Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Database Design Peter McCartney (CAP) RDIFS Training Workshop Sevilleta LTER October 28-30, 2002

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Two Perspectives on Design Field researcher –Goal is to facilitate entry and analysis of what they observed –Tend emphasize information that discriminates between cases –Data collection dictates design Data analyst –goal is to retrieve what they are looking for –Tend to emphasize information that aggregates cases –Data use dictates design

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Two Interfaces Field forms –Organized by sampling events –Hierarchical Analysis and reporting –Matrix (“flat file”) structure –Aggregated or transformed values ( sums, z-scores, etc) –Encoded or decoded

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University The Data Cycle RDBMS Storage ILSDFWEWFDSF FWEFSDFWE DIFJDISLDIJFS SDFSDFSDFSDIMLMKM FSMDSID MLSDIFMSLDFI SDFSDFSL LSIDFSLDFM JIJIJIJLSDIFS Sdfsdfsdfs dfsdfs 8989 sdfsdfsdsdfsd 90 sdfsdsd 4004 dfsdfsdfsdfsdfs dfsd Query Analysis QA/QC Data Entry DenormalizedNormalized Site, Type,Sample, date,taxon 1A, urb, 1,2/2/99, 145 1A, urb, 1, 2/2/99, 123 1A, ag, 2, 2/3/99, 145 …..1..A

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Normalization Information does not repeat Every row can be uniquely referenced Attributes are independent Attributes convey only one piece of information Each piece of information is expressed in only one attribute

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Denormalization You can take a good thing too far Large numbers of tables with small amount of data will degrade performance and be difficult to read Denormalize to avoid excessive links

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Data Modeling Concentrate on logical design – independent of vendor Entity-Relationship (ER) diagram –Relational databases –Tables=entities, columns=attributes, keys=relationships, code=procedures

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Entities - Tables Two dimensional matrix – fields and observations Should contain discrete attributes of information that repeat together as a group – use protocol as guide The majority of ecological datasets will have at least three entities –Site description (occurs once for each site) –Sample description (occurs each time a site is sampled) –Observations (occurs once for each observation made during a sample event)

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Attributes Data types –storage capacity –Precision –Performance Avoid dependencies –Do not make the meaning of one attribute conditional on another Avoid nesting –Do not restrict the domain of one attribute based on the value of another

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Attribute properties Indexes –Primary Key –Alternate Key –Foreign key –Inversion key Null/not null Default value Assignment –Identity –Production rules Check Constraints

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Attribute Domains Measurement scale Units Storage types Domains –Ranges –Enumerations –Rules

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Relationships (join conditions) Cardinality –Parent occurrences –Child occurrences properties –Propagating Enforcement –Keys –Triggers

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Some design considerations Use related tables to define domains if list is long and may grow, constraints if list is short Use alternate keys to enforce complicated nested identifiers, hidden primary keys for linking

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Spatial data Most ecological data have some spatial context –Some are just poorly referenced –Some gain spatial context through relationships GIS systems –Powerful for storing and analyzing spatial geometry –Not so good for managing data

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Spatial Data Entities Vector GIS –Attribute table with all the properties of Tables –Plus some association with a geometric representation –Geometry may be simple or complex Raster GIS –Cell attribute with similar properties View, Stored Procedures –Return tabular entities

Central Arizona Phoenix LTER Center for Environmental Studies Arizona State University Strategies for integrating spatial data Use a GIS rather than a database –Advisable only if attribute data are minimal and flat Hybrid GIS/relational database –Store geometries (points, lines, polygons) in a GIS. –Use a key field to relate geometries to tables in your database Use a spatial data server –Stores gis geometries in binary fields in your database –Uses special software to access spatial and non-spatial data in the same connection