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http:// http:// ebiquity.umbc.edu/ ebiquity.umbc.edu/
http://http://ebiquity.umbc.edu/ebiquity.umbc.edu/
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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, Building intelligent systems in open, heterogeneous, dynamic, distributed heterogeneous, dynamic, distributed environments”environments”
•Current research includes mobile and pervasive computing, security/trust/privacy, semantic web, multiagent systems, advanced databases, and high performance computing
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What is UMBC• The University of Maryland
Baltimore County • One of the three research campuses
in 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
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IT @ UMBC• Information Technology has UMBC’s
largest 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, …
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CSEE @ UMBC• Computer Science and Electrical
Engineering• UMBC’s largest Department with
48 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, …
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http://ebiquity.umbc.edu/
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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)
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Ebiquity Research Space
IntelligentIntelligentInformationInformation
SystemsSystems
NetworkingNetworking& Systems& Systems SecuritySecurity
AIAI DBDBsemanticsemantic
webweb
mobilitymobility
pervasivepervasivecomputingcomputing
trusttrustprivacyprivacy
assuranceassurance
web services/SOCweb services/SOC
userusermodelingmodeling
wirelesswireless
data mining
machinemachinelearninglearning
knowledgeknowledgemanagementmanagement
KRKR
intrusionintrusiondetectiondetection
contextcontextawarenessawareness
policiespolicies
IRIR
wearable computingwearable computing
DRMDRM
HPCCHPCC
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Ebiquity Research Space
IntelligentIntelligentInformationInformation
SystemsSystems
NetworkingNetworking& Systems& Systems SecuritySecurity
AIAI DBDBsemanticsemantic
webweb
mobilitymobilitypervasivepervasivecomputingcomputing trusttrustprivacyprivacy
assuranceassurance
web servicesweb services
userusermodelingmodeling
wirelesswireless
data mining
service oriented computing
HCIHCI
machinemachinelearninglearning
languagelanguagetechnologytechnology
knowledgeknowledgemanagementmanagement
KRKR
intrusionintrusiondetectiondetection
contextcontextawarenessawareness
policiespolicies
planningplanning roboticsrobotics
IRIR
wearable computingwearable computing
Building Building intelligent intelligent systems insystems in
open, open, heterogeneous,heterogeneous,
dynamic, dynamic, distributed distributed
environmentsenvironments
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Some Current and Recent Projects
Pervasive and mobile computing(1) Trauma Pod(2) Context aware pervasive computing(3) Mogatu: Tivo for mobile computing(4) Service Discovery & CompositionSemantic Web(5) Agents and the Semantic Web(6) Swoogle and SpireSecurity and trust(7) Rei(8) Semdis(9) Securing ad hoc networks(10) SWANS: Secure and Adaptive WSNs
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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
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(1) Trauma Pod
2005: da Vinci Surgical Robot 2005: da Vinci Surgical Robot 2020: Automated Trauma Pod treats wounded 2020: Automated Trauma Pod treats wounded soldiers on the battlefield. soldiers on the battlefield.
•A DARPA-sponsored project to enable a future generation of unmanned A DARPA-sponsored project to enable a future generation of unmanned medical systems to save lives on the battlefieldmedical systems to save lives on the battlefield
•A Trauma Pod will have no human medical personnel on-site to conduct the 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 surgery and will be small enough to be carried by a medical ground or air vehicle. vehicle.
•A human surgeon will conduct procedures from a remote location using A human surgeon will conduct procedures from a remote location using teleoperated surgical manipulators with support from automated robotic teleoperated surgical manipulators with support from automated robotic systemssystems
•Phase 1 will perform an unmanned surgical procedure within a hospital OR Phase 1 will perform an unmanned surgical procedure within a hospital OR space.space.
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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
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Motivation: Moving from this…
Source: UbiComp 2003
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Pervasive environments for the Military
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A Bird’s Eye View of CoBrA
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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
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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…
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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
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Semantic Web "The Semantic Web is anextension 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
04/22/2304/22/23 Filip PerichFilip Perich2323
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.
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.
http://swoogle.umbc.edu/
Ontologydiscovery
Webinterface
DB SWDcrawler WeWe
bb
OntologyAnalyzer
OntologyAgentsOntology
AgentsOntologyAgentsOntology
Agents Ontologydiscovery Google
Apache/Tomcat
php, myAdmin
mySQL
Jena JenaIR
engine
SIRE
Webservices
Agentservices
cachedfiles
FocusedCrawler
APIs
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.
SWD RankA SWD’s rank is a function of its type (SWO/SWI) and the rank and types of the documents to which it’s related.
SWD Properties
SWOs
SWIs
HTMLdocuments
Images
CGI scripts
Audiofiles
Videofiles
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)
SWD = SWO + SWI
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 triplesTernary: R(D1,D2,D3) • MP3: D1 maps a term from D2 to D3 using owl:sameClass, etc.
SWD Relations
Language and level; encoding, number of triples, defined classes, defined properties, & defined individuals; type (SWO, SWI); form (RSS, FOAF, P3P, …); rank; weight; annotations; …
Swoogle puts documents into a character n-gram based IR engine to compute document similarity and do retrieval from queries
Swoogle has metadata on classes, properties and individuals from ~240,000 SWDs SWD IR Engine
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Agents and the Semantic Web
http://taga.umbc.edu/
TechnologiesFIPA (JADE, April Agent Platform)Semantic Web (RDF, OWL)Web (SOAP,WSDL,DAML-S)Internet (Java Web Start )
FeaturesOpen Market FrameworkAuction ServicesOWL message contentOWL OntologiesGlobal Agent Community
MotivationMarket dynamicsAuction theory (TAC)Semantic webAgent collaboration (FIPA & Agentcities)
Travel Agents
Auction Service Agent
Customer Agent
Bulletin BoardAgent
Market Oversight Agent
Request
Direct Buy
Report Direct Buy Transactions
BidBid
CFP
Report Auction Transactions
Report Travel Package
Report Contract
ProposalWeb Service
Agents
Ontologieshttp://taga.umbc.edu/ontologies/ travel.owl – travel concepts fipaowl.owl – FIPA content lang. auction.owl – auction services tagaql.owl – query language
FIPA platform infrastructure services, including directory facilitators enhanced to use OWL-S for service discovery
Owl for representation and reasoning
Owl for service
descriptions
Owl for negotiatio
n
Owl as a content languag
e
Owl for publishing
communicative acts
Owl for contract
enforcement
Owl for modeling trust
Owl for authorization policies
Owl for protocol
description
04/22/2304/22/23 Filip PerichFilip Perich2525Research support was provided by NSF, award NSF-ITR-IIS-0326460, PI Tim Finin, UMBC.
(5)(5) SPIRESPIRE Semantic Prototypes in Research EcoinfomaticsSemantic Prototypes in Research Ecoinfomatics
ApproachWe 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.
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.• Ontologies for 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.
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.
Research TeamUMBC ebiquity (Finin) UC Davis ICE (Quinn)UMBC GEST Center (Sachs) RMBL PEaCE (Martinez)UMD MINDSWAP (Hendler) NASA GSFC (Schnase)
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.
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.
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.
Invasive species do more economic damage to the U.S. every year that all other natural disasters combined. Above: plants, animals, and a virus.
An ontology (found via Swoogle) is loaded into Photostuff to mark up regions of a field photograph.
The NBII California Information Node (CAIN), maintained by UC Davis, is a jumping off point to broader NBII deployment.
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.
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.
UMBCUMBCAN HONORS UNIVERSITY IN MARYLAND
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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.
04/22/2304/22/23 Filip PerichFilip Perich2727
Knowledge DiscoveryKnowledge Discoveryin the Semantic Webin the Semantic Web SEMDIS NSF award ITR-IIS-0325464
U. Georgia, Sheth, Arpinar, Kochut, Miller NSF award ITR-IIS-0325172 UMBC, Joshi, Yesha, Finin
June 2004
ObjectiveDesign, 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.
ApproachKnowledge 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 impactsTechniques and prototypes developed can be applied to a range of problems, including discovering new connections and relations in scientific information and homeland security.
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.
An experimental algorithm has been developed to integrate and rank discovered relationships.
Reference foaf:Agent
rdf:Statementselects
Justification TrustBelief
Association
contains
foaf:Document
rdf:Resourcefoaf:page
DocumentRelation
xsd:real [0,1]AssociationConnectiveconfidence
connective
source
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.
http://lsdis.cs.uga.edu/Projects/SemDis http://semdis.umbc.edu/
A. Joshi
L. Ding
H. Chen
P. Kolari
F. Perich
Y. Yesha
J. Golbeck
J. Hendler
Kagal
sink
hub
source
island
Finin A. Joshi
Ding
Chen
Kagal
Perich
Golbeck’s Trust Network
DBLP Network
FOAF Network
A. Sheth
M. P. Singh
Y. Peng
6
15
1
28
T. Finin
mapTo
knows
knows
knows
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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
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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…
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Securing Ad-Hoc Networks
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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
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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
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SWANS: Secure and Adaptive WSN
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http://ebiquity.umbc.edu/
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http://ebiquity.umbc.edu/