Short Presentation of DMOD 2 Universit y of Ioannina School of - - PowerPoint PPT Presentation

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Short Presentation of DMOD 2 Universit y of Ioannina School of - - PowerPoint PPT Presentation

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1 Short Presentation of DMOD 2 Universit y of Ioannina School of Philosophy Department of Philology Department of History and Archaeology Department of Philosophy, Education and Established in 1964 Psychology Independent University since

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Short Presentation of DMOD

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December 11@ INTERSOCIAL kickoff

DMOD Laboratory, University of Ioannina

Universit y of Ioannina

2 Established in 1964 Independent University since 1970

School of Philosophy Department of Philology Department of History and Archaeology Department of Philosophy, Education and Psychology School of Sciences Department of Mathematics Department of Physics Department of Chemistry Department of Computer Science School of Education Department of Primary School Education Department of Pre-School Education School of Medicine School of Sciences and Technologies Department of Materials Science and Engineering Department of Biological Applications and Techologies Independent Departm ents Department of Economics Department of Plastic Arts and Art Sciences

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DMOD Laboratory, University of Ioannina

Depart ment of Comput er S cience

3 Established in 1993

23 Faculty members Research in  Data management  Algorithms  Graphics  Machine learning  Image processing and analysis  Software engineering  Systems

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DMOD Laboratory, University of Ioannina

DMOD Lab

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Distributed Management of Data Laboratory DMOD perform s research on various topics in distributed m anagem ent, processing and visualization of data.

  • Data management with emphasis on mobile, ubiquitous, distributed
  • verlays, cloud computing, social netowrks
  • Computer Graphics, CAD and Data Visualization
  • Parallel Processing
  • Information Systems Architecting, with emphasis on data

warehousing, conceptual modeling, database evolution and quality

  • Middleware
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DMOD Laboratory, University of Ioannina

Our Group

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Research

  • In distributed overlays (p2p systems and publish subscribe) –

recent interest in social netw orks and cloud

  • On the integration of information retrieval and data management
  • Preferences,
  • Recommendations, and
  • Diversified search
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Pref erences f or S haring Dist ribut ed Cont ent

Why Preferences?

Through preferences: Two fundamental philosophies for expressing preferences:

▫ Quantitative approach

 Using scoring functions that assign a numeric score (interest) to an item:

▫ Qualitative approach

 Binary relations between pairs of items:

Huge amount of information available to diverse population => need to personalize

How?

genre = drama

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genre = horror

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DMOD Laboratory, University of Ioannina

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Pref erences f or S haring Dist ribut ed Cont ent

Context-Aware Preferences in a nutshell

Preference with two parts (context-descriptor, preference)

While in Greece with sunny weather, I like to walk outside In intrerreg proposals, regional SMEs In Ioannina (Greece), alevropita In Lyon (France), quannelle

context

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DMOD Laboratory, University of Ioannina

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Pref erences f or S haring Dist ribut ed Cont ent

Context-Aware Preferences in a nutshell

  • Context Attributes
  • Hierarchical Domains
  • Context Resolution:

Find the preferences applicable to the current context through special data structures the Profile Tree and the Context Graph

  • Efficient top-k com putation through pre-com putation of

representatives rankings (representatives defined through clustering preferences) 8

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DMOD Laboratory, University of Ioannina

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Pref erences f or S haring Dist ribut ed Cont ent

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Used preferences in

  • Keyword search in relational databases
  • Publish/ subscribe systems
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Publish/ Subcribe Systems

Publish/ Subscribe offers an attractive alternative to typical searching Users do not need to repeatedly search for new interesting data They specify their interests once through subscriptions and get notified by the system whenever data that match their subscriptions are published

Pref erences f or S haring Dist ribut ed Cont ent

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Event-notification service Notify Notify Publish Notify

Examples:

▫ Google Alerts ▫ Twitter ▫ Microsoft BizTalk Server

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Typically, all subscriptions are considered equally important But, users may receive:

  • overwhelming amounts of notifications
  • too much overlapping information
  • In such cases, users would like to receive only a

fraction of notifications, the most interesting ones:

  • Current publish/ subscribe systems do not allow users express different degrees
  • f interest

Pref erences f or S haring Dist ribut ed Cont ent

Why Preferential Pub/ Sub?

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DMOD Laboratory, University of Ioannina

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Extend subscription with preferences: users define priorities or degrees of interest on their subscriptions

preferential subscriptions

Use preferential subscriptions, we deliver to users only the k most interesting (highly ranked) events

Pref erences f or S haring Dist ribut ed Cont ent

Preferential Pub/ Sub

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PrefSIENA: We have extended SIENA to include preferential subscriptions and delivery based on ranking and diversity for the three delivery modes ▫ http:/ / www.cs.uoi.gr/ ~mdrosou/ PrefSIENA

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DMOD Laboratory, University of Ioannina

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Diversity

We wish to retrieve results on a broader variety of user interests Two different perspectives on achieving diversity:

▫ Avoid overlap: choose events that are dissimilar to each other ▫ Increase coverage: choose notifications that cover as many user interests as possible

How to measure diversity?

▫ Many alternative ways ▫ Common ground: measure similarity/ distance among the selected items

Pref erences f or S haring Dist ribut ed Cont ent

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IR and DB

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DMOD Laboratory, University of Ioannina

We propose assisting users in database exploration by recom m ending to them additional items that are highly related with the items in the result of their original query. Our recommendations are based solely on the result of the user query.

Our model:

The computation of recommended results is based on the most interesting sets of (attribute, value) pairs, that appear in the result of the original user

  • query. The interestingness of a faSet expresses how unexpected it is to see

this faSet in the result.

R e D r i v e : R e s u l t - D r i v e n D a t a b a s e R e c o m m e n d a t i o n s

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IR and DB

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  • Context-aware preference models [ACM TODS11,

Inform ationSystem s11, EDBT0 8 , ICDE0 7 and others]

  • extend preference models with a context component
  • Preferential Publish/ Subscribe [DEBS0 9]
  • Introduce ranking (top-k delivery) in pub/ sub
  • Preferences and Keyword Search in Relational Databases

[EDBT10 ]

  • You May Also Like Results (YMAL) in Databases [CIKM11,

PersDB0 9]

  • Result Diversification [Sigm odRecord10 , IEEEDBulletin0 9 and

subm ission]

December 11@ INTERSOCIAL kickoff

DMOD Laboratory, University of Ioannina

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Overlays f or S haring Dist ribut ed Cont ent

Dynamic Content Sharing Systems: P2P, social networks Peers:

  • Offer/ Store content
  • Request/ Query for content

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Overlays f or S haring Dist ribut ed Cont ent

Peers connect with a subset of other peers Overlay networks built on top of the physical networks

Overlay Networks

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Overlays f or S haring Dist ribut ed Cont ent

Our Work on Overlays

  • Distributed XPath over Distributed XML Collections [ICDE0 8 ,

SIGMOD0 8 , ICDE0 9, WWW10 ]

  • Cooperative caching for XML: use a DHT-based cache for indexing or

sharing cache [SIGMOD08]

  • Clustering for relaxation [ICDE08, ICDE09]
  • Clustering overlays as a game [VLDB0 9 ]
  • Duplicate elimination [CIKM11]

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DMOD Laboratory, University of Ioannina

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Comput er Graphics Research Group

Dat a Visualizat ion, Graphics, Augment ed Realit y

  • Multidimensional data visualization and rendering [Siggraph11

poster]

  • Animation [SCA11, CAVW]
  • Reverse Engineering [CAD]
  • 3D browsing and rendering and interacting using commodity and

new generation smartphone hardware.

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Thank you!

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