A method of performing auto-naming of content includes: receiving an auto-naming command for the content; performing auto-naming of the content by using different parameters according to different content types to obtain at least one auto-naming result for the content; and displaying the auto-naming result.

A schema for a dataset is identified by identifying a dataset comprising data and relationships between data pairs. An original schema is identified for the dataset. This original schema comprises an organizational structure. An initial fit between the dataset and the original schema is determined. The initial fit quantifying a conformity of the data in the dataset to the organizational structure of the original schema. A plurality of additional schemas are identified. Each additional schema is a distinct organizational schema. The dataset is partitioned into a plurality of subsets. Each subset comprises a modified fit quantifying a modified conformity of subset data in each subset to one of the original schema and the additional schemas. The modified fit is greater than the original fit.

A schema for a dataset is identified by identifying a dataset comprising data and relationships between data pairs. An original schema is identified for the dataset. This original schema comprises an organizational structure. An initial fit between the dataset and the original schema is determined. The initial fit quantifying a conformity of the data in the dataset to the organizational structure of the original schema. A plurality of additional schemas are identified. Each additional schema is a distinct organizational schema. The dataset is partitioned into a plurality of subsets. Each subset comprises a modified fit quantifying a modified conformity of subset data in each subset to one of the original schema and the additional schemas. The modified fit is greater than the original fit.

According to an embodiment of the present disclosure, it is determined whether a delta index is beneficial based on the difference between a first version and a second version of a document, wherein the first version is associated with a first index comprising a plurality of keywords appeared in the first version. The delta index is generated for the difference between the first and second versions if the delta index is beneficial, wherein the delta index comprises a first section including information about one or more keywords affected by the difference and the information about the positions of the affected keywords.

A full-text index can be created for each mailbox of an EDB to facilitate the performance of complex queries to quickly search for email data. In this way, relevant email data can be identified and retrieved quickly and efficiently from the full-text index rather than from the EDB. To create such indexes, each email in a mailbox can be retrieved and processed to convert the email from its native format into textual name/value pairs which can then be submitted for indexing. This use of name/value pairs to index each email enables the emails across all mailboxes to be efficiently queried using any possible combination of values.

Systems, methods and computer program products for using searchable aggregate indices associated with non-aggregated value storage. In one method, a search system stores metadata values for each of a plurality of objects in a storage unit. The metadata values are stored in corresponding value storage locations that are associated with an identifiable metadata fields. An aggregate index is provided which includes a dictionary of terms that are contained in metadata values associated with a designated set of the metadata fields. The aggregate index is searched for one or more specific search terms, and one or more of the metadata values are retrieved from the value storage locations in response to the search, where the individual metadata fields associated with the retrieved metadata values are identified.

Disclosed herein are systems and methods for implementing data upload, processing, and predictive query API exposure including means for receiving a dataset in a tabular form, the dataset having a plurality of rows and a plurality of columns; processing the dataset to generate indices representing probabilistic relationships between the rows and the columns of the dataset; storing the indices in a database; exposing an Application Programming Interface (API) to query the indices in the database; receiving a request for a predictive query or a latent structure query against the indices in the database; querying the database for a prediction based on the request via the API; and returning the prediction responsive to the request. Other related embodiments are further disclosed.

According to an aspect, automatically adding new concepts to a concept graph includes receiving a string of text, searching a corpus of data to locate additional text related to the string of text, and extracting concepts from the additional text. The extracted concepts include a subset of concepts in the concept graph. The adding new concepts also includes determining whether the string of text should be linked to an existing concept in the concept graph, performing the linking based on determining that the string of text should be linked to the existing concept in the concept graph and, based on determining that the string of text should not be linked to an existing concept in the concept graph, adding a new concept to the concept graph. The new concept is associated with the string of text.

Improved techniques for processing machine data are disclosed. Embodiments are operable to receive machine data input, interpret its meaning, and then represent that meaning in a knowledge base that grows over time with each new entry. The knowledge base enables extension of both syntax and lexicon, which are the main determinants of meaning. As new entries are added, the knowledge in the knowledge base grows. Over time, the knowledge base acquires more meaning. The disclosed machine data processing system includes entry type recognition, mapping entry types to semantic events, and building entries in the knowledge base based on the semantic event-entry type mapping. Data generated by this process may be used to conduct searches for patterns of semantic events across multiple different machine data sources. This information may then be used to perform useful work such as detecting security threats, identifying operational problems, or tracking customer purchases, etc.

Method of searching comprising applying a function to individual elements within a digital work to form a set of index elements. Storing the index elements as an index for the digital work. Receiving a search term. Applying the function to one or more individual elements within the search term to convert the search term into one or more converted search term elements. Identifying a digital work having an index containing one or more index elements that match one or more of the converted search term elements. Returning search results of the identified digital work.

Method of searching for a digital work comprising the steps of providing a search term. Receiving search results formulated by applying a function to one or more individual elements within the search term to convert the search term into one or more converted search term elements. Identifying a digital work having an index containing one or more index elements that match one or more of the converted search term elements, wherein the index is formed by applying the function to individual elements within the digital work to form a set of the index elements.

Searchable index for a digital work formed by applying a function to individual elements within the digital work to form a set of index elements.