Database and Data-Intensive Systems

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Database andData-Intensive Systems
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Data-Intensive Systems

• From monolithic architectures to diverse systems
• Dedicated/specialized systems, column stores
• Data centers, web architectures, distributed
  architectures
• From business data to all data
• Streaming and sensor data, semi-structured and
  unstructured data
• Multidimensional data, temporal data,
  spatio-temporal data
• Examples
• Clustering of high-dimensional data
• Tracking and continuous queries for moving
  objects
• Mobile service infrastructure
• Location privacy
• Spatio-textural search/hyper-local web search
• Multimedia similarity search
• This is where much of our research lives.

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Staff

• Ira Assent, associate professor
• Christian S. Jensen, professor
• Vaida Ceikute, Ph.D. student
• Xiaohui Li, visiting Ph.D. student
• NN, Ph.D. student
• GEOCROWD indoor positioning and services
  infrastructure
• NN, Ph.D. student
• GEOCROWD spatial web objects
• NN, Ph.D. student
• eData Anomaly Detection in e-Science
• NN, Ph.D. student
• Streamspin
• NN, Ph.D. student
• WallViz
• NN, Ph.D. student
• REDUCTION
• NN, Ph.D. student
• REDUCTION

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Graduate Course Portfolio dDO

• Data management for moving objects (Q3)
• The course covers selected research advances in
  the general area of indexing and update and query
  processing for moving objects.
• Moving object tracking
• Specific indexing techniques
• R-tree based indexing
• B-tree based indexing
• Techniques for the efficient handling of frequent
  updates
• Techniques for range and k nearest neighbor query
  processing, including one-time as well as
  continuous queries

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Graduate Course Portfolio MDDB

• Multidimensional databases (Q4)
• Selected techniques for the management of
  multidimensionally represented data
• Multidimensional data and applications
• Data warehouses and data mining
• Similarity search and query processing
• Efficient handling indexing and associated query
  processing
• Multistep similarity search
• Indexing multidimensional data
• Skyline query processing
• Data mining techniques
• Subspace clustering
• Classification
• Outlier detection

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Graduate Course Portfolio Index

• Indexing of disk-based data (Q1)
• Indexing techniques for disk-based data for
  different types of data, as well as their support
  for queries and updates
• General overview over indexes and query
  processing
• Spatial indexing structures
• Space partitioning indexing structures
• Indexes for high dimensional data
• Metric approaches
• Special techniques for complex data types
• Coming up for the first time this fall

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Graduate Course Portfolio dDB2

• Database management systems (Q2)
• The course aims to give the participants a solid
  conceptual foundation for making competent use of
  a database management system.
• Logical and physical query optimization and query
  processing
• Concurrency control techniques
• Database tuning
• Central concepts and techniques in relation to
  supporting temporal and multi-dimensional data
• Coming up for the first time this fall

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Projects

• Streamspin
• Enable sites that are for mobile services what
  YouTube is for video
• Easy mobile service creation and sharing
• Advanced spatial and social context functionality
• Be an open, extensible, and scalable service
  delivery infrastructure
• MOVE
• Knowledge extraction from massive data about
  moving objects
• Cross-cutting activities, showcases, and
  evaluation
• Representation of movement data and
  spatio-temporal databases
• Analysis of movement and spatio-temporal data
  mining
• WallViz
• Collaborative analysis, joint decision making on
  wall-sized displays
• scale to massive data collections
• support ad-hoc queries
• automatically provide entry points for analysis

http//www.move-cost.info
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Projects (2)

• GEOCROWD
• Creating a Geospatial Knowledge World
• advance the state-of-the-art in collecting,
  storing, analyzing, processing, reconciling, and
  publishing user-generated geospatial information
  on the Web
• REDUCTION
• Reducing the environmental footprint of fleets of
  vehicles
• Optimizing the behavior of drivers
• Supporting eco-routing of vehicles
• Enabling transparency in multi-modal
  transportation
• eData
• Robust analysis in the context of imperfect data
  in e-Science
• Detect and correct anomalies effectively
• on-line, interactive, lineage-preserving, and
  semi-automatic
• Scalable algorithms

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How We Typically Work

• We target some real problem that we find
  interesting.
• We define the problem precisely.
• We develop a solution that is typically a data
  structure or an algorithm, i.e., a concrete
  technique.
• To evaluate, we build prototypes.
• These are built for the purpose of studying the
  properties of our solutions.
• We are often interested in performance, e.g.,
  runtime, space usage, communication cost.
• For some solutions we state formal properties
  that we then prove, e.g., the correctness of a
  particular technique
• Brief isolate and define problem, construct,
  then evaluate

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Example 1 Spatial Web Querying

• Setting
• Google 90 billion queries/month, 20 billion
  with local intent.
• We want to integrate exact locations of websites
  (for shops, bars, etc.) and users into web
  querying.
• Queries
• Results must match the query text and must be
  near the user.
• Results of continuous queries must be updated as
  the user moves.
• Challenges?
• Support such queries with low computation cost on
  the server and
• with little communication between server and
  client.
• Solution
• Invent an index that supports both text and
  location
• Use a safe zone to reduce the communication
  between user and server for continuous queries

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Example 2 Fraud detection

• There are billions of financial transactions per
  minute
• How do we uncover fraud?
• Scalability
• In-time for reaction
• Manageable results
• Possible solution sketch
• Identify attributes of suspicious transactions
• Sort incoming transactions into a tree-structure
  of historic data
• When processing time is up, output degree of
  suspicion based on similarity to valid or
  fraudulent historic data

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Interested?

• Come talk to us!
• We currently have M.Sc. and PhD. thesis openings