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
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3. 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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4. 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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5. 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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6.
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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)
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8. Ebiquity Research SpaceKR
user modeling
semantic web
data
mining
machine
learning AI DB
Intelligent Information Systems
web services/SOC
knowledge management IR
wearable computing
policies
HPCC
mobility
Networking & Systems
wireless
assurance
Security
context awareness
trust
DRM
pervasive computing
intrusion detection
privacy
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9. Ebiquity Research Space
language technology
robotics
HCI
planning KR
Building intelligent systems in
open, heterogeneous, dynamic, distributed environments
user modeling
semantic web
data
mining
machine
learning AI DB
Intelligent Information Systems
knowledge management
web services IR
service oriented computing
wearable computing
policies
Networking & Systems
wireless
Security
mobility
assurance
context awareness
pervasive computing
intrusion detection
privacy
trust
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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
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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
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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
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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
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14. Motivation: Moving from this…
Source: UbiComp 2003
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15. 4/23/2019

16. 4/23/2019

17. Pervasive environments for the Military
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18. A Bird’s Eye View of CoBrA
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19. 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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20. 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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21. 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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22. Semantic Web "The Semantic Web is an
extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."
-- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001
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23. Apache/ Tomcat php, myAdmin
SWOs
SWIs
HTML
documents
Images
CGI scripts
Audio files
Video files
APIs
Web services
Agent services
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.
Web
interface
Apache/ Tomcat php, myAdmin
Focused Crawler
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
SWD Properties
Ontology
Analyzer
mySQL DB
SWD
crawler
Language and level; encoding, number of triples, defined classes, defined properties, & defined individuals; type (SWO, SWI); form (RSS, FOAF, P3P, …); rank; weight; annotations; …
Jena
IR engine
Jena
Ontology Agents
Ontology Agents
Ontology Agents
Ontology Agents
Ontology
discovery
SIRE
Ontology
discovery
Google
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.
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.
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.
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
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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 F and by NSF by awards NSF-ITR-IIS and NSF-ITR-IDM May 2004.

24. 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 )
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
Travel Agents
Auction Service
Agent
Customer
Bulletin Board
Market Oversight
Request
Direct Buy
Report Direct Buy Transactions
Bid
CFP
Report Auction Transactions
Report Travel Package
Report Contract
Proposal
Web Service
Agents
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
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25. AN HONORS UNIVERSITY IN MARYLAND
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.
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.
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)
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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 , PI Tim Finin, UMBC.
AN HONORS UNIVERSITY IN MARYLAND

26. 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.
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27. Knowledge Discovery in the Semantic Web
SEMDIS
Knowledge Discovery in the Semantic Web
NSF award ITR-IIS
U. Georgia, Sheth, Arpinar, Kochut, Miller
NSF award ITR-IIS
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.
Reference
foaf:Agent
rdf:Statement
selects
Justification
Trust
Belief
Association
contains
foaf:Document
rdf:Resource
foaf:page
DocumentRelation
xsd:real [0,1]
AssociationConnective
confidence
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.
An experimental algorithm has been developed to integrate and rank discovered relationships.
A. Joshi
L. Ding
H. Chen
P. Kolari
F. Perich
Y. Yesha
J. Golbeck
J. Hendler
Kagal
sink
hub
source
island
Finin
Ding
Chen
Perich
Golbeck’s
Trust Network
DBLP Network
FOAF Network
A. Sheth
M. P. Singh
Y. Peng 6 1 5 28
T. Finin
mapTo
knows
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.
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Filip Perich
June 2004

28. 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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29. 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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30. Securing Ad-Hoc Networks
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31. 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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32. 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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33. SWANS: Secure and Adaptive WSN
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34.
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35.
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