Susanne E. Hambrusch

Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects

Moving object environments are characterized by large numbers of moving objects and numerous concurrent continuous queries over these objects. Efficient evaluation of these queries in response to the movement of the objects is critical for supporting acceptable response times. In such environments, the traditional approach of building an index on the objects (data) suffers from the need for frequent updates and thereby results in poor performance. In fact, a brute force, no-index strategy yields better performance in many cases. Neither the traditional approach nor the brute force strategy achieve reasonable query processing times. This paper develops novel techniques for the efficient and scalable evaluation of multiple continuous queries on moving objects. Our solution leverages two complimentary techniques: Query Indexing and Velocity Constrained Indexing (VCI). Query Indexing relies on 1) incremental evaluation, 2) reversing the role of queries and data, and 3) exploiting the relative locations of objects and queries. VCI takes advantage of the maximum possible speed of objects in order to delay the expensive operation of updating an index to reflect the movement of objects. In contrast to an earlier technique that requires exact knowledge about the movement of the objects, VCI does not rely on such information. While Query Indexing outperforms VCI, it does not efficiently handle the arrival of new queries. Velocity constrained indexing, on the other hand, is unaffected by changes in queries. We demonstrate that a combination of Query Indexing and Velocity Constrained Indexing enables the scalable execution of insertion and deletion of queries in addition to processing ongoing queries. We also develop several optimizations and present a detailed experimental evaluation of our techniques. The experimental results show that the proposed schemes outperform the traditional approaches by almost two orders of magnitude.

On peer-to-peer media streaming

In this paper, we study a peer-to-peer media streaming system with the following characteristics: (1) its streaming capacity grows dynamically; (2) peers do not exhibit server-like behavior; (3) peers are heterogeneous in their bandwidth contribution; and (4) each streaming session may involve multiple supplying peers. Based on these characteristics, we investigate two problems: (1) how to assign media data to multiple supplying peers in one streaming session and (2) how to quickly amplify the systems total streaming capacity. Our solution to the first problem is an optimal media data assignment algorithm OTS/sub p2p/, which results in minimum buffering delay in the consequent streaming session. Our solution to the second problem is a distributed differentiated admission control protocol DAC/sub p2p/. By differentiating between requesting peers with different outbound bandwidth, DAC/sub p2p/ achieves fast system capacity amplification; benefits all requesting peers in admission rate, waiting time, and buffering delay; and creates an incentive for peers to offer their truly available out-bound bandwidth.

Indexing Uncertain Categorical Data

Uncertainty in categorical data is commonplace in many applications, including data cleaning, database integration, and biological annotation. In such domains, the correct value of an attribute is often unknown, but may be selected from a reasonable number of alternatives. Current database management systems do not provide a convenient means for representing or manipulating this type of uncertainty. In this paper we extend traditional systems to explicitly handle uncertainty in data values. We propose two index structures for efficiently searching uncertain categorical data, one based on the R-tree and another based on an inverted index structure. Using these structures, we provide a detailed description of the probabilistic equality queries they support. Experimental results using real and synthetic datasets demonstrate how these index structures can effectively improve the performance of queries through the use of internal probabilistic information.

Database Support for Probabilistic Attributes and Tuples

The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous and discrete data domains. This paper presents a model for handling arbitrary probabilistic uncertain data (both discrete and continuous) natively at the database level. Our approach leads to a natural and efficient representation for probabilistic data. We develop a model that is consistent with possible worlds semantics and closed under basic relational operators. This is the first model that accurately and efficiently handles both continuous and discrete uncertainty. The model is implemented in a real database system (PostgreSQL) and the effectiveness and efficiency of our approach is validated experimentally.

Orion 2.0: native support for uncertain data

Orion is a state-of-the-art uncertain database management system with built-in support for probabilistic data as first class data types. In contrast to other uncertain databases, Orion supports both attribute and tuple uncertainty with arbitrary correlations. This enables the database engine to handle both discrete and continuous pdfs in a natural and accurate manner. The underlying model is closed under the basic relational operators and is consistent with Possible Worlds Semantics. We demonstrate how Orion simplifies the design and enhances the capabilities of two example applications: managing sensor data (continuous uncertainty) and inferring missing values (discrete uncertainty).

Main Memory Evaluation of Monitoring Queries Over Moving Objects

In this paper we evaluate several in-memory algorithms for efficient and scalable processing of continuous range queries over collections of moving objects. Constant updates to the index are avoided by query indexing. No constraints are imposed on the speed or path of moving objects or fraction of objects that move at any moment in time. We present a detailed analysis of a grid approach which shows the best results for both skewed and uniform data. A sorting based optimization is developed for significantly improving the cache hit-rate. Experimental evaluation establishes that indexing queries using the grid index yields orders of magnitude better performance than other index structures such as R*-trees.

Parallelizing the Data Cube

This paper presents a general methodology for the efficient parallelization of existing data cube construction algorithms. We describe two different partitioning strategies, one for top-down and one for bottom-up cube algorithms. Both partitioning strategies assign subcubes to individual processors in such a way that the loads assigned to the processors are balanced. Our methods reduce inter processor communication overhead by partitioning the load in advance instead of computing each individual group-by in parallel. Our partitioning strategies create a small number of coarse tasks. This allows for sharing of prefixes and sort orders between different group-by computations. Our methods enable code reuse by permitting the use of existing sequential (external memory) data cube algorithms for the subcube computations on each processor. This supports the transfer of optimized sequential data cube code to a parallel setting.The bottom-up partitioning strategy balances the number of single attribute external memory sorts made by each processor. The top-down strategy partitions a weighted tree in which weights reflect algorithm specific cost measures like estimated group-by sizes. Both partitioning approaches can be implemented on any shared disk type parallel machine composed of p processors connected via an interconnection fabric and with access to a shared parallel disk array.We have implemented our parallel top-down data cube construction method in C++ with the MPI message passing library for communication and the LEDA library for the required graph algorithms. We tested our code on an eight processor cluster, using a variety of different data sets with a range of sizes, dimensions, density, and skew. Comparison tests were performed on a SunFire 6800. The tests show that our partitioning strategies generate a close to optimal load balance between processors. The actual run times observed show an optimal speedup of p.

Query Processing in Broadcasted Spatial Index Trees

The broadcasting of spatial data together with an index structure is an effective way of disseminating data in a wireless mobile environment. Mobile clients requesting data tune into a continuous broadcast only when spatial data of interest and relevance is available on the channel and thus minimize their power consumption. A mobile client experiences latency (time elapsed from requesting to receiving data) and tuning time (the amount of time spent listening to the channel). This paper studies the execution of spatial queries on broadcasted tree-based spatial index structures. The focus is on queries that require a partial traversal of the spatial index, not only a single-path root-to-leaf search. We present techniques for processing spatial queries while mobile clients are listening to a broadcast of the tree. Our algorithms can handle clients with limited memory, trees broadcast with a certain degree of replication of index nodes, and algorithms executed at the clients may employ different data structures. Experimental work on R*-trees shows that these techniques lead to different tuning times and different latencies. Our solutions also lead to efficient methods for starting the execution of a query in the middle of a broadcast cycle. Spatial query processing in a multiple channel environment is also addressed.

Computational Thinking in Elementary and Secondary Teacher Education

Computational thinking (CT) is broadly defined as the mental activity for abstracting problems and formulating solutions that can be automated. In an increasingly information-based society, CT is becoming an essential skill for everyone. To ensure that students develop this ability at the K-12 level, it is important to provide teachers with an adequate knowledge about CT and how to incorporate it into their teaching. This article describes a study on designing and introducing computational thinking modules and assessing their impact on preservice teachers’ understanding of CT concepts, as well as their attitude towards computing. Results demonstrate that introducing computational thinking into education courses can effectively influence preservice teachers’ understanding of CT concepts.

Parallel algorithms for line detection on a mesh

ShuffleNet and de Bruijn networks have been proposed as multihop lightwave networks based on wavelength division multiplexing (WDM). With multihop lightwave networks, few fixed wavelength transmitters/receivers are assigned to each user, eliminating the need for wavelength agility and pretransmission coordination. These networks have been shown to be very effective for uniform traffic. For communications with high locality, we propose two-level hierarchical networks. At the first level, each cluster of users can be connected either via a ShuffleNet (SH) or a de Bruijn network (dB). At the second level, all the clusters in the system can be connected by two rings in opposite directions (SH/Ring and dB/Ring), a de Bruijn network (dB/dB), or a ShuffleNet (SH/SH). The performance of ShuffleNet, de Bruijn networks, and the hierarchical networks SH/Ring, dB/Ring, dB/dB, and SH/SH is analyzed. For communications with a high locality, the hierarchical networks are shown to be very effective.