MarkLogic - Open Analytics Meetup

Jul 25, 2013Download as PPTX, PDF0 likes900 views

The document discusses enabling information discovery by unifying search and data management. It provides a brief history of search engines and databases from the 1960s to present. It then proposes that search and databases could be unified by using a schema-agnostic, hierarchical data model with a universal index that can index structured and unstructured data alike. Examples of potential use cases are given, such as creating a 360-degree customer view or enabling fraud prevention. The presentation concludes by suggesting future areas could include more semantic technologies and graph traversal capabilities.

Enabling Information Discovery by
Unifying Search and Data Management
Amir Halfon, CTO
Global Financial Services

Slide 2
Some Recent History
 1994: First full text web search engines become available
 1998: Google is founded
 2003-2004: GFS, MapReduce and BigTable whitepapers
 1999-2005: Lucene, Nutch and Hadoop

Slide 3
Some Not So Recent History
 1960s: Navigational and hierarchical databases (IMS, IDMS)
 1970s: Edgar Codd introduces the relational database
model; System R, INGRESS, and Oracle follow
 1980s: Object databases and ORM tools
 2000s: NoSQL databases

Slide 4
What if the Two Shall Meet?
SEARCHDATABASE

Slide 5
Schema-Agnostic, Hierarchical Data Model
Trade
Cashflows
Payment
Date
Net
Payment
Payer
Party
Receiver
Party
Payment
Amount
tradeId
Party
Identifier
Party
Reference currency amount

Slide 7
Universal Index
Words and phrases
... Semantic Web is a collaborative
movement led by the World Wide Web
Consortium (W3C) ...
Structure Label
Author Ing
Comp
ID Para
Org
Values
name:sorbitol
date:2012-06-04
company:Roche
Entities and positions
... ACE inhibitors, since the
risk of lithium toxicity is very
high in such patients...
Geospatial
<location>
<lat>46.946584</lat>
<lng>93.076172</lng>
</location>
Universal Index

Slide 8
PDF
Word txt
Use Case: 360 Degree Customer View
UNIFIED DATA
SEARCH
Load and index data “as is”
On-boarding docs,
call center logs
Personal
Connections
CardsDDA Mortgages

Slide 10
Use Case: Regulatory Reporting
AUTOMATED LINKAGE
SEARCH; WORKLIST
PDF
Word
Pre-Trade
Communications
Trade
Data
Reference
Data

Slide 11
What’s Next?
 Semantic technology
 Even more power – graph traversal, inference

Slide 12
Amir Halfon
amir.halfon@marklogic.comQuestions?

This document outlines DBpedia's strategy to become a global open knowledge graph by facilitating collaboration on data. It discusses establishing governance and curation processes to improve data quality and enable organizations to incubate their knowledge graphs. The goals are to have millions of users and contributors collaborating on data through services like GitHub for data. Technologies like identifiers, schema mapping, and test-driven development help integrate data. The vision is for DBpedia to connect many decentralized data sources so data becomes freely available and easier to work with.

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This document discusses graphs and graph databases. It provides examples of how organizations like eBay, NASA, and pharmaceutical companies use graph databases to connect disparate data sources and gain insights. Graph databases are becoming increasingly popular due to growth in connected data and can be used for applications like fraud prevention, product recommendations, and knowledge discovery. The document promotes Neo4j as the leading graph database and mentions its adoption by many large enterprises and upcoming cloud services.

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This document provides an introduction to big data and Hadoop. It defines big data as data that is too large and complex for traditional data management tools. Examples are given of the large amounts of data generated daily by organizations. It also discusses the four V's of big data - volume, variety, velocity, and veracity. Hadoop is introduced as a tool for managing large datasets using its distributed architecture and MapReduce programming model.

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The document discusses ICANN's new gTLD program which will allow hundreds of new generic top-level domains (gTLDs) beyond the traditional .com, .net, and .org. This will create new opportunities for companies to control their own domain endings like .target or .golf. However, it also creates challenges for brand owners who will need to monitor their trademarks across many new gTLDs. The application process for new gTLDs will be open from January to April 2012. Successful applicants will pay annual fees to operate the domain and may face auctions if multiple applicants seek the same gTLD. The program includes trademark protections and a clearinghouse for brand owners. Companies are advised to

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MarkLogic - Open Analytics Meetup

1. Enabling Information Discovery by Unifying Search and Data Management Amir Halfon, CTO Global Financial Services

2. Slide 2 Some Recent History  1994: First full text web search engines become available  1998: Google is founded  2003-2004: GFS, MapReduce and BigTable whitepapers  1999-2005: Lucene, Nutch and Hadoop

3. Slide 3 Some Not So Recent History  1960s: Navigational and hierarchical databases (IMS, IDMS)  1970s: Edgar Codd introduces the relational database model; System R, INGRESS, and Oracle follow  1980s: Object databases and ORM tools  2000s: NoSQL databases

4. Slide 4 What if the Two Shall Meet? SEARCHDATABASE

5. Slide 5 Schema-Agnostic, Hierarchical Data Model Trade Cashflows Payment Date Net Payment Payer Party Receiver Party Payment Amount tradeId Party Identifier Party Reference currency amount

6. Slide 6 Vs. the Relational Approach

7. Slide 7 Universal Index Words and phrases ... Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) ... Structure Label Author Ing Comp ID Para Org Values name:sorbitol date:2012-06-04 company:Roche Entities and positions ... ACE inhibitors, since the risk of lithium toxicity is very high in such patients... Geospatial <location> <lat>46.946584</lat> <lng>93.076172</lng> </location> Universal Index

8. Slide 8 PDF Word txt Use Case: 360 Degree Customer View UNIFIED DATA SEARCH Load and index data “as is” On-boarding docs, call center logs Personal Connections CardsDDA Mortgages

9. Slide 9 Use Case: Fraud Prevention Analytics Profile Configuration Profile Data Extracted from Claims Provider and beneficiary profiles

10. Slide 10 Use Case: Regulatory Reporting AUTOMATED LINKAGE SEARCH; WORKLIST PDF Word Pre-Trade Communications Trade Data Reference Data

11. Slide 11 What’s Next?  Semantic technology  Even more power – graph traversal, inference

12. Slide 12 Amir Halfon [email protected]?

Editor's Notes

#5: Schema-agnostic Scalable Scale out on commodity hardware Document-centric Can handle multitude of data types Fully integrated search When organizations are looking for infrastructure to manage and leverage Big Data, they look for three things:A database that can handle unstructured and multi-structured data with ease. Great search capabilities so users can find the data they are looking for and leverage it to make better decisions for the business.Application services and tools that allows developers to build applications quickly and easily so that the data turns into usable information.There are plenty of best of breed technologies out there to serve each one of these functions – but cobbling together a system to do that is time and resource intensive – not only to build, but more so to maintain.MarkLogic provides all three of those capabilities. And, we have the added bonus of having 11 years under our belt to ensure that the system is enterprise hardened with the security, back up, recovery, high availability and data integrity you come to expect from an Enterprise data management system.
#6: Cashflow-matching fpml message exampleSystemautomatically determines how to index data as the data is loaded into the databaseNo a prioriknowledge of data structureNo need for up-front logical data modeling… but some modeling is still importantAdding new data elements or changing data elements is not disruptiveSearching millions of records still has sub-second response time
#7: Every time you take hierarchical data and put it into a traditional database you have to put repeating groups in separate tables and use SQL “joins” to reassemble the data
#9: Key points:Quickly aggregate interaction history from diverse systems across LoBs, as well as onboarding docs (loan origination, etc.)Traverse personal connections graph (social and commercial) to glean new information.Receive alerts based on suspicious activities (fraud) or personal connections (AML), as well as marketing opportunities (targeted offers).Key technical featuresUnstructured content support (onboarding, loan origination docs, etc.)Search (interaction model: quickly grab all customer info based on name, etc.)Semantics (traverse social connections linking customer to corporate entities and other individuals)Schema-on-read (quickly aggregate info across diverse systems/products)Event processing (fraud alert, product targeting suggestions)
#11: Key points:Quickly aggregate interaction history from diverse systems across LoBs, as well as onboarding docs (loan origination, etc.)Traverse personal connections graph (social and commercial) to glean new information.Receive alerts based on suspicious activities (fraud) or personal connections (AML), as well as marketing opportunities (targeted offers).Key technical featuresUnstructured content support (onboarding, loan origination docs, etc.)Search (interaction model: quickly grab all customer info based on name, etc.)Semantics (traverse social connections linking customer to corporate entities and other individuals)Schema-on-read (quickly aggregate info across diverse systems/products)Event processing (fraud alert, product targeting suggestions)
#12: Find all the ISDA CSAs that are affected by a rating change, and aggregate credit risk based on existing positions

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Editor's Notes