so on Database vendors usually provide a JDBC driver that enables a Java program to access the vendor s RDBMS product As of this writing, Sun has 154 JDBC drivers listed in its driver database Note See http://industryjavasuncom/products/jdbc/drivers for a listing of available drivers for use with JDBC JDBC was included in core Java starting with version 11 With JDBC, the SQL is always dynamically generated at runtime and sent to the database.

It does not support drawing inferences However, Microsoft Research also offers MSBN (Microsoft Bayesian Networks), a tool for (manually) building Bayesian networks and drawing inferences with them, MSBN comes with a graphical user interface Both programs, WinMine as well as MSBN, are available for Windows only..

8.2.3.4.1 The Agreement on the UTRA Requirements and on Five UTRA Concepts Reached atSMG#22 in June 1997 As planned the elaboration and approval of the requirements for UTRA (high level and detailed) and the selection procedure for the choice of radio transmission technology could be achieved at SMG#22 in June 1997. It was possible to approve the relevant SMG2 input documents 50 in this meeting unanimously. It was also planned to agree on a limited number of concepts in this meeting. SMG2 had studied a wide variety of possible solutions since December 1996 and proposed ve concept groups for UTRA. 51

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Many have griped about the inefficiencies of applying dynamically created SQL against databases In response, another standard for Java database access has emerged and is called SQLJ SQLJ enables static SQL to be used and it requires less cumbersome syntax than JDBC One SQLJ advantage over JDBC is better code quality because SQL is checked at compile-time Also, SQLJ usually shows better performance than JDBC because access paths to the database are generated once and reused for subsequent executions of the static SQL We speak of levels for JDBC drivers; the slowest are level 1 drivers and the quickest are level 4 drivers In addition, some drivers serve as a bridge between JDBC and ODBC, mostly as an easy way to access ODBC databases (MSsomething or other databases, usually) A type 1 driver provides JDBC access using a JDBC-ODBC bridge This bridge provides JDBC access to most ODBC drivers.

When 3GPP was created, four Technical Speci cation Groups (TSGs) were formed to undertake the preparation of technical speci cations. The four TSGs were as follows:

Disadvantages of this type of JDBC driver include additional performance overhead of the ODBC layer, and the requirement to load client code on each client machine A type 2 driver is a partial Java driver that converts JDBC calls into the native client database API As with the type 1 driver, this driver requires some client code to be loaded on each client machine A type 3 driver is a pure Java driver that translates JDBC calls into a database-independent network protocol The database-independent protocol is implemented using a middleware server The middleware server translates the database-independent protocol into the native database server protocol Middleware vendors typically offer a type 3.

In the lter feature selection methods, various statistical measures are used to rank genes by their discriminative powers. Successful measures include using the t-test, the chi-square test, information gain, and the Kruskal Wallis test. A recently proposed biomarker identi cation approach involves clustering gene expression pro les [58]. In such an approach, genes are clustered based on their microarray expression pro les. Then, within each cluster, the most representative gene is selected (the representative gene could be the gene closest to the mean or median expression value within the cluster). The representative genes are collected and used as selected features to build a predictor for classi cation of unknown samples. However, selected sets of genes often lack biological justi cation and their size is usually too large for experimental validation. 4.3.6.2 Domain Knowledge-Based Feature Selection A recently proposed feature selection approach exploits the biological knowledge of gene functions as a criterion for selection [59]. The underlying hypothesis for this approach is that the difference between samples lies in a few key gene functions. Genes annotated with those key functions are likely to be very useful for classi cation. To use this observation, a statistical test is applied to microarray data in order to rank genes by their p-values and generate a subset of signi cant genes. Selected genes are compared to the overall population in order to identify the most signi cant function. Only genes associated with the most signi cant function are selected for classi cation. This approach results in a small set of genes that provide high accuracy (see the case study below).

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