Abstract: A multi-model data store comprises an enrichment catalog and an enrichment memory graph. Raw data from a source raw datastore is enriched based on an enrichment function. Enriched data is stored in graph nodes with edge associations indicating the enrichment type and confidence level. The enriched data is also stored in the enrichment catalog with full breadcrumb provenance and lineage identifying, with cataloged enrichment steps, down to a record and field in the source raw datastore. Additional enriched values and associations between graph nodes can be entered in the graph by a user. Queries to the multi-model database begin in the graph and continue to the enrichment catalog. Query results include the full sequential chain of enrichments to source raw data records and fields, and may include dynamically generated virtual enrichment catalog records that link search values not found in the enrichment catalog to enrichment catalog records via the graph.

Abstract: A multi-model data store comprises an enrichment catalog and an enrichment memory graph. Raw data from a source raw datastore is enriched based on an enrichment function. Enriched data is stored in graph nodes with edge associations indicating the enrichment type and confidence level. The enriched data is also stored in the enrichment catalog with full breadcrumb provenance and lineage identifying, with cataloged enrichment steps, down to a record and field in the source raw datastore. Additional enriched values and associations between graph nodes can be entered in the graph by a user. Queries to the multi-model database begin in the graph and continue to the enrichment catalog. Query results include the full sequential chain of enrichments to source raw data records and fields, and may include dynamically generated virtual enrichment catalog records that link search values not found in the enrichment catalog to enrichment catalog records via the graph.

Abstract: Fuzzy string comparison between a first textual string of a first orthography and a second textual string of a second orthography is provided. The first and second textural strings are transliterated to a first phonetic spelling and a second phonetic spelling, respectively. Application of constraints of the first orthography on the first phonetic spelling and of constraints of the second orthography on the second phonetic spelling is detected. The detected constraints of the first orthography on the first phonetic spelling are reversed to generate a first revised phonetic spelling, and the detected constraints of the second orthography on the second phonetic spelling are reversed to generate a second revised phonetic spelling. The first revised phonetic spelling and the second revised phonetic spelling are compared to generate a similarity score for the first textual string and the second textual string.

Abstract: Operating systems can be constructed through working “in the clouds” without dependence on a specific piece of hardware. Over the Internet, an operating system is constructed based on received requests. Received requests disclose modules that are to be added to the operating system and these modules are added. A constructed operating system can be tested in context of a web server and then downloaded to an electronic device. The electronic device boots the operating system from virtual hard disk by utilization of bare metal deployment and/or non-metal deployment.

Abstract: Operating systems can be constructed through working “in the clouds” without dependence on a specific piece of hardware. Over the Internet, an operating system is constructed based on received requests. Received requests disclose modules that are to be added to the operating system and these modules are added. A constructed operating system can be tested in context of a web server and then downloaded to an electronic device. The electronic device boots the operating system from virtual hard disk by utilization of bare metal deployment and/or non-metal deployment.