Big Data Appliance for Graph Analytics

1. Big Data Appliance for Graph Analytics ArvindParthasarathi

2. Many Big Data Business Problems are based on Graphs Telecom/Mobile Social Networking Intelligence/Security Life Sciences/ Biology Healthcare/ Medicine Finance Supply Chain Internet/ www Finance

3. Growing Momentum… United States Government Canadian Government

4. Current Big Data approaches are based on SEARCH… Current Big Data Approaches: Search: Partition and Scale out Needle-in-a-Haystack Paradigm … … • When we know what we’re looking for • We can break up the problem

5. YarcData focuses on DISCOVERY — not search Needle-in-a-Needlestack When we don’t know what we’re looking for… We can’t break up the problem. Current Big Data Approaches do NOT enable Discovery “ “ When the relationships among data are mysterious, and the nature of the inquiries unknown, no meaningful scheme for partitioning the data is possible. • …when the purpose of the system is discovery of relationships, not extracting information from • already known interrelations, achieving satisfactory performance is difficult. “ “ (Source: Gartner Report, YarcData'suRiKA Shows Big Data Is More Than Hadoop and Data Warehouses, September 2012)

6. YarcDataaugments existing Data Warehouse/Hadoop environments with graph analytics

7. Discovery through fast hypothesis validation “ “ In the amount of time it takes to validate one hypothesis, we can now validate 1000 hypotheses– massively improving our success rate and systematizing serendipity. “ YarcDataGovernment Customer

8. Government Organization: Discovering New Plots • Goal: Proactively identify patterns of activity and threat candidates by aggregating intelligence and analysis • Data sets: reference data, People, Places, Things, Organizations, Communications… • Technical Challenges: Volume, Variety and Velocity of data; Inaccurate, incomplete and falsified data • Users: Intelligence Analysts • Usage model: Search for patterns of activity and graphically explore relationships between entities for candidate behavior and activities • Augmenting: Existing Hadoop cluster and multiple data appliances

9. Healthcare Provider: Discovering New Treatments • Goal:Proactively identify optimal treatments for patients based on treatment results for “similar” patients • Data sets: Longitudinal patient data, Family history, Genetics, Reference data • Technical Challenges: Ad-hoc constantly changing definition of “similarity” across thousands of constantly changing attributes • Users: Doctors • Usage model: Compare results of candidate treatment options for “similar” patients based on ad-hoc physician specified patterns • Augmenting: Existing data warehouse

10. Government Organization: Discovering new cyber threats • Goal: Proactively identify unknown cyber threats by examining all relationships • Data sets: IP, MAC, BGP, Firewall, DNS, Netflow, Whois, NVD, CIDR… • Technical Challenges: Volume and Velocity of data; Temporal dependencies; Real-time response • Users: Cyber Analysts • Usage model: Iterative analysis of all patterns across all traffic to explore deviations in frequency of occurrence, derivative patterns of known threats and linking patterns through relationships in offline data • Augmenting: Existing data appliances

11. Healthcare Payer: Discovery New Fraud Patterns • Goal: Proactively identify new patterns of healthcare fraud (perpetrator/provider/patient nexus) by examining all healthcare relationships • Data sets: Provider, Beneficiary, Policy, Claims, Billing, Services, Outcomes, Social Network, Organization… • Technical Challenges: Volume and Velocity of data; Entity Resolution; Complex Inter-relationships; Temporal dependencies • Users: Fraud Inspectors/Analysts • Usage model: Analyze outcome and cost for various disease trajectories and identify outliers for treatment optimization and fraud investigation; Separate fraud patterns from benign treatment or legitimate errors • Augmenting: Existing data warehouse, Predictive Analytics

12. YarcData technology enables Big Data Graph Analytics “ Employing a single system-wide memory means data does not need to be partitioned, as it must be on MapReduce-based systems like Hadoop. Any thread can dart to any location, following its path through the graph, since all threads can see all data elements. This greatly reduces the imbalances in time that plague graph-oriented processing on Hadoop clusters. “ “ A unique approach to processor design, YarcData'sThreadstorm chip, shows no slowdown under the characteristic zigs and zags of graph-oriented processing… the data is held in-memory in very large system memory configurations, slashing the rate of file accesses. “

13. …while making it easy for Enterprises to adopt “ Open Standard Interface – RDF/SPARQL from W3C YarcData…uses Linux-ized processors to blow through graph analyses at speeds beyond classic RDBMS’ imagination – jet plane versus burro. Admins see just another Linux server – provisioning, security, management… “ Packaged as an appliance to hide complexity “ We are also impressed with… its product strategy, which combines the advantages of a pre-integrated hardware appliance with the flexibility of a subscription model. Subscription-based pricing for on-premise deployment “

14. YarcData: Big Data Appliance for Graph Analytics Graph analytics warehouse supports ad hoc queries, pattern-based searches, inferencing and deduction on dynamic data sets • Gain business insight • by discovering unknown • relationships in big data • Achieve competitive • Advantage with scalable • real-time graph analytics • Purpose built to solve big data graph problems with large shared-memory and massive multi-threading Data center ready appliance with open interfaces enables re-use of in-house skill sets, no lock-in and simplified integration • Ease adoption • with subscription • pricing and industry • standards support

15. Spacer to top left of heading – 9mm x 7mm Growing Momentum… Titles: Arial Bold – 22 points color: RGB: 72 102 133 Body Copy on typical pages, including bulleted copy Body Copy: Arial – 18 points RGB: 77 77 79 = 85% gray Color Palette