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Title: Faculty Research Areas LabsCenters Meetings


1
Faculty Research AreasLabs/CentersMeetings
2
Areas
  • Artificial Intelligence
  • Bio-Informatics
  • Databases
  • Graphics, Image Processing and Multimedia
  • Networks
  • Pervasive Computing
  • Software Engineering
  • Systems and Architecture
  • Security

3
Artificial Intelligence
  • Manfred Huber
  • Farhad Kamangar

4
Manfred Huber
  • Research Projects
  • Personal Service Robots
  • Hierarchical Skill Acquisition
  • CONNECT - Information Technologies
  • for the Disabled
  • Contact
  • huber_at_uta.edu (GACB114)

5
Farhad Kamangar
  • Research Projects
  • Computer Vision
  • Neural Networks
  • Robotics
  • CONNECT - Information Technologies
  • for the Disabled
  • Contact
  • kamangar_at_uta.edu (GACB 112)

6
Bio-Informatics
Dr. Jean Gao 338 Nedderman Hall Phone (817)
272-3628 E-mail gao_at_cse.uta.edu URL
http//crystal.uta.edu/gao
  • Dr. Nikola Stojanovic
  • 301 Nedderman Hall
  • Phone (817) 272-7627
  • E-mail nick_at_cse.uta.edu
  • URL http//ranger.uta.edu/nick

7
http//www.washbac.org/images/farside.gif
8
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?

9
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?

it will probably happen exactly that way
10
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?
  • Modern high-throughput technologies are
    generating tremendous volume of data - somebody
    needs to store and manipulate the data, generate
    reports and share them with the scientific
    community.

it will probably happen exactly that way
11
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?
  • Modern high-throughput technologies are
    generating tremendous volume of data - somebody
    needs to store and manipulate the data, generate
    reports and share them with the scientific
    community.

it will probably happen exactly that way
12
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?
  • Modern high-throughput technologies are
    generating tremendous volume of data - somebody
    needs to store and manipulate the data, generate
    reports and share them with the scientific
    community.
  • Can we turn that data into information, and
    eventually knowledge?

it will probably happen exactly that way
13
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?
  • Modern high-throughput technologies are
    generating tremendous volume of data - somebody
    needs to store and manipulate the data, generate
    reports and share them with the scientific
    community.
  • Can we turn that data into information, and
    eventually knowledge?

it will probably happen exactly that way
14
http//bioinformatics.ubc.ca/about/what_is_bioinfo
rmatics/
15
http//bioinformatics.ubc.ca/about/what_is_bioinfo
rmatics/
16
http//bioinformatics.ubc.ca/about/what_is_bioinfo
rmatics/
17
(No Transcript)
18
Biotechnology and pharmaceutical industry
  • Biotechnology and pharmaceutical industry
    revenues are estimated at hundreds of billions of
    dollars annually.
  • The industry's claim is that they spend 800
    million on research development for every new
    drug which receives FDA approval.
  • Much of the RD efforts are pursued
    computationally these days.

19
Biotechnology and pharmaceutical industry
  • Biotechnology and pharmaceutical industry
    revenues are estimated at hundreds of billions of
    dollars annually.
  • The industry's claim is that they spend 800
    million on research development for every new
    drug which receives FDA approval.
  • Much of the RD efforts are pursued
    computationally these days.
  • This is a large and growing industry - whether in
    RD or just software support, you may see
    yourself working for one of these companies in a
    few years.

20
http//bioinformatics.uta.edu
21
Bioinformatics lab projects
  • Motif discovery in DNA sequences.
  • Identification and characterization of mobile
    elements in DNA.
  • Studying structure and conservation patterns in
    genomic sequences.
  • Characterization of chromosomal recombination
    patterns.
  • Studying human genetic variation and its relation
    to disease susceptibility.

22
Bioinformatics lab projects
  • Motif discovery in DNA sequences.
  • Identification and characterization of mobile
    elements in DNA.
  • Studying structure and conservation patterns in
    genomic sequences.
  • Characterization of chromosomal recombination
    patterns.
  • Studying human genetic variation and its relation
    to disease susceptibility.

Research funded by the National Institutes of
Health, and preformed in collaboration with UTA
Biology Department and the University of Texas
Southwestern Medical Center in Dallas.
23
UT Arlington, October 17-18, 2008
http//www.biotconf.org
24
Databases
  • Sharma Chakravarthy
  • Ramez Elmasri
  • Leonidas Fegaras
  • Gautham Das
  • Chengkai Li

25
Information Technology LaboratoryProf. Sharma
ChakravarthyEmail sharma_at_cse.uta.edu, URL
http//itlab.uta.edu/sharma
Funding Sources NSF, Spawar, Rome Lab, ONR,
DARPA, TI, MCC
  • Select Projects
  • InfoMosaic (information integration from
    heterogeneous sources)
  • MavEStream (Event and Stream Processing)
  • Active Technology (Push Paradigm, pub/sub,
    event-driven architectures)
  • WebVigiL (General Purpose Change Monitoring for
    the web)
  • Mining Graph, Text, Assoc Rules
  • Prediction of Event Patterns
  • Information Search, Filtering, and
    classification
  • Information Security
  • Mobile Caching
  • Select Publications
  • 1. R. Adaikkalavan and S. Chakravarthy, Event
    Specification and Processing for Advanced
    Applications Generalization and Formalization,
    DEXA Sep 2007
  • A. Telang, R. Mishra, and S. Chakravarthy,
    Ranking Issues for Information Integration,
    DBrank workshop (ICDE 2007), Turkey, 2007.
  • S. Savla and S. Chakravarthy, Efficient Main
    Memory Algorithms for Significant Episode
    Discovery, To appear in the Intl Journal of Data
    warehousing and Mining, 2006.
  • R. Balachandran, S. Padmanabhan, S. Chakravarthy
    Enhanced DB-Subdue Supporting Subtle Aspects of
    Graph Mining Using a Relational approach in
    PAKDD, 2006
  • A. Srinivasan, D. Bhatia, and S. Chakravarthy,
    Discovery of Interesting episodes in Sequence
    Data, in 21st ACM SAC, Data Mining Track, 2006.
  • M. Aery, S. Chakravarthy eMailSift Email
    Classification Based on Structure and Content in
    IEEE ICDM 2005
  • H. Kona, S. Chakravarthy, and A. Arora, SQL-Based
    Approach to Incremental Association Rule Mining,
    in ADBIS Workshop on DMKD, 2005.
  • Q. Jiang, R. Adaikkalavan and S. Chakravarthy,
    NFMi An Inter-domain Network Fault Management
    System. IEEE ICDE, 2005.
  • R. Adaikkalavan, and S. Chakravarthy Active
    Authorization Rules for Enforcing Role-Based
    Access Control and its Extensions, PDM Workshop,
    IEEE ICDE, 2005.
  • L. Elkhalifa, R. Adaikkalavan, and S.
    Chakravarthy, InfoFilter A System for Expressive
    Pattern Specification and Detection Over Text
    Streams, ACM SAC, 2005.
  • .

People PhD Students Mr. Aditya Telang
(Adi) Ms. Roochi Mishra Masters Students Mr.
Mayur Motgi Mr. Supreet Chakravarthy Mr. Aamir
Syed Group Meeting 1 Pm to 2 Pm on
Fridays in NH 232
26
Ramez Elmasri
  • Professor
  • Databases
  • Distributed XML Querying and Caching
  • Object-Oriented Databases
  • Keyword-based XML Query Processing
  • Sensor Networks
  • Energy-Efficient Querying of Sensor Networks
  • Combining RFID and Sensor Networks
  • Indexing of Sensor Networks Data
  • Bioinformatics
  • Modelling Complex Bioinformatics and Biomedical
    Data
  • Mediators for Accessing Heterogeneous Data Sources

27
Leonidas Fegaras
  • Associate Professor
  • (PhD UMass 1993)
  • Areas of interest
  • Databases
  • Web Databases and XML
  • Object-Oriented Databases
  • Query Processing and Optimization
  • Data Management on Peer-to-Peer Systems
  • Programming Languages
  • Functional Programming
  • Program Optimization

28
Research Review Gautam Das
  • Database Exploration
  • Web/Information Retrieval searching techniques in
    databases
  • OLAP, Data Warehouse, Approximate Query
    Processing
  • Data Mining
  • Clustering, Classification, Similarity models,
    Time-Series Analysis
  • Algorithms
  • Graph Algorithms, Computational Geometry
  • More information available at
  • http//ranger.uta.edu/gdas/website/research.htm

29
Graphics Image Proc., Multimedia
  • Ishfaq Ahmad
  • Multimedia Authoring, Compression, Communication
  • Video Processing,
  • Next Generation TV
  • Network Security
  • Parallel Algorithms
  • Dr. Gutemberg Guerra-Filho
  • Computer Vision, Animation, and Humanoid Robotics

30
Prof. Ishfaq Ahmad
  • Dr. Ahmad works closely with federal agencies,
    Arlington police and multimedia industry.
  • Several projects in power-aware video
    compression, multimedia systems, next generation
    TV are being pursued in his lab.

31
High-Performance
  • Ishfaq Ahmad
  • Resources Management in Parallel and Distributed
    Systems
  • Power Management in Data Center and Distributed
    Systems

32
http//www.iris.uta.edu/
Institute for Research in Security (IRIS)
Ishfaq Ahmad
A Multi-disciplinary center focusing on
infrastructure, people, and environmental security
33
Networks
  • Sajal Das
  • Mohan Kumar
  • Gergley Zaruba
  • Hao Che
  • Yonghe Liu

34
Sajal K. Das
Center for Research in Wireless Mobility and
Networking (CReWMaN) Sajal K. Das, Mohan
Kumar Yonghe Liu, Hao Che das_at_cse.uta.edu URL
http//crewman.uta.edu Woolf Hall 411,413, Tel
2-7409 Networking, Mobile Computing and Parallel
Computing Research Group
35
Mohan Kumar Pervasive and Mobile
Computing Sensor Systems
  • Pervasive Computing
  • Middleware
  • Service creation, composition and deployment
  • Prototype development
  • Sensor networks and smart environments
  • Information Fusion in pervasive/sensor
    environments
  • Uniform Information Access in
  • Distributed, mobile and pervasive systems
  • Caching, prefetching, and broadcasting
  • Data management
  • Peer-to-Peer (P2P) Systems
  • Information and service sharing
  • Efficient communication and collaboration
  • Security and privacy
  • Active and Overlay Networking
  • Novel protocols
  • Role in mobile, pervasive and P2P computing

Recommended courses before starting thesis work
CSE5311, CSE5346,CSE5306 and CSE5347/5355 Directe
d Study
36
Gergely Zaruba
  • Research Projects
  • Personal Area Networks
  • Heterogeneous Wireless Networks
  • Architecture, Admission Control and Handoff
  • Optical Networks
  • Optical Burst Switching, Routing, QoS
    Provisioning
  • Traffic Modelling
  • Contact Zaruba_at_uta.edu (GACB 112)

37
Hao Che
  • Embedded hardware/software design for NG network
    processors
  • Traffic engineering
  • Implementation issues and software development
  • MPLS path protection and fast rerouting
  • Routing redundancy
  • Traffic modeling for wireless networks
  • Contact http//crystal.uta.edu/hche/
  • hche_at_uta.edu

38
Yonghe Liu
  • Sensor network and security
  • Prototyping and experimental study
  • Theoretic design and analysis
  • Cross layer optimization
  • Channel dependent performance
  • Software security
  • Design and analysis
  • In need of
  • Strong mathematic skill (probability/signal
    processing/number theory/etc), or
  • Strong programming skill (hardware/software)
  • Contact http//ranger.uta.edu/yonghe/

39
Software Engineering
  • David Kung
  • Yu Lei
  • Dr. Christoph Csallner
  • Arthur Reyes
  • David Levine

40
David Kung
  • Agent-Oriented Software Engineering
  • Testing Object-Oriented Software
  • Expert System for Design Patterns
  • Formal Methods for Quality Assurance
  • Fault Tolerance and Automatic Recovery Using
    Dynamic Class Diversity

Contact http//ranger.uta.edu/kung/kung.html
41
Yu Lei
  • Concurrent and real-time software systems
  • Race analysis, Deterministic Execution
    Environment, Reachability Testing, State
    Exploration-Based Verification
  • Automated software testing
  • Object-Oriented Testing, Component-Based Testing,
    Combinatorial Testing

Contact http//ranger.uta.edu/ylei
42
Arthur Alexander Reyes, Ph.D.
  • Autonomous Vehicles Laboratory
  • Faculty Advisor, along with MAE, IE faculty
  • AUVSI Student UAV Competition
  • 2004 team didnt place
  • 2005 team won 1st Place Overall
  • 2006 team won 3rd Place Overall
  • Teaches
  • CSE 4310/5323 Software Eng. Processes
  • CSE 4321 Software Testing
  • CSE 4392 Game Development (new)
  • http//ranger.uta.edu/reyes/

43
David Levine High Throughput Computational
Science Clusters and Grids
  • David Levine, CSE_at_UTA
  • Projects (Computers applied to)
  • High Energy Physics, Bioinformatics,
  • Medical Informatics, People with
  • Disabilities, Streaming Processing, other..

44
Software EngineeringResearch Center
Check out the lab NH 246
  • Faculty members
  • Dr. Christoph Csallner
  • Dr. Dave Kung
  • Dr. Jeff Lei

45
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46
Software Engineering
  • Software has become pervasive in modern society
  • Directly contributes to quality of life
  • Malfunctions cost billions of dollars every year,
    and have severe consequences in a safe-critical
    environment
  • All about building quality software, especially
    for large-scale development
  • Requirements, design, coding, testing,
    maintenance, configuration, documentation,
    deployment, and etc.

47
THE Best Job in America
What is the 2nd best job?
Go for a PhD in Software Engineering!!
48
Great Impact
49
Quotes from Dr. Parnas
Extracted from his ACM Fellow Profile http//www.s
igsoft.org/SEN/parnas.html
50
Current Research Projects
  • Object-Oriented Software Analysis and Testing
    (Dr. Kung)
  • Software Security Analysis and Testing (with Drs.
    Kung and Liu)
  • Pervasive Context-Aware Computing (with Dr.
    Kumar)
  • Formal Testing and Verification of Concurrent
    Software Systems (with GMU)
  • Automated Combinatorial Testing for Software
    (with National Institute of Standards and
    Technology)
  • Interaction Testing of Web Applications (with
    UMBC)

51
Current Research Projects
  • Hybrid static-dynamic program analyses
  • Automatic test case generators
  • JCrasher, Check n Crash, DSD-Crasher
  • New Testing of database-centric applications
  • OrmCheck with
  • ToDo Support complex languages like UML
  • New Dynamic symbolic invariant detector
  • Pex/DySy with
  • ToDo Scale analysis to large applications
  • ToDo Add static knowledge to dynamic inference

52
(No Transcript)
53
If you want to improve..
..come talk to us
54
Embedded Systems Roger Walker
  • Embedded Systems for Transportation Applications
  • Real-time Multi-core Systems for Embedded
    Applications
  • Stochastic Modeling From Sensor Measurements
  • Development of Special Measurement Systems for
    Transportation Related Applications

Contact http//ranger.uta.edu/walker/
55
Information Security
Donggang Liu Matt Wright
56
One aspect of security
  • Operational Security
  • Classified material can be leaked based on how
    its used or through side effects
  • Dominos Pizza Anyone?
  • Last Wednesday, he adds, "we got a lot of
    orders, starting around midnight. We figured
    something was up." This time the news arrived
    quickly Iraq's surprise invasion of Kuwait.
  • "And Bomb the Anchovies", Time, p. 13, 8/13/90

57
Wireless and System Security Donggang Liu
  • Security in wireless sensor networks
  • key management, security of services such as
    localization, routing, clustering etc.
  • Integrity of wireless embedded devices
  • Code integrity, tamper-resistant techniques
  • Software and system security
  • Security testing, detection of malicious code
  • Contact http//ranger.uta.edu/dliu

58
Network Security and Privacy Matthew Wright
  • Anonymous Communications
  • timing analysis, performance, new defenses
  • Stepping-Stone Detection
  • Interplay between attack and defense
  • Incentives in Security and Privacy
  • Trust in complex, ad-hoc environments
  • Contact http//ranger.uta.edu/mwright

59
Computer Science and Engineering DepartmentThe
University of Texas at Arlington
Assist Laboratory
F. Kamangar, M. Huber, D. Levine, G. Zaruba
60
Information Technologies for Persons with
Disabilities and Health Care
  • Assistance for Persons with Disabilities
  • Communication devices and technologies
  • Intelligent assistive devices
  • IT for improved care
  • Information Technologies for Healthcare and
    Aging
  • Automatic health monitoring
  • Intelligent environments
  • IT to improve uniform communication needs

61
Connect - Intelligent Communication Technologies
for Disability Health Care
  • Intelligent communication services connect
    individuals with care providers and with
    important information
  • Seamlessly connected devices
  • Adaptive interfaces
  • Universal underlying
  • software architecture
  • Intelligent information
  • analysis and interpretation
  • Seamless, omnipresent
  • access to information

62
Assistive Technologies
  • Computer Technologies Can Enhance Assistive
    Devices
  • Ayuda Intelligent wheelchair
  • Autonomous navigation capabilities
  • Environment sensing
  • Integration of computer control and user
    instructions
  • Force feedback technologies to enhance
    interaction capabilities for persons with
    physical disabilities

63
Health Monitoring and Intelligent Environments
for Aging in Place
  • Wirelessly Connected Sensors Provide Health
    Information and can Improve Quality of Life
  • Health sensors can monitor conditions and detect
    problems
  • Wireless communications permit continuous
    monitoring
  • Prediction and modeling technologies facilitate
    automatic analysis of the data
  • Communication technologies allow connectivity to
    physician
  • Sensors in the environment allow automation of
    important functions and assistance
  • Monitoring and assistance for Aging in Place

64
Computer Science and Engineering DepartmentThe
University of Texas at Arlington
AI and Robotics Laboratory
M. Huber, F. Kamangar
65
Adaptation and Learning in Robots and Computer
Systems
  • Personal Service Robots
  • Service robots have to interact with people
  • Programmability by unskilled users
  • Robustness in real world situations
  • Variable Autonomy
  • Robots have to be easy to program
  • Robots should understand any kind of user
    command
  • Cognitive Development
  • Computer systems have to learn how to act and
    reason in the world

66
Robot Imitation Programming by Demonstration
  • Learning to Sense
  • Imitating robots have to be able to interpret
    their observations
  • Learning to Relate Human Demonstrations to
    Robot Actions
  • Learning to extract the important aspects of
    human actions
  • Translating human actions into corresponding
    robot controls
  • Learning to Interpret Task Requirements
  • Robots have to be able to learn to ignore
    dangerous commands

67
Hierarchical Skill Learning / Cognitive
Development
  • Learning Behavioral Strategies
  • Adaptation to unknown conditions
  • Automatic extraction of subtasks
  • Hierarchical Learning
  • Learning with abstract actions
  • Learning using state abstractions
  • Facilitation of incrementally more complex
    behavior

68
Robot Activities and Platforms
  • Robot Soccer (RoboCup)
  • Autonomous robotic soccer with robot dogs
  • Student team
  • Computer Game Trials
  • UCT Urban Combat Testbed

69
HERACLEIA
70
Participating Faculty
Professor Fillia Makedon Bioinformatics Mobile
and Pervasive Computing
Professor Heng Huang Bioinformatics Multimedia
and Video Processing
71
HERACLEIA Security Projects
  • Open Collaboration System
  • How can we enable any entity to join a
    collaboration group in a P2P environment?
  • Entities can log on to system with any names they
    want.
  • Collaborative groups can be created by any entity
    and share files in a P2P fashion.
  • Our OC System supports operations on groups,
    roles and shared files.
  • Certified Authority Project
  • Short-lived certificates(SLC) are used to prevent
    unofficial information propagation which can take
    place due to various causes
  • We use forward secure signatures in SLCs and cut
    off the cost for the revocation.

72
HERACLEIA Sensor Localization Projects
  • Geographical Distributed Localization (GDL)
  • Localization is a fundamental Problem in Wireless
    Sensor Networks.
  • Our GDL protocols support both static and mobile
    node localization
  • We use a novel computational model for
    localization.
  • Wormhole Detection Project
  • How can we protect Wireless Sensor Networks in
    hostile environments?
  • We have developed a distributed wormhole
    detection algorithm based on static GDL to detect
    as well as locate wormholes inside the network.

73
HERACLEIA Location Privacy Projects
  • Location privacy in sensor networks
  • How can we protect the location privacy of the
    sources of messages in a sensor network?
  • We create routing traps to confuse the attackers.
  • Sensor network anonymity project
  • Location sensors can be attached to peoples cell
    phone and medical sensors can be attached to
    human body to monitor vital signs.
  • In such sensor networks, anonymity of
    participants needs to be protected.
  • We use one-way hash chains to refresh IDs of
    sensor nodes.

74
HERACLEIA Negotiation Projects
  • SCENS Project
  • SCENS (Secure Content Exchange Negotiation
    System) is a meta-data based negotiation system
    for agreeing data sharing conditions among
    parties who do not know each other. It can be
    accessed at
  • http//heracleia.uta.edu/scens
  • Sensor Test Bed
  • Using programmable motes as sensors in a wireless
    network.
  • To run experiments in assisted living
  • To test algorithms on a sensor net

75
Research at the Vision-Learning-Mining Lab
  • Vassilis Athitsos
  • University of Texas at Arlington

76
American Sign Language
  • 0.5-2 million users in the US.
  • Complete and independent language.
  • Not a signed version of English.

77
Looking Up a Sign
  • It is easy to go from an English word to ASL.

78
Looking Up a Sign
  • It is easy to go from an English word to ASL.
  • It is hard to look up the meaning of a sign.

79
Looking Up a Sign
  • Our goal automated sign lookup.
  • Input video of a sign.
  • The user performs the sign in front of a camera.
  • Output best matches in a database of 3000 signs.

80
Research Directions
  • Challenging problems in vision, learning,
    database indexing.
  • Large-scale motion-based video retrieval.
  • Need for developing novel atabase indexing
    methods
  • Efficient large-scale multiclass recognition.
  • How can a computer learn to recognize 3000 signs?
  • Learning complex patterns from few examples.

81
Object Detection
82
Object Detection
83
Parsing Satellite Images
  • Research goals
  • Accuracy.
  • Efficiency.

84
IDIR (Innovative Database and Information Systems
Research) group
  • Chengkai Li
  • http//ranger.uta.edu/cli
  • Sept. 5, 2008

85
My Lab
  • IDIR (Innovative Database and Information
    Systems Research)
  • Students
  • 2 PhD students
  • several master students
  • 1 master student recently graduated
  • Collaboration
  • University of Illinois at Urbana-Champaign
  • University of Waterloo
  • IBM Almaden Research Center
  • Yahoo! Research
  • Gautam Das, Sharma Charkravarthy

86
  • the Database
  • and
  • the Web

Query
Search
Two Sides to the Story
87
Search the Database and Query the Web
  • Our Goal
  • Search the Database (flexibly) use databases
    like the Web
  • Query the Web (expressively) use the Web like a
    database
  • We study the following aspects
  • Core of Data Management Systems
  • storage engine and indexing methods
  • query processing algorithms and query
    optimization
  • Applications
  • new interfaces and queries for retrieving and
    exploring data
  • data management in new domains semantic Web,
    social networks, medical information

88
Projects
  • RankSQL Ranking and Top-k Queries in Databases
  • Set Query A New Type of Queries in Data
    Warehousing
  • WebEQ Querying, Retrieval and Exploration of
    Structured Information on the Web
  • DataMasher Information Aggregation,
    Summarization, and Analysis on the Web
  • RDFM RDF Data Management Systems
  • RECOMM Large-Scale Recommendation Engine for
    Social Networks

89
1. RankSQLExample What Boolean queries provide
  • SELECT
  • FROM Houses H
  • WHERE 200Kltpricelt400K AND bedroom 4

89
90
1. RankSQLExample What may be desirable
  • lt 500K is more acceptable
  • but willing to pay more for big house
  • close to the lake is a plus
  • avoid locations near airport

90
91
1. RankSQLRetrieval of Databases by Ranking and
Other Mechanisms
Ranking
Clustering
Navigation Map
Facets
Categorization

91
92
2. Set QueriesA New Type of Queries in Data
Warehousing
  • Example
  • SELECT dept
  • FROM Students
  • GROUP BY dept
  • HAVING set(city) CONTAINS Dallas,
    Arlington, Fort Worth

93
3. WebEQQuerying, Retrieval and Exploration of
Structured Information on the Web
  • The Web is a huge database of structured
    information
  • Enable expressive and efficient access to the Web
    of data, by providing querying, retrieval, and
    exploration facilities.
  • Start with Wikipedia, then extend to general Web

94
Topic 1 Faceted Wikipedia
95
Topic 1 Faceted Wikipedia
Texas university
  • Population
  • Undergraduate
  • lt10000
  • 10000-20000
  • 20000
  • Graduate
  • People
  • Alumni
  • Astronaut
  • Actress
  • Faculty
  • Fraternities and Sororities
  • Alpha Epsilon Pi

96
Topic 2 Shallow Semantic Queries
  • Which Silicon Valley based companies were funded
    by Stanford alumni?

97
Topic 2 Shallow Semantic Queries
  • SELECT P.name, C.name
  • FROM People P, Company C
  • Where co-occur(P, "graduated", "Stanford")
  • AND co-occur(C, "based", "Silicon
    Valley")
  • AND co-occur(P, "funded", C)

98
4. DataMasherInformation Aggregation,
Summarization, and Analysis on the Web
user (graduate school applicants)
The Mashed-up Web
DataMasher
  • mash-up browser
  • summarization of research publications
  • researchers in same area
  • professors taught the same courses
  • schools with similar programs
  • head-to-head comparisons

The Web
99
5. RDFM RDF Data Management Systems
  • RDF de facto data model for semantic Web.
  • RDBMS is not appropriate for managing RDF data.
  • B-store for RDF (bitmap-index based)
  • Optimization of RDF queries
  • Ranking in RDF queries

100
6. RECOMM Large-Scale Recommendation Engine for
Social Networks
  • A lot of Websites provide objects
  • Youtube (videos), Flickr (photos), blogger
    (blogs)
  • Current recommendation systems
  • Not tailored for individual interest
  • Collaborative filtering algorithms
  • Not scalable
  • Imagine a better recommendation engine
  • personalized
  • continuous

101
  • Looking for students
  • http//ranger.uta.edu/cli
  • cli_at_uta.edu
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