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© 2010 IBM Corporation 1 Content Analytics Solutions September, 2010
© 2009 IBM Corporation 2 Social Network Analysis Showing relationships between people, organisations, phone numbers, etc. Event Timeline Analysis Plotting specific events against a timeline. Social Network and Event Timeline Analysis are just two examples of this – there are many more. Content Analytics – An Increasingly Important Solution Component
© 2009 IBM Corporation 3 SOURCEEXTRACTCORRELATEDFUSED Structured File System Web Articles URN 12345678 Born 1970 Luke Born 22/01/1970 J S Luke Age mid-30s URN 12345678 Born 1970 URN 12345678 Born 22/01/1970 URN 12345678 Age mid-30s URN 12345678 Name J S Luke Born 22/01/1970 Age 34 An Open Information Centric Architecture
© 2009 IBM Corporation 4 SOURCEEXTRACT STORE DATENAMELOCATION 19/04/03LukeUK ….. 24/07/02BentUSA NAMEDATEFLIGHT Biddle29/08/2004BA 256 ….. Coates21/07/2001QA 725 CORRELATION & FUSION TOOLS Structured Web Articles NAMEDOB Luke22/01/1970 ….. Bent25/12/0000 Visualisation i2 GIS ESRI Tenet Search & Discovery Engines OmniFind IBM Content Analytics Data Fusion & Mining SPSS EAS ….. File System An Open Information Centric Architecture
© 2009 IBM Corporation 5 An IBM Content Analytics Solution In Law Enforcement Now, this police department can: Check for errors & inconsistencies with existing databases Provide management with actionable information Have improved search capabilities Perform identity resolution and relationship mining Lockable pocket knife Evidence_2_Description 1 oz Cannabis Resin Evidence_1_Description IpswichSuspect_Addr_Town 22 East Dene RidgeSuspect_Addr_Street Ford MondeoSuspect_Vehicle_Make White Suspect_Vehicle_Colour W563WDLSuspect_VRN SetsukoSuspect_Surname JohnSuspect_Forename 15/06/2006 : 23:47Arrest_Date_Time PC 143Arresting_Officer PC 143 (Hunter) 15 June 2006 23:47 Suspect identified himself as John Setsuko. Matched description given by night club doorman (IC1, Male, Ag 22-24 yrs, blue Everton shirt). Stopped whilst driving White Ford Mondeo, W563 WDL. Address given as 22 East Dene Ridge, Copdock, Ipswich. Searched at scene and found in possession of 1oz Cannabis Resin and lockable pocket knife.
© 2009 IBM Corporation 6
7 SOURCEEXTRACT STORE DATENAMELOCATION 19/04/03LukeUK ….. 24/07/02BentUSA NAMEDATEFLIGHT Biddle29/08/2004BA 256 ….. Coates21/07/2001QA 725 CORRELATION & FUSION TOOLS Structured Web Articles NAMEDOB Luke22/01/1970 ….. Bent25/12/0000 Visualisation i2 GIS ESRI Tenet Search & Discovery Engines OmniFind IBM Content Analytics Data Fusion & Mining SPSS EAS ….. File System Open Information Architecture
© 2009 IBM Corporation 8 IBM Visual Search For A Government Agency The Goal:The Problem:The Solution: Reducing analysts time in locating relevant information. Keyword search technologies do not allow the definition of complex searches. For example, “find every person mentioned in a document describing drug smuggling associated with another person mentioned in a document describing organised crime.” Deployment of a graphical search interface enabling the definition of complex patterns.
© 2009 IBM Corporation 9 Find groups of 3 people who are linked together and are associated with the same organization
© 2009 IBM Corporation 10 IBM Success at a Government Agency The automated solution saved each analyst over 6 hours per day, improving the quality and consistency of analysis The Goal:The Problem:The Solution: I dentify the re-occurrence of phone numbers within historical documents. Using keyword search technologies had historically resulted in large numbers of false hits for credit card, visa and other reference numbers. The tedious nature of the task also resulted in oversights and errors. Deployment of an automated software solution to analyze documents and identify recurring phone numbers Semantic rules were used to ensure a high degree of accuracy All extracted phone numbers were compared against other documents with the results visualized through a carefully designed User Interface.
© 2009 IBM Corporation 11 What’s the business case? How good is the text analytics? How do we know how good the text analytics is? How do we respond to changes in the content and of course the business environment? Are we creating, rather than solving a problem, when we invest in text analytics? What Are The Inhibitors?
© 2009 IBM Corporation 12 New Architectural Models For Text Analytics
© 2009 IBM Corporation 13 New Architectural Models For Text Analytics Real-time Analysis Index Driven Annotation Engine Node 1 … Interactive Rule Development & Manual Annotation Enterprise Services Geospatial Analysis Network Analysis Semantic Search … Node 1 Node 2 Node n Large scale development / training / test corpus Near real-time feedback on impact Analytics as opposed to speculation (mining instead of prospecting)
© 2010 IBM Corporation 14 IBM Text Analytics Solution November, 2009
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