1. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 1 The Evolution of Semantic Technologies-The Value of Merging Smart Data With Big Data Eric Little, PhD VP – Chief Scientist

2. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 2 Who is Modus Operandi?  Privately held small business headquartered in Melbourne, FL. Satellite locations in Aberdeen, MD and Ft. Huachuca, AZ. 82% of employees possess a security clearance.  U.S. Government is our primary customer Expanding into select commercial markets

3. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 3 IT’s common challenge  TOO BIG - Too much data or too many variables  SILOED - Data is in legacy silos so nothing is integrated  LOST EXPERTISE - SME info is lost in people’s heads  NO EXCHANGE - No good processes for data exchange  NO VIZ - No good ways to visualize data  NO QUALITATIVE - Cannot use statistical tools to get qualitative answers  DIRTY DATA - Too many errors in the data  NO RULES - No way to capture business rules without big coding effort  NO VOCAB - No good vocabularies exist to capture data elements  MANY MODELS - Too many data models to be controlled effectively

4. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 4 MODUS APPROACH Rather than build one type of technology we realize the need for an end-to-end platform to provide solutions for our customers

5. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 5 The “Cognitive Evolution” of Intelligent Software  Semantic technologies are part of an IT evolution from code to data centricity In the Code-Centric years, data was often stored in flat files with no structure, while complex, procedural “edit” programs contained all knowledge about the data The creation of databases, specifically Network and RDBMS, was one of the first steps leading to Data-Centric evolution The last decade has seen standards such as XML, RDF, Web services, and now OWL, that further evolve IT to a Data-Centric environment  Big data and scalability is now helping to shape semantic tech at large scale. Big data science Retrieval at Scale is most important

6. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 6 New & Expanding Tech Areas  The past few years have seen a significant rise in new tech fields data science, big data analytics, semantic technologies, natural language processing, graph computing, and systemics  These areas provide new paradigms for data analysis and integration  These are driving new innovations in the ways people can access and use their data.

7. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 7 Innovation is Key in These Types of Tech Spaces  The idea seems straight-forward and easy But it is difficult to find true spots of Blue Ocean  Requires new approaches that are taken from numerous disciplines  Small businesses need to compete by focusing and being disruptive Being disruptive involves the counter intuitive approach of focusing on specific market segments Requires an ability to be nimble and respond quickly to needs (iterative prototyping) Every wave is different – reading the wave is key

8. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 8 SEMANTIC TECHNOLOGY

9. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 9 Semantics and Reasoning

10. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 10 Semantic Approach Improves Data Access 10 Traditional Approach Semantic Approach Database Experts Domain Experts & Scientists Systems Engineers Management & Executives Manual Data Correlation Manual Report Generation (High Potential for Error) Integrated Classifications/Schemas Automated Reasoning Capabilities (Significant Error Reduction) Domain Experts & Scientists Systems Engineers Management & Executives Ontology Engine

11. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 11 Semantic Approach Simplifies Queries Traditional Approach Database Experts Query Must Contain: 1.Data Requirements 2.All Logi c Required to Relate the Data (Rules, Joins, Decode, Sub-queries, etc.) Complexity: HIGH Reusability: LOW-MED Semantic Approach Reasoning is done on the user side for each query Reasoning is performed by Ontobroker within the system Database Experts Scientists, Systems Engineers Management & Executives Query Must Contain: 1.Data Requirements only Complexity: LOW Reusability: HIGH (Logic embedded in Model)

12. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 12 12 Building Semantic Profiles From Raw Data Key data elements are identified – creating lexicon of important terms Data elements are categorized into appropriate classes – ranges are captured for autoclassification Can be applied to any type of data elements: equipment, reports, products, processes, etc. Advanced logics allow for reasoning over data sets such that new patterns and information can be gained

13. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 13 Utilizing Semantics to Integrate Disparate Medical Data 807862ACETAMINOPHEN (TYLENOL) LEVEL 855406Acetaminophen (Tylenol) Level 2273960Acetaminophen (Tylenol) Level 7543180ACETAMINOPHEN (TYLENOL) LEVEL 253965Acetaminophen [ Tylenol] 512270Acetaminophen + Codeine 3016745Acetaminophen + Codeine 6075682Acetaminophen + Codeine 6327790Acetaminophen + Codeine 1688184Acetaminophen + Codeine (120mg-12mg/5ml) (NF) Liquid 1701785Acetaminophen + Codeine (120mg-12mg/5ml) (NF) Liquid 3967939Acetaminophen + Codeine (120mg-12mg/5ml) (NF) Liquid 7271363Acetaminophen - 325 mg PO q4h PRN Temp >101 7881183Acetaminophen - 650 mg PO q6h PRN Pain 64654ACETAMINOPHEN SUPP 325 MG SUPP 4851508ACETAMINOPHEN SUPP 325 MG SUPP 9870184Acetaminophen Tab 325MG 679752ACETAMINOPHEN TAB 325 MG TAB 1715007ACETAMINOPHEN TAB 325 MG TAB 2292336ACETAMINOPHEN TAB 325 MG TAB 3914196ACETAMINOPHEN TAB 325 MG TAB 6768031ACETAMINOPHEN TAB 325 MG TAB 8163956ACETAMINOPHEN TAB 325 MG TAB 9629590ACETAMINOPHEN TAB 325 MG TAB 6802504Acetaminophen (160mg/5ml) Suspension Hospital 1 Data Hospital 2 Data Disparate data sources can be ingested by the system and automatically classified into their appropriate class, attributes, etc. The models only need to be developed initially with the help of medical SMEs (as opposed to continuous point-to-point mappings with traditional systems). Common Data Model

14. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 14 Classification Schemas Must Reflect Subject Matter Expertise Orbis Technologies, Inc. Proprietary14 SME’s are often ill equipped to capture their knowledge semantically Knowledge can be captured in ontologies (as attributes, advanced relationships, etc.) – but this requires a separate skills set Multiple ontologies can be integrated to capture enterprise-wide applications for advanced business intelligence

15. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 15 Federated Ontology Layers Allow for Advanced Data Modeling

16. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 16 Putting It All Together Into A Platform Unstructured Outcomes Data Structured Data Customizable User Interfaces Ontology Engine

17. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 17 BIG DATA – NOW THAT YOU HAVE SEMANTICS, HOW TO SCALE…

18. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 18 The Problem of Big Data is Real (And Closing In)  The past couple of decades have been spent on data gathering and storage  Most Data Stores were not built to get data out  The new push is connecting data  New high-performance systems are required to meet those needs Data solutions must be big, smart, and easy to deploy

19. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 19 Big Data Analytics Challenge for Intelligent Systems  Data Analysis in the many spaces requires near-real-time decision support tools.  Connecting the dots is paramount to successful and effective analysis  This requires a culmination of new techniques that combine robust data modeling and linkage (e.g., graphs) with high-performance computing capability

20. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 20 Capturing Complex Data Is Difficult  People are now attempting to utilize their data like never before. Semantics has shown significant promise but has not scaled well in the past. Entities, attributes, locations, temporal signatures, etc. result in data explosions  Breakthroughs in cloud computing and high performance graph stores are providing new ways to innovate data science.  Multiple users can now apply perspectives  Can be driven to an entire enterprise Built on Standards- based Approaches

21. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 21 Scaling semantics  Semantics has not scaled well in the past  Entities, inferred data, facets, over time, with quality attribution,… = a data explosion  Our newest breakthroughs in cloud computing and high performance graph stores allow semantics- at-scale BIG GRAPHS + +

22. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 22 Scaling semantics (cont.)  Enterprise-level graph computing requires cutting edge technology components  Data Ingest at Cloud scale – must be able to ingest millions of entities and thousands of documents per second. (Modus Operandi Wave Engine)  Data Storage (Triple Stores and Cloud DBs) 60 billion triples, sub-second queries, thousands of unstructured docs processed per second  Data Traversal (High-performance UI’s) – app stores and BI tools to provide a diverse user experience High- performance triple store Semantic Reasoner Graph- based Appliances

23. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 23 Avoiding the Hype Cycle

24. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 24 EASY-TO-USE DATA

25. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 25 EASY SMART BIG Easy UI’s Leverage Common Models Our user Interfaces are designed around common use models HDFS / Hadoop / MapReduce Accumulo Key Value Store Semantic search Geospatial views Semantic Wiki – collaborate Timelines Explore Visualizations Large-scale Semantic triple stores with reasoning

26. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 26 Driving the Knowledge to Multiple Users  Combining software tools in innovative ways allows for multiple users to view the same data at once. These technologies are providing new platforms that are driving new ways to utilize advanced analytics like never before  Information can be driven to multiple users in near-real- time for improved decision support End Users

27. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 27 Visualizing patterns  Correlations, associations and patterns require special purpose visualizations  Our ExtJS/Ozone framework enables fast assembly of point solutions  Patterns recognition leads to prediction  Prediction leads to prevention

28. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 28 Big Data Results in a Highly Intuitive UIs is Key  Complex data does not require complex UIs Many new tech innovations involve simple, intuitive front ends (apps)  Users must be able to quickly manipulate information  Must be able to quickly derive answers  Different technologies must be integrated into a common look and feel

29. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 29 Providing an End-to-End Solution  Many companies our size provide a capability or two Modus Operandi provides a complete platform for a multitude of user applications (and growing).  Information can be ingested from nearly any source (structured, semi-structured or unstructured). Common models such as UMLS, Ucore-SL, BFO, etc. Custom models can be created based on project specifics.  Information is stored in a high-performance graph knowledge base (we can integrate numerous ones – currently using Bigdata, Rya and Allegrograph).  Results can be driven to a wide variety of easy-to-use UI’s that can be highly customized to fit user needs.  Smart + Big + Easy provides a new means to successfully apply semantic technologies to large scale graph computing.

30. Jan. 22, 2013 | © 2013 Modus Operandi, Inc. | 30 THANK YOU QUESTIONS?