Aviv D Orani

20060235831, Oct 19, 2006

Dec 22, 2005

11/318355

Ronald Adinolfi - Yardley PA, US
Cornelius Crowley - Morristown NJ, US
Teresa Glasser - New York NY, US
Jennifer Gromada - Princeton NJ, US
Max Hrabrov - Brooklyn NY, US
Guerney Holloway Hunt - Yorktown Heights NY, US
Sugandh Mehta - Wayne NJ, US
Francis Parr - New York NY, US
Aviv Orani - Highland Park NJ, US
Matthew Rice - Medway MA, US

G06F 17/30

707003000

Forming and maintaining a multi-source multi-tenant data repository on behalf of multiple tenants. Information in the multi-source multi-tenant data repository is received from multiple sources. Different sources and different data quality enhancement processes may yield different values for attributes of the same referred entity. Information in the multi-source multi-tenant data repository is tagged with annotations documenting the sources of the information, and any data quality processing actions applied to it. Tenants of the multi-source multi-tenant data repository have entitlement to values from some sources and to the results of some quality enhancement processes. Aspects of the method maintain this entitlement information; employ evolutionarily tracked source data tags; receive requests for information, locate the requested information, apply any sourcing preference, enforce entitlements and return entitled values to the requester. An outsourced reference data utility is one context where such a multi-source multi-tenant data repository is useful.

20060247944, Nov 2, 2006

Dec 22, 2005

11/318425

Edward Calusinski - Bartlett IL, US
Cornelius Crowley - Morristown NJ, US
Teresa Glasser - New York NY, US
Jennifer Gromada - Princeton NJ, US
Max Hrabrov - Brooklyn NY, US
Guerney Hunt - Yorktown Heights NY, US
Kenneth Jones - Lansdale PA, US
Sugandh Mehta - Wayne NJ, US
Francis Parr - New York NY, US
Aviv Orani - Highland Park NJ, US

G06Q 99/00
G06Q 40/00

705001000, 705035000

Provision for scalable cleansing and value enhancement of data in the context of a multi-source multi-tenant data repository. The source data comes from multiple sources and on multiple topics. Evolutionarily tracked source data tags are used to hold tracking information reflecting the nature and sources of each change to the data, as it is affected during the various stages of data processing. The stages of processing include validation, normalization, single-source cleansing and cross-source processes. Various rules are applied during these stages, and evolutionarily tracked source data tags are used to record sources and agents of all changes to the data. As information is processed, transformed, and added to the repository, corresponding evolutionarily tracked source data tags are stored in association with the various information elements. The information contained in these tags can be used to enforce data entitlements in a multi-tenant data repository environment.

20060235715, Oct 19, 2006

Dec 22, 2005

11/318428

Carl Abrams - Briarcliff Manor NY, US
Ronald Adinolfi - Yardley PA, US
Edward Calusinski - Bartlett IL, US
Cornelius Crowley - Morristown NJ, US
Michael Fleming - Eastchester NY, US
Teresa Glasser - New York NY, US
Jennifer Gromada - Princeton NJ, US
Max Hrabrov - Brooklyn NY, US
Guerney Hunt - Yorktown Heights NY, US
Kenneth Jones - Lansdale PA, US
Sugandh Mehta - Wayne NJ, US
Francis Parr - New York NY, US
Aviv Orani - Highland Park NJ, US
Matthew Rice - Medway MA, US

G06Q 99/00

705001000

A multi-source multi-tenant reference data utility and methods for forming and maintaining the same, delivering high quality reference data in response to requests from clients, implemented using a shared infrastructure, and also providing added value services using the client's reference data. Included are data cleansing and quality assurance of the received data with full tracking of the sourcing of each value, storage of resulting entity values in a repository which allows retrievals and enforces source based entitlements, and delivery of retrieved data in the form of on demand datasets supporting a wide range of client application needs. An advantageous implementation has additional services for reporting on data quality and usage, a selection of value adding data driven computations and business document storage. By using a shared infrastructure and amortizing the costs of data quality assurance across a plurality of clients, while ensuring that clients only receive values from data sources to which they are licensed, better quality data at lower cost is delivered.