Egemen Tanin

Using a distributed quadtree index in peer-to-peer networks

Peer-to-peer (P2P) networks have become a powerful means for online data exchange. Currently, users are primarily utilizing these networks to perform exact-match queries and retrieve complete files. However, future more data intensive applications, such as P2P auction networks, P2P job-search networks, P2P multiplayer games, will require the capability to respond to more complex queries such as range queries involving numerous data types including those that have a spatial component. In this paper, a distributed quadtree index that adapts the MX-CIF quadtree is described that enables more powerful accesses to data in P2P networks. This index has been implemented for various prototype P2P applications and results of experiments are presented. Our index is easy to use, scalable, and exhibits good load-balancing properties. Similar indices can be constructed for various multidimensional data types with both spatial and non-spatial components.

The V*-Diagram: a query-dependent approach to moving KNN queries

The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*-Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*-Diagram outperforms the best existing technique by two orders of magnitude.

Use of the SAND spatial browser for digital government applications

Numerous federal agencies produce official statistics made accessible to ordinary citizens for searching and data retrieval. This is frequently done via the Internet through a Web browser interface. If this data is presented in textual format, it can often be searched and retrieved by such attributes as topic, responsible agency, keywords, or press release. However, if the data is of spatial nature, for example, in the form of a map, then using text-based queries is often too cumbersome for the intended audience. We describe the use of the SAND Spatial Browser to provide more power to users of these databases by enabling them to define and explore the specific spatial region of interest graphically. The SAND Spatial Browser allows users to form either purely spatial or mixed spatial/nonspatial queries intuitively, which can present information to users that might have been missed if only a textual interface was available.

A distributed quadtree index for peer-to-peer settings

We describe a distributed quadtree index for enabling more powerful access on complex data over P2P networks. It is based on the Chord method. Methods such as Chord have been gaining usage in P2P settings to facilitate exact-match queries. The Chord method maps both the data keys and peer addresses. Our work can be applied to higher dimensions, to various data types, i.e., other than spatial data, and to different types of quadtrees. Finally, we can use other key-based methods than the Chord method as our base P2P routing protocol and index scale well. The index also benefits from the underlying fault-tolerant hashing-based methods by achieving a nice load distribution among many peers. We can seamlessly execute a single query on multiple branches of the index hosted by a dynamic set of peers.

Gesture recognition using RFID technology

We propose a gesture recognition technique based on RFID: cheap and unintrusive passive RFID tags can be easily attached to or interweaved into user clothes, which are then read by RFID antennas. These readings can be used to recognize hand gestures, which enable interaction with applications in an RFID-enabled environment. For instance, it allows people to interact with large displays in public collaboration spaces without the need to carry a dedicated device. We propose the use of multiple hypothesis tracking and the use of subtag count information to track the motion patterns of passive RFID tags. To the best of our knowledge, this work is the first on motion pattern tracking using passive RFID tags. Despite the reading uncertainties inherent in passive RFID technology, our experiments show that the proposed gesture recognition technique has an accuracy of up to 93%.

The end of zero-hit queries: query previews for NASA's Global Change Master Directory

The Human-Computer Interaction Laboratory (HCIL) of the University of Maryland and NASA have collaborated over three years to refine and apply user interface research concepts developed at HCIL in order to improve the usability of NASA data services. The research focused on dynamic query user interfaces, visualization, and overview + preview designs. An operational prototype, using query previews, was implemented with NASA’s Global Change Master Directory (GCMD), a directory service for earth science datasets. Users can see the histogram of the data distribution over several attributes and choose among attribute values. A result bar shows the cardinality of the result set, thereby preventing users from submitting queries that would have zero hits. Our experience confirmed the importance of metadata accuracy and completeness. The query preview interfaces make visible the problems or gaps in the metadata that are undetectable with classic form fill-in interfaces. This could be seen as a problem, but we think that it will have a long-term beneficial effect on the quality of the metadata as data providers will be compelled to produce more complete and accurate metadata. The adaptation of the research prototype to the NASA data required revised data structures and algorithms.

The Geometry of Browsing

We present a geometric counting problem that arises in browsing and solve it in constant time per query using nonexhaustive tables. On the other hand, we prove that several closely related problems require exhaustive tables, no matter how much time we allow per query.

Spatial interpolation in wireless sensor networks: localized algorithms for variogram modeling and Kriging

Wireless sensor networks (WSNs) are rapidly emerging as the prominent technology for monitoring physical phenomena. However, large scale WSNs are known to suffer from coverage holes, i.e., large regions of deployment area where no sensing coverage can be provided. Such holes are the result of hardware failures, extensive costs for redeployment or the hostility of deployment areas. Coverage holes can adversely affect the accurate representation of natural phenomena that are monitored by a WSN. In this work, we propose to exploit the spatial correlation of physical phenomena to make monitoring systems more resilient to coverage holes. We show that a phenomenon can be interpolated inside a coverage hole with a high level of accuracy from the available nodal data given a model of its spatial correlation. However, due to energy limitations of sensor nodes it is imperative to perform this interpolation in an energy efficient manner that minimizes communication among nodes. In this paper, we present highly energy efficient methods for spatial interpolation in WSNs. First, we build a correlation model of the phenomenon being monitored in a distributed manner. Then, a purely localized and distributed spatial interpolation scheme based on Kriging interpolates the phenomenon inside coverage holes. We test the cost and accuracy of our scheme with extensive simulations and show that it is significantly more energy efficient than global interpolations and remarkably more accurate than simple averaging.

Privacy-Aware Traffic Monitoring

Traffic-monitoring systems (TMSs) are vital for safety and traffic optimization. However, these systems may compromise the privacy of drivers once they track the position of each driver with a high degree of temporal precision. In this paper, we argue that aggregated data can protect location privacy while providing accurate information for traffic monitoring. We identify a range of aggregate query types. Our proposed privacy-aware monitoring system (PAMS) works as an aggregate query processor that protects the location privacy of drivers as it anonymizes the IDs of cars. Our experiments show that PAMS answers queries with high accuracy and efficiency.

visible nearest neighbor queries

We introduce the visible k nearest neighbor (VkNN) query, which finds the k nearest objects that are visible to a query point. We also propose an algorithm to efficiently process the VkNN query. We compute the visible neighbors incrementally as we enlarge the search space. Our algorithm dramatically reduces the search cost compared to existing methods that require the computation of the visibility of all objects in advance. With extensive experiments, we show that our algorithm to process the VkNN query outperform the existing algorithms significantly.