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2	Overview Market & Technology Trends Spatial Database Technology GeoSpatial DBMS in GeoSciences Life Sciences Data Management Challenges BioSpatial DBMS in Life Sciences
3	Spatial data becoming ubiquitous Location Aware and Enabled Infrastructure Defense, Logistics, Mobile devices Internet Portals: MapQuest, Yahoo, MapPoint.NET Automobiles: by 2006, 80% of new cars will have some telematics navigation access (eyeforauto 2001) Structure Databases: Proteomics, Materials Science
4	Spatial Analysis Revealing patterns, relationships & trends
5	Overcoming Application “Stovepipes” Specialty GIS servers Data isolation High systems admin and management costs Scalability problems High training costs Complex support problems Information not aligned with Business Processes Applications can’t leverage brute force of large servers
6	Life Sciences: Drug Discovery The Process
7	Many Different Kinds Data
8	IT Challenges
9	Oracle Platform
10	Integrated NYC Spatial Architecture
11	Managing All the Data in an e-Enterprise
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13	Spatial Database Technology: Manage Location & Structure Data
14	"SELECT STREET_NAME FROM ROADS," SELECT STREET_NAME FROM ROADS, COUNTIES WHERE SDO_RELATE(road_geom, county_geom, ‘MASK=ANYINTERACT QUERYTYPE=WINDOW’) =‘TRUE’ AND COUNTYNAME=‘PASSAIC’;
15	Vector Map Data in Oracle Tables
16	Sub-surface Geological Analysis
17	Raster/Vector Mapping
18	How Spatial Data Is Stored
19	Performing Location Query in Oracle9i
20	Jphone J-Navi Launch May 2000
21	Extensible Database Framework
22	Dealing with large data volumes How large is large ? 100’s of thousands is normal Millions is interesting 10’s of millions is serious 100’s of millions is large What is the problem with large volumes ? They mean big structures Cumbersome to manage Long operations Data reload, refresh Index rebuilds
23	Partitioning: Divide and Conquer For manageability Break large problems into manageable pieces Can load / rebuild individual partitions Can load / rebuild multiple partitions concurrently For performance Query parallelism Partition elimination
24	Oracle9i Spatial Features Spatial Reference System Spatial Operators Versioning/Long Transactions Linear Referencing Quadtree/R-tree index Parallel Index create Geodetic Support Spatial Aggregates Topology * Raster/Grid Management * Spatial Data Mining * * Planned Release 10i
25	Life Sciences Data Management Trends
26	Data Storage Today “To meet the scientific goals we believe we need to add around 80 - 100TB of storage each year for the next 5 years” P. Butcher, The Sanger Centre
27	Increasing Computational Load
28	What does DBMS technology bring? Access and storage of vast quantities of life science data from a variety of sources High throughput loading, indexing, processing and update of information Data integration from a variety of sources Scalability and reliability problems Find patterns & insights through queries, analyses and data mining Collaboration & security challenges
29	1. Vast quantities of data, types & sources
30	2. High Throughput Processing
31	3. Scalability & Reliability
32	4. Hidden Patterns & Relationships
33	5. Collaboration & Security
34	Some Additional Proteomics Challenges: High-throughput crystallography generating large volumes of complex protein structure data Small molecule (structure) databases growing to tens of millions of compounds 3D and pharmacophore analysis require efficient storage, indices and operators of structure data Integrated visualization & computation tools with DBMS
35	How do spatial databases help? Object-relational model and extensibility enable 2D data types and indices Powerful and growing operator set for sophisticated location/structure queries Validation by Geographic Information Systems (GIS) and CAD Community Common query language – SQL- that all data banks and tool vendors leverage Security, reliability, scalability and flexibility Faster, bigger, better, cheaper
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37	Structural Bioinformatics and Rational Drug Design
38	Virtual High-throughput Screening Ligand-Protein Docking Simulation
39	Planned Oracle BioSpatial Types and Functions
40	Managing Protein Structures in DBMS Extend Oracle DBMS with custom 3D structure features Provide BioSpatial types and an object-relational schema for large & small molecule data in Oracle Compliant with mmCIF; SQL interface Provide a low-level interfaces consistent with OMG standard (RCSB) Integration with leading visualization and analytical tools (commercial, shareware)
41	Rich BioSpatial Operators Support the SQL query and computation requirements from needed by biotechs and pharmas and independent software vendors Implement indices and operators in the server to meet requirements Begin with simple operators and those that serve as foundations for extension Integration with 3^rd party visualization tools
42	Foundation Operators Sample BioSpatial Operators: Nearest atom(s) to a specified position or residue in a structure Embedded atomic position index Retrieve polypeptide skeleton list On-the-fly bond and bond-order computation
43	Advanced Operators Protein active site identification Protein surface representation van der Waals; solvation. Surface classification, abstraction Charges; hydrophobicity; H-bond donors/acceptors Extraction of pharmacophore keys
44	Integrate with Existing Tools Current visualization tools based on PDB format parsers Integrate with popular public domain tools and make available Deposition tools Support transition with PDB-to-CIF conversion tool Protein 3^rd party docking and homology applications
45	Oracle Life Sciences Product Directions Better support for life sciences data types Improved support for life science specific analytics Improved support for data import and incremental update Enhanced XML (XDB) & Java support in the Database and Application Server (IAS) Enhanced support for distributed data Partner with ISVs and researchers to deliver “solution” Customer Advisory Board participation
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