Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.

Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.

Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.

Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.

Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.

Abstract: Processing of source documents to generate data for indexing, and of queries to generate data for searching, is done in accordance with retrieved tokenization rules and, if desired, retrieved normalization rules. Tokenization rules are used to define exactly what characters (letters, numbers, punctuation characters, etc.) and exactly what patterns of those characters (one or more contiguous characters, every individual character, etc.) comprise indexable and searchable units of data. Normalization rules are used to (potentially) modify the tokens created by the tokenizer in indexing and/or searching operations. Normalization accounts for things such as case-insensitive searching and language-specific nuances in which document authors can use accepted variations in the spelling of words.

Abstract: Processing of source documents to generate data for indexing, and of queries to generate data for searching, is done in accordance with retrieved tokenization rules and, if desired, retrieved normalization rules. Tokenization rules are used to define exactly what characters (letters, numbers, punctuation characters, etc.) and exactly what patterns of those characters (one or more contiguous characters, every individual character, etc.) comprise indexable and searchable units of data. Normalization rules are used to (potentially) modify the tokens created by the tokenizer in indexing and/or searching operations. Normalization accounts for things such as case-insensitive searching and language-specific nuances in which document authors can use accepted variations in the spelling of words.