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ebiquity.umbc/

http://ebiquity.umbc.edu/. UMBC and Ebiquity. UMBC is a research extensive University with a a major focus on Information Technology Ebiquity is a large and active research group with the goal of “ Building intelligent systems in open, heterogeneous, dynamic, distributed environments”

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ebiquity.umbc/

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  1. http://ebiquity.umbc.edu/

  2. UMBC and Ebiquity • UMBC is a research extensive University with a a major focus on Information Technology • Ebiquity is a large and active research group with the goal of “Building intelligent systems in open, heterogeneous, dynamic, distributed environments” • Current research includes mobile and pervasive computing, security/trust/privacy, semantic web, multiagent systems, advanced databases, and high performance computing

  3. What is UMBC • The University of MarylandBaltimore County • One of the three research campusesin the University of Maryland System • Ranked in top tier of nation's research universities--Doctoral/Research Universities-Extensive -- by the Carnegie Foundation • Has 500 full time and 335 part time faculty, 10K undergraduate and 2K graduate students • Located in suburban Baltimore County, between Baltimore and Washington DC. • Special focus on science, engineering, information technology and public policy with ~$80M in external research funding in 2003

  4. IT @ UMBC • Information Technology has UMBC’slargest concentration of faculty & students • Over 100 faculty and more than 2500 students • College of Engineering and Information Technology • Degree programs (graduate and undergraduate) • Computer Science, Computer Engineering, Information Systems, Electrical Engineering, Digital Imaging, and (soon) Systems Engineering • Certificate and training programs (degree and non-degree) • Electronic Government, Information Security, Web Development, Systems Administration, Oracle, CISCO, … • Many institutes and centers • Center for Women and Information Technology, Center for Information Security and Assurance, Bioinformatics Research Center, Center for Photonics, …

  5. CSEE @ UMBC • Computer Science and ElectricalEngineering • UMBC’s largest Department with48 faculty, ~1300 undergrads, ~300 grad students • Degree programs (graduate and undergraduate) • Computer Science, Computer Engineering, Electrical Engineering • Many institutes, centers and labs • Institute for Language and Information Technology, Center for Information Security and Assurance, Center for Photonics, Lab For Advanced Information Technology, VLSI Lab, CADIP, … • Breadth and focus in research areas • ~ $6M/year in sponsored research from Government and Industry • Areas include pervasive computing, AI, security, information retrieval, graphics, databases, VLSI, …

  6. http://ebiquity.umbc.edu/

  7. People and funding • Faculty: Finin, Yesha, Joshi • Colleagues: Peng, Halem, Pinkston, Segall, … • Students: ~10 PhD, ~10 MS, ~5 undergrad • Funding • Current: DARPA (DAML, traumaPod), NSF (two ITRs, Cybertrust, NSG, …), Intelligence community, NASA, NIST, Industry (IBM, Fujitsu, …) • Recent: DARPA (CoABS, GENOA II), NSF (CAREER)

  8. Ebiquity Research Space KR usermodeling semanticweb data mining machine learning AI DB IntelligentInformationSystems web services/SOC knowledgemanagement IR wearable computing policies HPCC mobility Networking& Systems wireless assurance Security contextawareness trust DRM pervasivecomputing intrusiondetection privacy

  9. Ebiquity Research Space languagetechnology robotics HCI planning KR Building intelligent systems in open, heterogeneous,dynamic, distributed environments usermodeling semanticweb data mining machine learning AI DB IntelligentInformationSystems knowledgemanagement web services IR service oriented computing wearable computing policies Networking& Systems wireless Security mobility assurance contextawareness pervasivecomputing intrusiondetection privacy trust

  10. Some Current and Recent Projects Pervasive and mobile computing • Trauma Pod • Context aware pervasive computing • Mogatu: Tivo for mobile computing • Service Discovery & Composition Semantic Web (5) Agents and the Semantic Web (6) Swoogle and Spire Security and trust (7) Rei (8) Semdis (9) Securing ad hoc networks (10) SWANS: Secure and Adaptive WSNs

  11. Pervasive Computing “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it ” – Mark Weiser Think: writing, central heating, electric lighting, water services, … Not: taking your laptop to the beach, or immersing yourself into a virtual reality

  12. (1) Trauma Pod • A DARPA-sponsored project to enable a future generation of unmanned medical systems to save lives on the battlefield • A Trauma Pod will have no human medical personnel on-site to conduct the surgery and will be small enough to be carried by a medical ground or air vehicle. • A human surgeon will conduct procedures from a remote location using teleoperated surgical manipulators with support from automated robotic systems • Phase 1 will perform an unmanned surgical procedure within a hospital OR space. 2020: Automated Trauma Pod treats wounded soldiers on the battlefield. 2005: da Vinci Surgical Robot

  13. UMBC’s role in Trauma Pod • Our role focuses on using RFID technology to track the location and use of medical tools and supplies in the OR • And to integrate this information with • Legacy supply chain systems • Hospital and patient records

  14. Motivation: Moving from this… Source: UbiComp 2003

  15. Pervasive environments for the Military

  16. A Bird’s Eye View of CoBrA

  17. MoGATU: TIVO for Mobile Computing A mobile computing vision and a problem • Devices “broadcast” information and service descriptions via short-range RF (802.11, Bluetooth, UWB, etc.) • As people and their devices move, they can access this data, but only while it’s in range • The data may be out of range when it’s needed • Devices must anticipate their information need so they can cache data when it’s available • Based on user model, preferences, schedule, context, trust, … • Compute a dynamic utility function to create a “semantic” cache replacement algorithm

  18. MoGATU’s distributed belief model • MoGATU is a data management module for MANETs • Devices send queries to peers • Ask its vicinity for reputation of untrusted peers that responded -- trust a device if trusted before or if enough trusted peers trust it • Use answers from (recommended to be) trusted peers to determine answer • Update reputation/trust level for all responding devices • Trust level increases for devices giving what becomes final answer • Trust level decreases for devices giving “wrong” answer • Each devices builds a ring of trust…

  19. Service Discovery and Composition • Develop a peer-to-peer caching based distributed service discovery mechanism • Integrated with routing layer for better performance • Uses semantic service descriptions • Caching of “neighboring services” • Selective forwarding of requests • Broker-based Service Composition • Dynamic Broker selection based mechanism • Distributed Broker-based mechanism • Utilizes the peer-to-peer service discovery layer • Source-monitored fault-tolerance

  20. Semantic Web "The Semantic Web is an extension of the current webin which information is givenwell-defined meaning, betterenabling computers andpeople to work in cooperation." -- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001

  21. CGI scripts SWOs Videofiles HTML documents Audiofiles SWIs Images APIs Webservices Agentservices SWD = SWO + SWI Swoogle is a crawler based search & retrieval system for semantic web documents (SWDs) in RDF, Owl and DAML. It discovers SWDs and computes their metadata and relations, and stores them in an IR system. Apache/Tomcatphp, myAdmin Web interface FocusedCrawler The web, like Gaul, is divided into three parts: the regular web (e.g. HTML), Seman- tic Web Ontologies (SWOs), and Semantic Web Instance files (SWIs) Web mySQL Ontology Analyzer SWD crawler SWD Properties DB Language and level; encoding, number of triples, defined classes, defined properties, & defined individuals; type (SWO, SWI); form (RSS, FOAF, P3P, …); rank; weight; annotations; … Jena Jena IRengine OntologyAgents OntologyAgents OntologyAgents OntologyAgents Ontology discovery Ontology discovery Google SIRE cached files SWD Relations SWD Rank Swoogle uses two kinds of crawlers to discover semantic web documents and several analysis agents to compute metadata and relations among documents and ontologies. Metadata is stored in a relational DBMS. A SWD’s rank is a function of its type (SWO/SWI) and the rank and types of the documents to which it’s related. • Binary: R(D1,D2) • IM: D1 owl:imports D2 • IMstar: transitive closure of IM • EX: D1 extends D2 by defining classes or properties subsumed by D2’s • PV: owl:priorVersion & subproperties • TM: D1 uses terms from D2 • IN: D1 uses individual defined in D2 • MAP: D1 maps some of its terms to D2’s • SIM: D1 & D2 are similar • EQ: D1 & D2 are identical • EQV: D1 & D2 have the same triples • Ternary: R(D1,D2,D3) • MP3: D1 maps a term from D2 to D3 using owl:sameClass, etc. http://swoogle.umbc.edu/ Swoogle has metadata on classes, properties and individuals from ~240,000 SWDs SWD IR Engine Swoogle puts documents into a character n-gram based IR engine to compute document similarity and do retrieval from queries Filip Perich Contributors include Tim Finin, Anupam Joshi, Yun Peng, R. Scott Cost, Jim Mayfield, Joel Sachs, Pavan Reddivari, Vishal Doshi, Rong Pan, Li Ding, and Drew Ogle. Partial research support was provided by DARPA contract F30602-00-0591 and by NSF by awards NSF-ITR-IIS-0326460 and NSF-ITR-IDM-0219649. 20 May 2004.

  22. Report Direct Buy Transactions Report Contract Report Auction Transactions Market Oversight Agent Request CFP Report Travel Package Bid Bid Bulletin Board Agent Auction Service Agent Customer Agent Proposal Direct Buy Travel Agents Web Service Agents Agents and the Semantic Web Owl for protocol description Owl for contract enforcement Motivation • Market dynamics • Auction theory (TAC) • Semantic web • Agent collaboration (FIPA & Agentcities) Features • Open Market Framework • Auction Services • OWL message content • OWL Ontologies • Global Agent Community Technologies • FIPA (JADE, April Agent Platform) • Semantic Web (RDF, OWL) • Web (SOAP,WSDL,DAML-S) • Internet (Java Web Start ) Ontologieshttp://taga.umbc.edu/ontologies/ • travel.owl – travel concepts • fipaowl.owl – FIPA content lang. • auction.owl – auction services • tagaql.owl – query language Owl for representation and reasoning Owl for publishing communicative acts Owl for modeling trust Owl for negotiation Owl as a content language FIPA platform infrastructure services, including directory facilitators enhanced to use OWL-S for service discovery Owl for authorization policies Owl for service descriptions http://taga.umbc.edu/

  23. Approach We are building prototype tools and applications that demonstrate how semantic web technology supports infor-mation discovery, integration and sharing in scientific com-munities.  The National Biological Information Infrastructure (NBII) and Invasive Species Forecasting System (ISFS) pro-vide requirements and serve as testbeds for our prototypes. Invasive species do more economic damage to the U.S. every year that all other natural disasters combined. Above: plants, animals, and a virus. (5)SPIRE Semantic Prototypes in Research Ecoinfomatics • Significant Results • SWOOGLE - a search engine for the semantic web. • MoaM(Meal of a Meal) - Given a species list, infer a food web. • Photostuff - annotate regions of a picture with OWL. • SWOOP- the first ontology editor written specifically for OWL. • Ontologiesfor ecological interaction, and observation data. • Food web visualization and analysis tools that are driven by OWL ontologies and instance data. • CRISIS CAT - an RDF based catalog of Invasive Species resources in California. • Coordination with USGS, NASA, EPA, GBIF, and the Intergovernmental, Interagency Cooperation on Ecoinformatics. Spire is a distributed, interdisciplinary research project exploring how semantic web technology supports information discov-ery, integration, and sharing in scientific communities. We are building prototype tools and applications for inclusion in the National Biological Information Infrastructure (NBII), with a focus on the early detection and warning of invasive species. Meal of a Meal (after Friend of a Friend). We know Fish 1 eats Plant 1. We then infer that Fish 1 may also eat the taxonomic siblings of Plant 1: Plants 2 and 3. Similarly, we infer that the taxonomic siblings of Fish 1 - Fishes 2 and 3 - may eat Plant 1. Swoogle is a crawler based search and retrieval system for semantic web doc-uments (SWDs) in RDF and OWL. It discovers SWDs and computes their metadata and relations, and stores them in an IR system. Users can search for ontologies or instance data, and hits are ranked according to our Ontology Rank algorithm. The RMBL team expresses food webs in OWL using an ontology for eco-logical interaction they have constructed in coordination with other ecolo-gists. The OWL model drives the simulation and visualization. • Broader Impacts • Enable knowledge from one community to be effectively used by another. • Harness the power of the citizen scientist. (The majority of invasives are discovered by amateurs.) • Integrate research and education in the classroom. • Coming Soon • ELVIS – an end to end application that starts with a location and produces a model of its food web. • The Pond Project - a junior high school classroom activity to monitor the health of local ecosystems. • Enhanced tools. Spatial distribution of exotic plants at the Cerro Grande fire site. The statistical techniques used to generate these maps do not take trophic data as input. Yet. An ontology (found via Swoogle) is loaded into Photostuff to mark up regions of a field photograph. Research Team UMBC ebiquity (Finin) UC Davis ICE (Quinn) UMBC GEST Center (Sachs) RMBL PEaCE (Martinez) UMD MINDSWAP (Hendler) NASA GSFC (Schnase) Filip Perich The NBII California Information Node (CAIN), maintained by UC Davis, is a jumping off point to broader NBII deployment. UMBC Research support was provided by NSF, award NSF-ITR-IIS-0326460, PI Tim Finin, UMBC. AN HONORS UNIVERSITY IN MARYLAND

  24. Security and Trust in Open Environments • Many new information systems are open, heterogeneous and dynamic • Examples: the web, web services, P2P systems, Grid computing, pervasive computing, MANETs, etc. • Providing security and privacy in such systems is challenging • We can not rely on traditional authentication-based schemes • Recognizing “bad actors” in such systems is hard • We are exploring new approaches using computational policies, trust and reputation.

  25. xsd:real [0,1] AssociationConnective confidence connective Association FOAF Network Y. Yesha island Kagal source J. Golbeck knows L. Ding H. Chen J. Hendler Justification Trust knows DocumentRelation P. Kolari Belief knows F. Perich T. Finin A. Joshi Golbeck’s Trust Network hub sink foaf:Document Reference foaf:Agent mapTo Ding Y. Peng 1 6 28 A. Sheth Kagal Finin A. Joshi 1 5 selects Chen M. P. Singh rdf:Resource rdf:Statement contains foaf:page An experimental algorithm has been developed to integrate and rank discovered relationships. Perich DBLP Network source SWETO is large ontology covering several test-bed domains. It is pop-ulated with 800K instances and 1.M relations extracted from heterogeneous Web sources. SWETO was developed using Semagix Freedom system. SEMDIS NSF award ITR-IIS-0325464 U. Georgia, Sheth, Arpinar, Kochut, Miller NSF award ITR-IIS-0325172 UMBC, Joshi, Yesha, Finin Objective Design, prototype and evaluate a system supporting the discovery, indexing and querying of complex semantic relationships in the Semantic Web. The system maintains and utilizes trust and provenance information to enhance the relationship discovery. Approach Knowledge representation systems reason over sem-antic web content discovered on the web which is re-duced to triples that can be efficiently stored and pro-cessed in relational databases. Trust models and heuristics guide the formation of conclusions Broader impacts Techniques and prototypes developed can be applied to a range of problems, including discovering new connections and relations in scientific information and homeland security. Knowledge Discoveryin the Semantic Web A “web of belief” model and associated ontology is used to represent, integrate, and evaluate conclusions drawn from the large volume of heterogeneous assertions found in the data. Filip Perich http://semdis.umbc.edu/ http://lsdis.cs.uga.edu/Projects/SemDis June 2004

  26. Rei Policy Language • Developed several versions of Rei, a policy specification language, encoded in (1) Prolog, (2) RDFS, (3) OWL • Used to model different kinds of policies • Authorization for services • Privacy in pervasive computing and the web • Conversations between agents • Team formation, collaboration & maintenance • The OWL grounding enables policies that reason over SW descriptions of actions, agents, targets and context

  27. Applications – past, present & future • Coordinating access in supply chain management system • Authorization policies in a pervasive computing environment • Policies for team formation, collaboration, information flow in multi-agent systems • Security in semantic web services • Privacy and trust on the Internet • Privacy in pervasive computing environments 1999 2002 2003… 2004…

  28. Securing Ad-Hoc Networks

  29. Monitoring and Response • Active Response Framework • Nodes Snoop Locally • Send Signed Accusations to Other Nodes • Each Node Makes Decision Locally based on Policy • Accusations can be Corroborated and lead to increase in reputation • False Accusations Can Be Flagged and lead to loss of reputation (or even sanctions) • Nodes Can Choose Not To Communicate Through Suspected Nodes

  30. SWANS: Secure and Adaptive WSNs • A holistic policy driven approach to designing secure and adaptive wireless sensor networks • Secure self-organization • Centralized and distributed protocols • State determination • Parameters to define “raw” state • Node-level logical construct to identify complete state • Network-level logical construct to help identify global state • A set of policies to adapt to changes in state

  31. SWANS: Secure and Adaptive WSN

  32. http://ebiquity.umbc.edu/

  33. http://ebiquity.umbc.edu/

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