Introduction to Oracle Cloud

Editor's Notes

• #6: Oracle’s platform for life sciences helps Oracle to delivers applications for the three other areas of the Life Sciences beyond Discovery. Development (Oracle Clinical) for managing clinical trials data Manufacturing and Supply Chain Management (Oracle11i Process Manufacturing) for managing process and/or discrete manufacturing processes and Sales and Marketing (Oracle11i CRM) for managing customers, providers, and distribution channels. Additionally, Oracle now offers a new application, the Healthcare Transaction Base (HTB) for a health information platform that provides interoperability, portability and accessibility of health information. All the Oracle applications for life sciences are built on the 9i DB,9iAS Application Server and the Oracle Collaboration Suite.
• #7: As a result of the magnitude of the challenge of life sciences discovery, scientists, investigators and researchers have formed collaborations, such as the Human Genome Project, to produce and share basic scientific data and results. Genetic sequences, gene expression, proteomics data, and clinical data frequently reside in different systems. However, scientists often need to access and integrate this data from a variety of data sources across public and private systems in their research. Enabling researchers to conveniently access up-to-date versions of the data places significant demands on a life sciences organization's IT department. Consequently, any life science IT environment need to address the needs of accessing, managing, sharing and analyzing large quantities of data and to support collaboration yet maintain proper security.
• #13: To analyze the mysteries hidden within life sciences data, scientists must access and integrate vast quantities of data from a variety of data sources. Oracle provides a rich set of functionality that allows life scientists to quickly, reliably, and easily access and integrate data from heterogeneous data sources. Oracle Transparent Gateways - Integrate Data from Disparate Systems A typical life sciences research environment includes multiple databases from different vendors supporting different groups inside and outside an organization. The challenge is to quickly, efficiently, and economically deploy data that may exist on many disparate systems through a single application, providing a comprehensive view of the data, regardless of the data sources. Oracle Transparent Gateways enable scientists to transparently access life sciences data in non-Oracle systems from an Oracle environment. Bioinformatics applications can be developed using a consistent interface for both Oracle and non-Oracle systems. The Oracle Transparent Gateway solution provides the ability to translate SQL dialect, data dictionary, and data types between the systems and has the ability to securely manage the transactions with a non-Oracle system.
• #14: Oracle Transparent Gateways Integrate data from disparate systems Generic Connectivity ODBC/JDBC connectivity External Tables Access data from flat files Distributed Queries Query across multiple Oracle and heterogeneous data sources Transportable tablespaces Rapidly move tablespaces between Oracle databases SQL*Loader High performance data loader Oracle Streams Rule-based subscription for information sharing Dblinks Connectivity between databases UltraSearch Query range of data repositories (web sites, files, email, databases, etc.) Migration Toolkits Tools to facilitate movement of data into Oracle Merge / Upsert Update and insert in one step Oracle Transparent Gateways Oracle9i provides two kinds of gateway solutions for data integration. Oracle Generic Connectivity Oracle Transparent Gateways. Oracle Generic Connectivity is a generic solution for any ODBC or OLEDB compliant non-Oracle system. Generic Connectivity relies on industry standards. Oracle Transparent Gateways are end-to-end certified, tailored solutions, specifically coded for non-Oracle systems and access these systems using their native interface. These powerful solutions provide scientists both location and operation transparency in accessing heterogeneous data sources common in a life sciences research environment. External Tables - Access Data from Flat Files. A lot of bioinformatics data is contained in flat files such as the sequence data from GenBank, proteomic data from SwissProt, and Mass Spectrometry results. The Oracle9i External Tables feature allows you to access and query data in external flat files as if they were in a database. Transportable Tablespaces - Efficiently Move Tablespaces Between Oracle Databases Oracle9i's Transportable Tablespaces feature is a powerful mechanism that moves tablespaces between Oracle databases. It is the most efficient and fastest way to move bulk data between Oracle databases. It allows researchers to share, publish and exchange data quickly and easily. UPSERT - Enable Update or Insert in One Step Life sciences data is constantly being updated with new sequences, expression data, pathway information, and annotations. The Oracle9i UPSERT feature (also known as MERGE statement) supports fast update of records or insertion of new records, all in one step. This feature greatly enhances performance when merging two data sources and always outperforms the procedure logic when you use an "IF/ELSE" statement. Oracle Streams - Enable Rule-based Information Sharing Among Multiple Systems Oracle Streams, introduced in Oracle9i Database Release 2, provides an information sharing solution that allows data sources to publish events, and consumers to subscribe to those events meeting their subscription criteria. For example, the data source can be set up to publish an event whenever a new protein structure is introduced, the consumer subscribing to the event can then update its own system accordingly. SQL*Loader - Load External Data Into Oracle Tables SQL*Loader allows you to load data into Oracle tables from operating system files. It can load data from multiple data files during the same session, as well as having the capability to load data into multiple tables. The data can be loaded from disk, tape, or named pipe. SQL*Loader contains a powerful data parsing engine which puts little limitation on the format of the data in the data file. For example it can load arbitrarily complex object-relational data.
• #16: Provides conditional “insert or update” processing e.g. perform check sum on annotation or DNA sequence used in periodic data loads where new data is merged with existing data and the content of source and/or destination are unknown so INSERT or UPDATE cannot be used exclusively supports fast update of records or insertion of new records in one step the MERGE Statement command is expanded to support loading to multiple tables at once provides a means to skip updating if a certain user specified condition is met provides a way to perform INSERT or UPDATE if the WHEN clause always evaluates to TRUE or FALSE
• #17: Future Capability Move tablespaces between different hardware platforms Solaris HP-UX AIX Tru64 Linux NT DB format is the same for 32 and 64 bit systems
• #19: Data Pump A high speed, parallel infrastructure that enables quick movement of data and metadata from one database to another. This technology is the basis for Oracle’s new data movement utilities – Data Pump Import and Data Pump Export. By using the parallel parameter, the maximum number of threads of active execution servers operating on behalf of the job can be specified, resulting in better performance. In addition, there is now more support for fine-grained object selection. Oracle Data Pump Oracle Data Pump is a new feature of Oracle Database 10g that enables very fast bulk data and metadata movement between Oracle databases. Oracle Data Pump provides new high-speed, parallel Export and Import utilities (expdp and impdp) as well as a web-based Enterprise Manager interface. Data Pump Export and Import utilities are typically much faster than the original Export and Import Utilities. A single thread of Data Pump Export is about twice as fast as original Export, while Data Pump Import is 15-45 times fast than original Import. Data Pump jobs can be restarted without loss of data, whether or not the stoppage was voluntary or involuntary. Data Pump jobs support fine-grained object selection. Virtually any type of object can be included or excluded in a Data Pump job. Data Pump supports the ability to load one instance directly from another (network import) and unload a remote instance (network export).
• #20: Distributed Queries Enables users to perform optimized queries across distributed Oracle and non-Oracle data sources. Cost-based optimizer enhanced to capture complete statistics for remote tables Considers network bandwidth and latency in deciding what parts of the query plan should be remotely mapped
• #21: Oracle Streams - Enable Rule-based Information Sharing Among Multiple Systems Oracle Streams, introduced in Oracle9i Database Release 2, provides an information sharing solution that allows data sources to publish events, and consumers to subscribe to those events meeting their subscription criteria. For example, the data source can be set up to publish an event whenever a new protein structure is introduced, the consumer subscribing to the event can then update its own system accordingly. http://otn.oracle.com/products/dataint/htdocs/streams_fo.html Oracle Streams enables the propagation and management of data, transactions and events in a data stream either within a database, or from one database to another. The stream routes published information to subscribed destinations. The result is a new feature that provides greater functionality and flexibility than traditional solutions for capturing and managing events, and sharing the events with other databases and applications. As users' needs change, they can simply implement a new capability of Oracle Streams, without sacrificing existing capabilities. Capture Implicitly captures both DML as well as DDL changes. Filters captured changes based on user-defined rules. Lets applications explicitly enqueue user messages into staging area. Staging/Propagation Provides secure holding area with auditing and tracking of captured data. Allows data to be routed through staging areas before being applied. Supports transformation of data as it is enqueued into or dequeued from the staging area, or prior to propagation. Consumption Automatically detects and resolves conflicts before applying changes using default apply mechanism. Gives user complete control over how data is modified and applied with user-defined apply mechanism. Allows applications to directly access data in Stream's staging area using explicit dequeue. OLD INFO: Improved performance & management Offload mining of logs to remote site Change capture & apply improvements Support for LONG & NCLOB columns Support for Index-only tables Streams-based Change Data Capture Solution Streams-based file transfer Migration for AQ and Replication customers Integrated with new job scheduler
• #22: Oracle Transparent Gateways Transparently access data in non-Oracle systems from Oracle, by using the native interface of the non-Oracle system. End-to-end certified, tailored solution for many non-Oracle databases. Support for remote (non-Oracle) functions in select statements has been added for the Sybase and SQL Server Gateways.
• #28: 10iR1 XML Improvements To provide better support for standards based access to XML, this release provides SQL/XML Standard compliance. Applications can use standard SQL/XML operators to generate complex XML documents from SQL queries and to store XML documents. The XML Parser is also extended to support the updated and new W3C XML standards. Also in this release, evolution of XML Schemas is supported. There have also been major improvements in XML processing performance. The XML Developer Kit (XDK) libraries and interfaces in Java, C, and C++ all transparently support the database XMLType, increasing throughput and scalability without high resource and processing costs. Additionally, the architecturehas been redesigned using a pipeline process model and SAX to increase performance while reducing resources. --- Already have best support for XML today Applications can use standard SQL/XML operators to generate complex XML documents from SQL queries and to store XML documents The XML Parser is also extended to support the updated and new W3C XML standards Support for evolution of XML schemas Major improvements in XML processing performance XML Developer Kit (XDK) libraries and interfaces in Java, C, and C++ all transparently support the database XMLType, increasing throughput and scalability without high resource and processing costs.  Additionally, the architectures been redesigned using a pipeline process model and SAX to increase performance while reducing resources.
• #31: Oracle Text For better handling of documents in a global environment, automatic discovery of the language and character set of unknown documents is provided. To make it easier to develop text-enabled applications, a series of JDeveloper Wizards enable the generation of catalogs and text driven applications. In addition, HTML based Thesaurus Manager is also provided. Manageability of text documents in the database has also been improved – locally partitioned text indexes can be created online, and documents can be inserted during index creation and rebuild. Document services, such as highlighting, themes and gists no longer require the building of a text index, and a text framework allows user defined components to be plugged in while invoking these document services. The quality of retrieval of text searches has also been improved. Query log analysis is supported, allowing searches to be customized based on the success of past user queries. The ability to classify and cluster documents has been enhanced by taking advantage of the Support Vector Machines and Nonnegative Matrix Factorization algorithms in Oracle Data Mining.
• #34: Oracle interMedia Support for images in the database has been enhanced with functionality that includes rotating and flipping an image, adjusting contrast and bit depth, and brightening and darkening an image using gamma processing. Standards based access to multimedia documents is enhanced with support via the SQL Multimedia Standard (ISO/IEC 13249-5) and Java Advanced Imaging 1.1.1 capabilities. New audio and video media formats are supported, including MPEG4, MPEG2 and WMF, along with the associated automatic recognition and extraction of all metadata supported by these formats.
• #35: Oracle Spatial Network Data Model Enables data to be managed and analyzed as if it were stored in a graph representation, i.e. nodes and links. Available analytics include shortest path, all paths, within cost, nearest neighbors, and minimum cost spanning tree. It is possible to add constraints to all of the analytics.
• #37: User-Defined Datatypes and Extensible Indexing User-defined datatypes make it easier to work with complex data such as chemical, proteomics, genomics, spatial, and image data. Life Science ISVs such as MDL, Daylight and Tripos take advantage of extensible indexing. Oracle’s Extensibility Framework allows users to create: User defined types User defined operators User defined index User defined optimizer
• #38: Using Oracle’s extensibility framework, customers and ISVs can build special user interfaces that allow the chemist to search for chemicals that “look” similar. Internally, Oracle’s extensibility indexing framework searches the Oracle database and finds what the chemists is looking for in a fast, performant, scalable database platform without forcing the chemist to learn SQL.
• #40: Grid support in Oracle 10g Oracle Scales to Petabytes Largest life sciences databases run Oracle Oracle 80% market share - IDC Partitioning Divide and conquer Oracle 10g Application Server Provide scalability for middle tier Oracle Data Guard Protect data from human or system failures --- Life sciences data is growing rapidly, and often scales to tens and hundreds of TBs of data. In many life sciences applications, such as high volume genomic sequencing operations, uptime availability and the ability to scale as data volume grows are very important. Oracle9i database scales to PetaBytes of data and offers unbreakable reliability. It also provides you the flexibility to add servers as your data grows. Oracle9i Real Application Clusters Oracle9i RAC provides scalability, high availability, and full application compatibility to meet the increasing needs of life sciences customers. Oracle9i RAC provides: Scalability and flexibility: users can add nodes for processing power and storage capacities as needed, High availability: end users enjoy uninterrupted server access regardless of hardware and/or software failures, Ease of management: a single system image is preserved across the clusters. DBA manages one virtual server and Full application compatibility: you don't need to make any modification to your applications. Partitioning Partitioning addresses key issues in supporting very large tables and indexes by letting you decompose them into smaller and more manageable pieces called partitions. Oracle9i Application Server All of Oracle's core middle-tier services have been integrated into one product, Oracle9i Application Server (Oracle9iAS), enabling customers to build and deploy portals, transactional applications, and business intelligence applications with a single product. Oracle 9iAS Web Cache stores frequently accessed pages in memory and allows the database queries to be processed faster. Also, by reducing the load on the backend database, this service allow the database to eventually support even more users. Additionally, Oracle9iAS makes intensive use of connection pooling and load balancing mechanism. Such mechanisms enable applications to run with fewer connections to the database than the number of actual users, thus increasing the total number of users that can be supported by the database. Oracle Data Guard Oracle Data Guard protects data from user errors, disasters, storage failures, and planned outages. It provides out-of-the box rapid deployment and management interface for a standby database, a real time copy of your production system. When disasters occur, you can switch over instantly to a standby database with no loss of data. Oracle Data Guard does more than offer protection. It also automates the tasks involved in monitoring and controlling a standby system, allowing the standby system to be used for maintenance, reporting, and removing processing load from the production system. In Oracle9i Database Release 2, Oracle introduces Logical Standby Database, where Data Guard applies SQL statements instead of redo logs to the standby system. Your production and logical standby system can be physically different. This allows you to optimize your standby system for reporting purpose and enables you to do production work at the standby system. Support for Grid Concepts Oracle has a range of features that provide support for grid concepts including support for the Globus Toolkit.
• #41: If you are a small biotech or a large Pharma, you are faced with a very familiar concern … how large is my data management requirement going to grow … and how can I ensure that what I have now can scale to meet tomorrow’s needs. Oracle9i RAC answers those concerns and even more. You could start with just one server and then have the number of servers grow as your business and data grows. This reduces cost of ownership and also saves you from buying a very large server trying to anticipate your requirements in the future. You could grow these servers into a cluster of servers offering you the kind of scalability and reliability you have been looking for. It has only one database to administer thereby reducing your data administration cost of maintaining several small databases. Also you do not have to replicate your systems to avoid contention of usage between your internal scientists and your customers.
• #42: Automated job scheduling across Grid Already has been lots of support for Grid concepts provided within Oracle environment Distributed queries External tables Security RAC Participate in Global Grid Forum Incremental Grid support
• #51: Oracle Business Intelligence Helps Caprion Pharmaceuticals Store and Analyze Multiple Terabytes of Data for Drug Discovery "The Oracle Data Warehouse is a key component of our IT platform for proteomic analysis. The massive amount of information we produce every day requires a system with proven performance to effectively capture our biological data." --Bernard Gagnon, IT Director, Caprion Pharmaceuticals Inc. Key Benefits • Easy access and management of integrated information • Rapid deployment of new Ad/Hoc Query • Scalability necessary to accommodate growth Oracle Products & Services • Oracle9iAS - Enterprise Edition • Oracle9i Developer Suite • Oracle Database Enterprise Edition • Oracle Consulting Services • Oracle9iAS Discoverer Corporate Profile Caprion Pharmaceuticals, Inc. Montreal, Canada www.caprion.com/ Caprion Pharmaceuticals Inc. is privately held biotechnology company applying proteomics - the study of the nature and function of proteins - to accelerate the diagnosis and treatment of disease. Caprion provides CellCarta¿ proteomics research for major biopharmaceutical companies to increase the speed and efficiency of early stage drug discovery and to develop its own diagnostic and therapeutic products. Caprion's current product development programs include the first blood screening reagents for Mad Cow Disease and its human equivalent. Industry Life Sciences Employees Fewer than 500 employees Oracle Implementation To Help Accelerate Methods To Diagnose and Treat Diseases Caprion Pharmaceuticals uses proteomics - the study of the nature and function of proteins - to discover and develop innovative products for the diagnosis and treatment of disease. Using its integrated technology platform and unique expertise in cell fractionation, Caprion uncovers the protemic changes involved in disease, and delivers novel targets to develop effective new treatments for cancer and other conditions. Caprion obtains critical information on proteins and peptides daily and from different sources, which needs to be loaded into a database. At the same time, users need to conduct database queries. The company was looking for a solution enabling both functions simultaneously without impacting query performance. Scalability was also an issue since Caprion continues acquiring data and stores this information indefinitely. The company eventually requires the capacity to process and store multiple terabytes of information per year. Oracle helped Caprion build a complete and integrated solution that meets their needs and requirements today and in the future. High Level Project Goals Scalability for a multi-terabyte system Integration of all components with the existing computing environment Security and protection of data integrity Why Oracle? "With the help of Oracle's technology and consulting services, we were able to develop and implement a one-of-a-kind system that addressed our unique demands of proteomic data analysis for drug discovery. We can now offer a complete solution to accelerate the identification of disease-related targets for pharmaceutical partners," says Lloyd M. Segal, president and chief executive officer at Caprion Pharmaceuticals. Building the Solution Caprion implemented Oracle Warehouse Builder to store, consolidate and analyze vast amounts of information efficiently and effectively. Oracle Warehouse Builder enables Caprion to quickly and easily update the structure of its data warehouse and integrate data from diverse data sources and legacy systems. This datawarehouse has been designed to manage multiple terabytes of information captured. Caprion¿s data warehouse is used by 60 scientists daily and allows the capture, analyzing and querying a vast amount of information on proteins and peptides. "The ROI of our data warehouse can be reduced to the simplest expression: without it our business model is not possible", said Bernard Gagnon, IT Director, Caprion Pharmaceuticals Inc. "The enormous data volume required to describe the human proteome (tens of terabytes, possible hundreds of terabytes) makes a data warehouse essential to be able to make sense of it. Without a doubt, manual analysis would be impractical. Storage of the data in any other media would make the analyses too time consuming." Caprion also utilized the Oracle database and Oracle9i Application Server (Oracle9iAS) to protect data integrity, help ensure high availability and limit downtime. In addition to providing the proven integrated security applications, Oracle provided Caprion with a single, fully integrated management solution. Oracle enabled Caprion to store and analyze the vast amounts of protein information within normal and diseased tissues. The ability to view these results was made possible with Caprion's proprietary web-based interface, created using Oracle9iAS. Oracle9i JDeveloper provided Caprion with a Java and XML integrated development environment for end-to-end, J2EE standards compliance. Future Vision Realized Today As Caprion Pharmacuticals discovers more about proteome, their data warehouse is expected to grow to tens of terabytes. Oracle continues to provide the software platform and solutions Caprion needs to further its research in the critically important areas of genomics, proteomics and drug development
• #54: Oracle’s Collaboration Suite provides a complete environment for communicating, sharing files and data, and security. Oracle Collaboration Suite - Integrated communications - Single enterprise search across all repositories Internal & external - Flexible access Web, desktop Wireless and telephone
• #57: On the side of the pyramid it also highlighted that it is necessary for Oracle to offer very robust security. As the primary asset that a pharma company has is its data, then it’s imperative that this data is kept secure and that IP can be protected. In addition, having strong security capabilities makes it easier to meet regulatory requirements. Oracle provides strong security features that ensure the safety of your intellectual property. Oracle sets a new standard in database security with the introduction of Virtual Private Database (VPD). The VPD enables, within a single database, per-user or per customer data access with the assurance of physical data separation. It ensures that, no matter how a user gets to the data, the same strong access control policy is enforced. Oracle Label Security extends VPD to enforce label-based access control in Oracle9i Database. Label-based access control provided by Oracle Label Security allows organizations to assign sensitivity labels to information, control access based on those labels, and ensure that data is marked with the appropriate sensitivity label. For example, you might mark you data as “Company Confidential” or “Public.” Further, there may be some information that can be shared with partners. And some that is only accessibly by certain groups within the company. The ability to natively manage labeled data is a tremendous advantage in being able to provide the right information to the right people at the right level of secure data access, as FDA 21 CFR Part 11 requires that “ System access limited to authorized individuals. [11.10(d)]” Oracle9i Database and 9i Application Server also offers many other security features such as support for selective data encryption, SSL for data transport, Public Key Infrastructure for authentication, as well as single sign-on and Oracle Internet Directory, or other LDAP-compliant directory. A critical aspect in life sciences IT system is maintaining a record of system activity to ensure that users are held accountable for their actions. The FDA requires companies submitting new drug applications to have a “secure, computer-generated, time-based audit trails to independently record the date and time of entries and actions that create, modify, or delete electronic records. [11.10(e)]” Oracle’s Auditing feature generates audit trails. With Auditing, organizations can define audit policies, which specify the data access conditions, such as querying the target tables, misuse of legitimate access, or database intrusion, that trigger the audit event. If the audit condition is met, an audit event entry, including username, SQL text, bind variable, policy name, session id, time stamp, and other attributes, is inserted into the audit trail. Auditing also allows companies to use a flexible event handler to notify administrators that the triggering event has occurred or respond with other appropriate actions. Oracle allows companies to audit both successful statements and unsuccessful attempts, as FDA requires that “use of safeguards to prevent unauthorized use of passwords and identification codes, and to immediately report to the system security unity any attempts at their unauthorized use [11.300(d)]” specified in 21 CFR Part 11.
• #58: Level Compartment: category, non-hierarchical Group: hierarchical: ownership trip
• #59: Text There are essentially five areas of focus (listed below the bottom of the slide) when it comes to the security & privacy of medical information: Authentication – When a user gains access to an application or system and is comprised of basically three levels: What you know – A password What you have - i.e. Secure ID card…The best example of this is an ATM card. Using your ATM card requires you to know something (a password) and have something (the ATM card itself). Who you are - This requires identifying you based on part of your body, for example, a fingerprint,a scan of part of your eye (retina, iris), or the geometric dimensions or your hand. Privacy & Integrity of Communications – Refer to this as data in motion. When information is in transit between a web server, a web browser or a database server - data is often easily viewable to anyone who can connect, or “sniff” the network. Any information, whether it be email text or lab test information from a clinical system, can easily be seen. This is analogous to being able to look through a car window on the highway and see who is driving. The protection of information in motion requires for data to be encrypted (or scrambled) while in route and protected with a cryptographic checksum. A cryptographic checksum ensures that data cannot be modified while in route. For example, a salary transaction that increases a persons salary to $20,000 could be intercepted and changed to $200,000 and sent back on its path across the network… Access Control – The ability to provide access only to the information needed by an individual to do their job. Sometimes this is implemented in the code of a particular application. While this is effective when using the application, the security does not apply if a user were to access the data another method, or outside the context of the application. (When presenting point out that the nurse (designated by the color blue and only see corresponding blue data shot, lab test, therapy, etc)). *Note: The notes for the two Advance Security slides are split on both slides for ease of printing purposes…A hint to change to the second security slide is included in the next slide.
• #60: Oracle leads the industry in security. Additional information on Oracle’s “Unbreakable” security if available http://otn.oracle.com.
• #67: With Oracle’s comprehensive collection of both deductive and inductive analyses embedded in the database, researchers and scientists can query the database to search for answers to specific questions or they can use advanced data mining functionality to search for complex and subtle relationships, associations, clusters and patterns hidden in the data.
• #71: Enable Regexp support in database through SQL and PL/SQL Provide SQL and PL/SQL functions for Regexp matching and string manipulations Follow POSIX style Regexp syntax Support standard Regexp operators including *, +, ?, |, ^, $, ., [ ], {m, n}, etc. Include common extensions such as case-insensitive matching, sub-expression back-references, etc. Compatible with popular Regexp implementations like GNU, Perl, Awk
• #74: Highlight new stats?
• #84: Oracle Text For better handling of documents in a global environment, automatic discovery of the language and character set of unknown documents is provided. To make it easier to develop text-enabled applications, a series of JDeveloper Wizards enable the generation of catalogs and text driven applications. In addition, HTML based Thesaurus Manager is also provided. Manageability of text documents in the database has also been improved – locally partitioned text indexes can be created online, and documents can be inserted during index creation and rebuild. Document services, such as highlighting, themes and gists no longer require the building of a text index, and a text framework allows user defined components to be plugged in while invoking these document services. The quality of retrieval of text searches has also been improved. Query log analysis is supported, allowing searches to be customized based on the success of past user queries. The ability to classify and cluster documents has been enhanced by taking advantage of the Support Vector Machines and Nonnegative Matrix Factorization algorithms in Oracle Data Mining.
• #105: However, customers started to request that we enhance the NDM feature to support an RDF Data Model. This is because they wanted to be able to more easily integrate the many different biological graphs. For example, biological pathway and interaction data is in KEGG, IntAct, DIP, MINT, and Ecocyc. As these data sources become more prevalent, and increase in size and complexity, it becomes more important that these data sources can be managed as a graph representation in a relational database management system (RDBMS). A RDBMS offers users the ability to store data in a secure, highly available and scalable environment. To date, Oracle is the only relational database vendor to incorporate graph functionality in the database. It also become important that these data sources can be integrated with in-house data. RDF is the main syntactic standard for the Semantic Web. RDF is a format for making statements about facts, statements about associations, and statements of statements. The RDF model is based on the use of triples (subject-predicate-object). Databases actually also use triples, but the components of the triple are a row, a column and the value in the cell. RDF triples are very simplistic and flexible and so they can represent data in a table structure, in a tree structure, or in any structure. The RDF triple can be used to merge data very effectively, as you can see in the slide, the image on the left shows how a graph can be used to represent data about an individual, for example, their e-mail, their name, and a meeting that they are attending. When the original graph is merged with a second graph in red, you can see that the person is chairing a session at the meeting, and where exactly the meeting is being held. So using RDF, information can also be assembled into useful blocks of knowledge. Any information expressed in RDF can be connected to any other information expressed in RDF, in much the same manner that any document expressed in HTML can link to any other document in HTML. It is possible to merge graphs because their types are explicitly defined using a URI, which is a global identifier. Having a URI is good, as it allows people to know exactly what it is that you are talking about. For example, instead of saying yellow, specify the exact shade of yellow that you mean with a unique identifier. Biologists are interested in the semantic web because so much of the data that they already work with is in the form of a graph, for example protein-protein interactions, metabolic pathways, signal transduction pathways, regulatory pathways, gene expression networks. So if their data is already in the form of a graph, it is so much easier if they can then just store and manage it as a graph. RDF has the added benefit of enabling researchers to easily merge the biological graphs. Also biologists are used to the idea of unique identifiers and have been working on LSID for some time now. Explicit semantics are of value to people in LS, as different people define the same concept using differing terms. For example, different people use gene to represent different concepts, for example mRNA or introns plus exons, or the chromosome loci for mapping.
• #108: This is a screen snapshot of BioDASH, which is a demo that I was involved in building with a number of people in the life sciences community. You can download the software to run BioDASH from the W3C’s Web site. The demo was built to show how it becomes easier to integrate and aggregate data using RDF. On the left hand panel, you can see the red GSK beta protein, and some of the chemical compounds that it interacts with. If you click on any of these entities you get to see more information. On the right, you can see the wnt pathway.
• #109: We integrated these data sources with Siderean using RDF in less than a week.
• #110: We built plugin for RDF Data Model, so you can now easily save an OWL ontology in RDF.
• #112: SQL/RDBMS, Xquery/XML and SPARQL/RDF offer three different ways to query and manage information. Why do we need three different methods?  They are designed to serve different, complementary purposes.  By using each of these in different situations, a user can optimize the quality and efficiency of information querying and management. A relational database and SQL are best where concise, efficient transactions are needed. Typically, this occurs within an enterprise application such as an ERP, CRM or SCM application. In these applications the user is interacting with the data through a tightly constrained set of forms provided by the application.  Given the tightly controlled environment, the application (and the underlying RDBMS) need a minimal amount of input (e.g. a string, a number, a date) to execute properly. This is because all the metadata about the transaction is embedded or implicit in the application or database schema itself. The benefits of SQL/RDBMS are the low overhead required to execute a transaction and, therefore, the performance and scalability with a known level of quality of service that can be achieved. However, when executing a transaction across organizational boundaries, the environment is much less tightly controlled. A supplier or customer may use a different application and a different database schema for the same type of transaction.  In addition, systems may constantly vary in a large population of organizations that are sharing information. In that case, SQL is at least very difficult to use if not utterly inadequate. For this environment, Xquery/XML combined with Web services is more appropriate.  XML documents can be used to execute transactions (e.g. purchase order) just as with SQL except that XML wraps the metadata about the transaction around the data itself.  When an XML document is sent from one organization to another, an agreed upon schema can be used to decode the metadata about the transaction.  This is feasible when you have a well-structured federation of organizations as, for example, in a supply chain.  Many industries are adopting standard XML schemas for their industry to define business documents such as purchase order, resume, prescription, etc.  These can be used with standardized industry Web services to build sophisticated inter-business processes. Xquery/XML is not as efficient as SQL/RDBMS but offers much richer transactions and more flexibility for information sharing across applications. But even Xquery/XML requires some agreement among parties as to the format of documents. Users must know ahead of time how, approximately, the information will be used. In many cases, it is impossible to know who will be looking for information and how they may choose to use it.  A new technology, SPARQL/RDF, is designed for information sharing with ultimate flexibility.  By encoding the relationships between data, RDF enables semantics as well as syntax to be embedded in documents.  Users can apply arbitrary ontologies to the data and semantics to discover information that may not have even been anticipated by the original data provider.  Users with little or no technical knowledge of where the data is located or how it is structured can also formulate queries. This can be particularly powerful for applications on enterprise grids. The disadvantages of SPARQL/RDF it is difficult to guarantee the completeness and accuracy of query results.  Also, these queries cannot be performed as efficiently or with the scalability of Xquery, let alone a SQL.  Each of the different information management models has distinct strengths. An important question is how all three can be used together on a single set of data to meet the needs of all users while ensuring a single consistent source of truth.  Only Oracle Database offers the ability to use all three information management models on a single set of data within a database. Oracle supports relational, XML and RDF schemas within a single database. Each can be used on the same data in different ways. [email_address]