IBM Content Analytics

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1 IBM Content Analytics Trendthema Semantische Textanalyse Software Experience 08. März 2012 IBM Industry Solutions IBM Software Group 1

2 Trademarks and notes Copyright IBM Corporation 2012 IBM Software Group One Rogers Street Cambridge, MA U.S.A. Produced in the United States of America February 2011 All Rights Reserved IBM, the IBM logo, and ibm.com are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at Copyright and trademark information at ibm.com/legal/copytrade.shtml. References in this publication to IBM products or services do not imply that IBM intends to make them available in all countries in which IBM operates. Client success stories are available at ibm.com/software/success/cssdb.nsf. The information contained in this documentation is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this documentation, it is provided as is without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this documentation or any other documentation. Nothing contained in this documentation is intended to, nor shall have the effect of, creating any warranties or representations from IBM (or its suppliers or licensors), or altering the terms and conditions of the applicable license agreement governing the use of IBM software. IBM customers are responsible for ensuring their own compliance with legal requirements. It is the customer s sole responsibility to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer s business and any actions the customer may need to take to comply with such laws. 2

3 Agenda 09:15-09:30 Empfang Eintreffen der Teilnehmer und Begrüßungskaffee 09:30-10:15 IBM Content Analytics Einführung 10:30-11:00 Lab: Content-Analyse von Kundenrückmeldungen 11:00-11:15 Kaffeepause 11:15-12:30 Lab: Enterprise Search und automatische Klassifikation 12:30 12:45 Mini Roundtable 12:45-13:30 Mittagessen 13:30-14:00 ICA Showcase Pressemitteilungen & Smartphones 14:00 15:00 Lab: Content-Analyse und Klassifikation 15:00-15:15 Kaffeepause 15:15-15:30 Einsatzmöglichkeiten von IBM Content Analytics 15:30-15:45 Fragen, Diskussion, Ausklang 3

4 IBM Enterprise Content Management 4

5 IBM Enterprise Content Management Clients & Integrationen (FileNet T, Notes, Outlook, Sharepoint, Portal, Connections, Forms, Quickr, Office Integration...) ACM Advanced Case Management BPM Business Process Management Content Analyse Knowledge Management & Geschäftsfall Management Records Management Unternehmensweite DMS Suche & Recherche Content Management System Output & Report Management (Cold) Scanning & Klassifikation Content & Fileserver Management SAP Archivierung Federation & Integration Services ERP Integration Compliance Information Management Archiv: Storage Management / Revisionssicheres Archiv Plattformen: Windows, AI, Linux, Sun Solaris, HP-U, iseries, System z 5

6 Versicherung in USA: Analyse von Schadensmeldungen: Hilfe bei Erkennung von Unregelmäßigkeiten 1 Schadensmeldungen 2 3 Content Analytics Based Predictive Fraud Indicators: ed ov r p Im In ato c i d Soft Tissue Injury Unwitnessed Event Prior Injury Multiple Claims rs...der Wasserschaden könnte durch verstopften Kanal verursacht worden sein. Ein verstopfter Kanal in diesem Strassenzug ist leider keine Seltenheit Verstopfter Kanal führte zu Rückstau und Wasserschaden.. Datenanalyse 5 Auffällige Meldungen an Sachbearbeitungen senden... 6

7 Weitere Kundenbeispiele (Auszug) Öffentlicher Arbeitgeber in Deutschland mit weltweiten Einsatzgebiet Polizei in New York und Estland Callcenter einer Fluggesellschaft in der Schweiz Callcenter eines Mobilfunkanbieters in Italien Rundfunkanstalt in Großbritannien Versicherungen in Großbritannien, USA und Japan Medizinbereich: Universitäten in USA und Health Prototyp in Israel Universität & FH in Linz Anmerkung: Kundenbeispiele und Screens wurden aus dieser Version entfernt 7

8 IBM Content Analytics adds value to 88 Healthcare Analytics Customer Care Analyzing: E-Medical records, hospital reports For: Clinical analysis; treatment protocol optimization Benefits: Better management of chronic diseases; optimized drug formularies; improved patient outcomes Analyzing: Call center logs, s, online media For: Buyer Behavior, Churn prediction Benefits: Improve Customer satisfaction and retention, marketing campaigns, find new revenue opportunities Crime Analytics Insurance Fraud Analyzing: Case files, police records, 911 calls For: Rapid crime solving & crime trend analysis Benefits: Safer communities & optimized force deployment Analyzing: Insurance claims For: Detecting Fraudulent activity & patterns Benefits: Reduced losses, faster detection, more efficient claims processes Automotive Quality Insight Social Media for Marketing Analyzing: Tech notes, call logs, online media For: Warranty Analysis, Quality Assurance Benefits: Reduce warranty costs, improve customer satisfaction, marketing campaigns Analyzing: Call center notes, SharePoint, multiple content repositories For: churn prediction, product/brand quality Benefits: Improve consumer satisfaction, marketing campaigns, find new revenue opportunities or product/brand quality issues

9 Trendthema unstrukturierte Daten Daten enthalten Information und menschliches Wissen Strukturierte Daten: zb in Datenbanken, Warehouse, BI-Tools, etc.. Semi-strukturierte Daten: ML, Metadaten in Dokumenten Management Systemen, Eigenschaften von Word-Datein, etc.. Unstrukturierte Daten: Text, Audio, Video *UIMA Whitepaper by IBM 9

10 Ziel der Text Analyse Tools Strukturieren von Unstrukturierten oder Semi- strukturierten Daten Example 10

11 Text Analytics The term text analytics describes a set of techniques: Linguistic Statistical Machine learning These techniques model and structure the information content of textual sources for business intelligence exploratory data analysis research investigation. The term is roughly synonymous with text mining Text Analytics is now more frequently in business settings while "text mining" is used in life-sciences research government intelligence. 11

12 Text Analytics Text analytics involves information retrieval lexical analysis to study word frequency distributions pattern recognition tagging/annotation information extraction data mining link and association analysis Visualization predictive analytics The overarching goal is, essentially, to turn text into data for analysis via application of natural language processing (NLP) analytical methods. 12

13 Entwicklung von Text Analytics (1/2) Der Kunde ist nicht zufrieden mit Mobil Telefon, Anruf vom Kunde möchte zu Yellow inc wechseln Wort Extraktion Kd Kunde Yellow inc zufrieden nicht wechseln Mobil Telefon 70 er 13 BegriffsExtraktion Kunde Yellow inc Mobil Telefon Nicht zufrieden Named Entities Recognition (NER) Kunde -> CRM-Begriff Kd? Yellow inc -> Telco Company (nicht die Farbe) Mobil Telefon -> Telco-Begriff nicht zufrieden 18. Nov > Date 80 er 90 er

14 Entwicklung von Text Analytics (2/2) Der Kunde ist nicht zufrieden mit Mobil Telefon, Anruf vom Kunde möchte zu Yellow inc wechseln Sentiment-Analyse findet den Stimmungsindikator Kunde (Kd) Mobil Telefon unzufrieden (negativ); Wechseln zu (negative Vorhersage) yellow inc (Wettbewerber) Kombination strukturieretr mit strukturierten Daten und Events /Alerts Schnelle Entscheidung Wechselwilliger Kunde hoher Kundenwert? individuelles Angebot Heute 14 14

15 UIMA - Unstructured Information Management Architecture UIMA beschreibt die Architektur und das Framework zu Erzeugung von UIMA konformen Applikationen UIMA Projekt wurde von IBM gestartet und als Open Source frei gegeben Heute definiert UIMA ein OASIS Standard und wird seit 2006 von Apache betreut (http://uima.apache.org/) Apache UIMA is an Apache-licensed open source implementation of the UIMA specification Grundidee (by IBM): Einheitlicher Standard zur Strukturierung von unstrukturierten Informationen Data Representation: Artefakte und Metadaten unabhängig vom Quell-Objekt Data Modeling and Interchange: Plattform unabhängige Analyse und Austausch Discovery, Reuse and Composition: der Analytischen Komponenten (zb Annotator) Service Level Interoperability: WSDL: ML Repräsentation für SOAP Services 15

16 UIMA Komponenten (Auszug) CAS (Common Analysis Structure ) als spezielle Datenstruktur = Container..is the common data structure shared by all UIMA analytics to represent the unstructured information being analyzed (the artifact) as well as the metadata produced by the analysis workflow (the artifact metadata)... for the independent development of text analytic annotators. Each annotator adds its own annotations to the CAS. The CAS in its entirety is then made available to the next annotator in the UIMA pipeline. AE (Analysis Engine) Basic Building Blocks der Analyse von artifacts (unstrukturierten Segmenten) Diverse Algorithmen werden in AEs zu sog Annotatoren 16

17 Structure of UIMA - Analytics Pipeline The UIMA architecture can be thought of in four dimensions: 1. It specifies component interfaces in an analytics pipeline 2. It describes a set of Design Patterns 3. It suggests two data representations: an in-memory representation of annotations for high-performance analytics an ML representation of annotations for integration with remote web services. 4. It suggests development roles allowing tools to be used by users with diverse skills 17

18 UIMA & IBM Content Analytics (ICA) IBM Content Analytics basiert auf einer Apache UIMA compliant processing engine diese Technologie wurde u.a. auch im IBM Watson verwendet. Text Analytics (NLP- Natural Language Processing) describes a set of linguistic, statistical, and machine learning techniques that allow text to be analyzed and key information extraction for business integration. Content Analytics (Text Analytics + Mining) refers to the text analytics process plus the ability to visually identify and explore trends, patterns, and statistically relevant facts found in various types of content spread across internal and external content sources. Statistical and rule-based Natural Language Processing 18

19 UIMA Pipeline & IBM Content Analytics (ICA) Document Processing Engine Indexable Document with Fields and Facets Raw Text document ICA Index Indexer Indexer Task Raw Data Store Document Processor 19 Classification Multi-word Analytics Named Entity Recognition Word Analytics Word Custom Analytics Parser UIMA CAS Tokenization Raw Text document Language Identification Document Document Processor Processor UIMA CAS UIMA Indexable Document with Fields and Facets Document Generator

20 UIMA Pipeline & IBM Content Analytics (ICA) Annotator- a software component that performs linguistic analysis tasks and produces and records annotations 20

21 UIMA Pipeline in IBM Content Analytics (ICA) Language Identification annotator: erkennt die Sprache(n) Linguistic Analysis annotator: zerlegt die Sätze Dictionary Lookup annotator: matched Worte & Synonyme Named Entity Recognition annotator: findet Entitäten Pattern Matcher annotator: erkennt Sequenzen im Text IBM Classification Module annotator Custom annotators 21

22 Bsp: Named Entity Recognition Annotator Annotiert Personennamen, Orte/ Länder, Firmennamen im Artefakt Lookup gegen ein Dictionary (zb alle Plz & Ortsnamen in Österreich) 22

23 Bsp: Pattern Matcher Annotator Definition von Mustern/ Pattern als Analyse Subjekt Erkennung komplexer Zusammenhänge im Text (z.b. positive, negative, neutrale Aussagen) Pattern können einer Sprache zugeordnet werden Bsp Erkennung von Text: The product is not broken Products are not working Rule: [product name] + [be] + [negative term] = product problem [product name] + [be] + [not] + [positive term] = product problem 23

24 UIMA Pipeline in IBM Content Analytics (ICA) Custom Annotator Custom Annotator Regular Expression Dictionary Named Entity Identify Languages Crawled Documents Find Words & Roots Text Analytic Annotators UIMA Framework and Pipeline Inbound Unstructured Data Device Malfunction Description: It was reported that during a gastric bypass rouxen-y procedure, on the 3rd firing with a blue load on the stomach there was bleeding. Not sure if staples were formed properly. They over sewed the staple line. There was no PT consequence Annotated Documents Text Analytic Collection annotations Extracted / Derived Information Involved Body Part Type of Injury Procedure Performed stomach bleeding gastric bypass surgery

25 (UIMA) Erstellen von Custom Annotators UIMA SDK: Apache UIMA Java Framework stellt eine Java-basierte Runtime Environment bereit (zahlreiche BSP) IBM UIMA SDK: Eclipse-based development environment auf IBM DeveloperWorks Java based IBM LRW: IBM LanguageWare Resource Workbench stellt eine zu ICA voll integrierte Entwicklungsumgebung bereit Apache UIMA: stellt noch weitere Frameworks bereit (z.b. C++ framework) und teilweise werden z.b. Perl, Python, and TCL annotators unterstützt. Apache UIMA-AS: Asynchronous Scaleout, Erweiterung des Collection Processing Managers.. teilweise in IBM Watson verwendet, ICA hat eigene Collectors 25

26 LRW IBM LanguageWare Resource Workbench Provides a complete development environment for building and customization of dictionaries, rules and UIMA annotators. Allows the user to focus on the concepts and relationships of interest, and to develop analyzers which extract them from text without having to write any code OOTB integration between LRW and ICA UIMA annotators and resources exported by LRW can be easily imported into ICA as custom text analysis. Leading innovator across SWG in the application & standardization of text analytics and Natural Language Processing technology 11 patents, 9 disclosures, many publications from our Research partners Integrated into technology across IBM divisions 26

27 LRW & IBM Content Analytics Integration LRW 7.2 and ICA 2.2 significantly enhanced the ability to use LRW for ICA: LRW users can export a UIMA Pipeline directly from the LRW and have it installed as the Custom annotator stage of an IBM Content Analytics Collection no more multiple steps using both tools! ICA Fields and Facets can be assigned to UIMA Types created in the pipeline, and will be automatically created on the ICA server if necessary. LRW users can send one or more documents to ICA to be annotated by the pipeline configured for one of the Collections on the server. View the resulting annotations in LRW Compared the results to previous annotation results. 27

28 Developing a model is an iterative process between LRW and ICA ICA Text Miner ICA Crawlers ICA Document Processing ICA is the platform for finding insights using the annotations!! 28 ICA Custom Annotator Configuration

29 ICA System Architektur 29

30 IBM Content Analytics v2.2 Enterprise-Wide Content Reach CA-Datacom 10 Microsoft Exchange Server 2003 IBM Content Manager Enterprise Edition 8.4, 8.4.1, DB2 for iseries 5.4 and 6.1 Microsoft Windows SharePoint Services 3.0 DB2 UDB for Linux, UNI, Windows 9.1, 9.5 and 9.7 DB2 for z/os 8.1, 9.1 SharePoint Server 2003 SP2, 2007 & 2010 Microsoft SQL Server 2005, 2008 MySQL 5.0 EMC/Documentum 6.0 and 6.5 Network News Protocol Newsgroup FileNet Content Services 5.4, 5.5 FileNet P8 Content Manager 4.0, 4.5, and 5.0 Hummingbird DM with SR6 and IMS 10.0 and Open Text Livelink Enterprise Server 9.6, 9.7 and Oracle 10g and 11g Software AG Adabas 7.1 Sybase , 12.0, 12.5x Informix Dynamic Server and IBM Case Manager V5.0 UNI file systems VSAM for z/os 1.4 IBM Lotus Connections and IBM Lotus Domino DM 6.5.1, 7.0, IBM Lotus Domino R7, R8 and R8.5 Web (HTTP or HTTPS) IBM WebSphere Portal 6.1, & 7 Windows file systems: 2003 and 2008 servers IBM Lotus Quickr (NSF & J2EE), 8.1, 8.2 and 8.5 Lotus Web Content Management 6.1, 6.1.5, &

31 ICA Language & Function Support* Part of speech Phrase constituent Pattern Matching ICM Integration Arabic Chinese Danish Dutch English French German Italian Japanese Portuguese Spanish Language Person Location Organization *Ability to add new language modules 31

32 ICA - IBM Content Analytics Customer Service Product Management Corporate Reputation Churn Alerts Voice of the Customer Monitor FAQs CSR Training and Monitoring Market Sentiment Service Management Sales Partners Marketing Industry Reports 32 Market Research Transcripts CSR Logs Web Internal Docs and Reports Blogs

33 Literatur IBM Red Book: IBM ICA Information Center: IBM ICA Product Website: UIMA: UIMA Whitepaper by IBM: UIMA IBM SDK: IBM LRW Education: topic=/com.ibm.iea.lrw/plugin_coverpage.html IBM DeveloperWorks: https://www.ibm.com/developerworks/community/groups/service/html/communityview? communityuuid=6adead f6-bdbb-baf0d2e8a673 IBM Watson & IBM ICA Whitepaper: 33

34 Lokale Ansprechpartner 34

35 35

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