Hybrid in-memory faceted engine

• US 10,747,808 B2
• Filed: 01/19/2017
• Issued: 08/18/2020
• Est. Priority Date: 01/19/2016
• Status: Active Grant

• Current Assignee: Regwez, Inc.
• Original Assignee: Regwez, Inc.
• Inventors: Eleish, Ragy Farouk
• Primary Examiner(s): Pham, Hung Q
• Application Number:
  US15/410,666
• Publication Number:
  US 20170206226A1
• Time in Patent Office: 1,307 Days
• CPC Class Codes: G06F 16/2468 Fuzzy queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/289 Object oriented databases
• CPC Class Codes: G06F 16/41 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/4393 Multimedia presentations, e.g. slide shows, multimedia albums
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/5846 using extracted text
• CPC Class Codes: G06F 16/5866 using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
• CPC Class Codes: G06F 16/901 Indexing; Data structures therefor; Storage structures for retrieval from the web G06F16/951
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06F 21/36 by graphic or iconic representation
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 2221/2113 Multi-level security, e.g. mandatory access control
• CPC Class Codes: G06F 2221/2133 Verifying human interaction, e.g., Captcha
• CPC Class Codes: G06F 2221/2147 Locking files
• CPC Class Codes: G06F 2221/2149 Restricted operating environment
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/0486 : Drag-and-drop
• CPC Class Codes: G06F 3/04883 : for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06F 3/061 : Improving I/O performance
• CPC Class Codes: G06F 3/0638 : Organizing or formatting or addressing of data
• CPC Class Codes: G06F 3/0673 : Single storage device
• CPC Class Codes: G06F 40/186 : Templates
• CPC Class Codes: G06F 40/30 : Semantic analysis
• CPC Class Codes: H04L 63/083 : using passwords cryptographic mechanisms or cryptographic arrangements for entity authentication using a predetermined code H04L9/3226
• CPC Class Codes: H04L 63/10 : for controlling access to devices or network resources
• CPC Class Codes: H04L 63/101 : Access control lists [ACL]
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A system for performing a hybrid in-memory faceted search for one or more digital objects, comprising: a media platform comprising one or more processors configured to: store an index for a data set generated using one or more indexing processes, wherein the index includes a mapping of values to identifiers for each digital object in the data set; receive an update for the index; store the index update with a timestamp independent from the stored index; and in response to a request for the stored index, apply the index updates to the index.

Claims

1. A system for performing a hybrid in-memory faceted search for one or more digital objects, comprising:

a media platform comprising one or more processors configured to;

store an index for a data set generated using one or more indexing processes, wherein the index includes a mapping of a plurality of facet values to a plurality of identifiers for each digital object in the data set;

receive an index update for the stored index;

store the index update independently from the stored index and with a timestamp independent from a timestamp associated with the stored index; and

in response to a next request for the stored index, apply the index update to the stored index.

2. The system of claim 1, wherein the processors are further configured to determine a characteristic associated with the data set;

and wherein the index is processed based on the characteristic of the data set.

3. The system of claim 1, wherein the data set includes discrete data and the index includes multiple arrays for each digital object comprising at least one of sorted values of the discrete data and the plurality of identifiers.

4. The system of claim 2, wherein the identifiers are grouped in ordered groups.

5. The system of claim 1, wherein the data set includes continuous data and each digital object is mapped to a unique timestamp.

6. The system of claim 1, wherein when the mapping of the plurality of facet values to the plurality of identifiers for each digital object in the data set includes overlapping values, the one or more processors are further configured to determine a certainty factor associated with each value for each digital object;

and rank each of the plurality of identifiers based on the certainty factor.

7. The system of claim 1, wherein the request for the stored index is a search request with query criteria, and the one or more processors are further configured to:

generate a selector object to match query criteria to at least some of the plurality of identifiers of the index;

calculate a count of unmodified identifiers associate with each query criteria based on the selector object;

execute the search starting with the query criteria associated with the most unmodified identifiers of the query criteria.