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Embedding¶ To de-normalize data, store two related pieces of data in a single document. Operations within a document are less expensive for the server than operations that involve multiple documents. In general, use embedded data models when: you have “contains” relationships between entities. See Model Embedded One-to-One Relationships Between Documents. you have one-to-many relationships where the “many” objects always appear with or are viewed in the context of their parent documents. See Model Embedded One-to-Many Relationships Between Documents. Embedding provides the following benefits: generally better performance for read operations. the ability to request and retrieve related data in a single database operation.

Referencing¶ To normalize data, store references between two documents to indicate a relationship between the data represented in each document. In general, use normalized data models: when embedding would result in duplication of data but would not provide sufficient read performance advantages to outweigh the implications of the duplication. to represent more complex many-to-many relationships. to model large hierarchical data sets. See Model Tree Structures in MongoDB. Referencing provides more flexibility than embedding; however, to resolve the references, client-side applications must issue follow-up queries. In other words, using references requires more roundtrips to the server.

racle SQL Developer Data Modeler provides a model driven approach for database design and generation, implemented by integrated set of models – Logical, Data types, Dimensional, Relational, Data Flow diagrams and Physical models for supported Oracle Databases, Microsoft SQL Server 2000 and 2005

Cube Views metadata

Dimensional Models   Dedicated full featured Dimensional model – star and snowflake schema easily can be built and expressed on detailed and compact diagrams   Dimensions – support for merging (level can belongs to more than one dimension), shared, fact and role playing dimensions   Hierarchies – value based hierarchies (parent-child), and regular and ragged level based hierarchies   Measures – fully, semi and none additive; different aggregation functions on different dimensions ; fact dimension; calculated measures   Query wizard allows Select statements to be generated from the dimensional model   Support for Oracle OLAP. This includes specifics like cube partitioning, sparse dimensions, and compressed measures   Built-in wizards help to define all required object types and view definitions that enable SQL access to dimensional data in Oracle AW (using the OLAP_TABLE interface)   Bidirectional integration with Oracle physical model - Dimensional model can be created using SQL dimension definitions in physical model or the definitions can be created from the dimensional model

Data models are often used as an aid to communication between the business people defining the requirements for a computer system and the technical people defining the design in response to those requirements. They are used to show the data needed and created by business processes.

A data model explicitly determines the structure of data. Data models are specified in a data modeling notation, which is often graphical in form.[3]

Cameo Data Modeler plugin provides data-related modeling for MagicDraw. It includes entity-relationship, database and XML schema modeling features.

Otherwise, if the number of books per publisher is unbounded, this data model would lead to mutable, growing arrays, as in the following example:

The logical data model includes the following entities: ■ Reference Entities ■ Lookup Entities ■ Base Entities ■ Derived Entities ■ Aggregate Entities

Data in the base entities support the derived and aggregate layers to facilitate Star and population, and act as a source for Data Mining for advanced analysis.

The ADRM Software Healthcare Services Provider models consists of a set of integrated Enterprise, Business Area and Data Warehouse data models developed for organizations in the healthcare service provider industry. The focus of these models is to provide a blueprint of the complex data required to support a variety of applications, analytics and information services for health plan service providers. ·  Service Provider ·  Encounter ·  Claim ·  Patient ·  Service ·  Facility ·  Insurer ·  Health Plan

The challenge is to identify individuals for whom intervention can make a difference and find intervention opportunities at the right time: before adverse health outcomes escalate medical costs.

Maximizing Care Management Savings through Advanced Total Population Targeting

PREDICTIVE MODELING FOR ACCURATE POPULATION TARGETING • Advanced Methodology, Focused Models) • Targeted Interventions) • Testing and Validation)

How to do it... Create your entities first. This example consists of a user, an address, and a SuperSecretData entity: @Entitypublic class User extends Model { @SerializedName("userLogin") public String login; @NoJsonExport public String password; @ManyToOne public Address address; @OneToOne public SuperSecretData secrets; public String toString() { return id + "/" + login; }}@Entitypublic class Address extends Model { public String street; public String city; public String zip;}@Entitypublic class SuperSecretData extends Model { public String secret = "foo";}

Modeling friend of friend relationships in MongoDB

"When a document's stored value for a index key field is an array, MongoDB indexes each element of the array. See the Multikeys page for more information." so to find all of "alex"'s friends I can just do: db.user.find( { 'friends' : '4e77bb3b8a3e000000004f7a'});

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration

There is however Morphia, an Object Document Mapper for MongoDB + Java which closely emulates the JPA model but using MongoDB appropriate semantics

DataNucleus already supports MongoDB, for JPA and JDO, the standardised APIs in Java.

clipseLink is planning support for MongoDB from JPA.

Our Data Analytics program focuses on the strategic and advanced uses of data analytics across a broad range of industries and occupations. Courses cover data mining, visualization, modeling and optimization